chapter 2 literature survey - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/8869/7/07_chapter...
TRANSCRIPT
CHAPTER 2
LITERATURE SURVEY
2.1. Introduction
Smart environments represent the next evolutionary development step in building,
utilities, industrial, home, shipboard, and transportation systems automation. Like any
conscious human being, the smart environment relies first and foremost on sensory
data from the real world. Sensory data comes from multiple sensors of different
modalities in distributed locations. The smart environment needs information about its
surroundings as well as about its internal workings; this is captured in biological
systems by the distinction between exteroceptors and proprioceptors.
With respect to the performance of wireless sensor networks, the data transmission
capacity and the lifetime of the sensor networks are critical and influential towards the
design of optimal deployment strategies of these sensor networks. The fundamental
limits of these two critical performance parameters lead to a few interesting open
problems. First, what is the maximum sustainable throughput of the network? Second,
what is the maximum lifetime of the network? These questions are usually considered
given a set of parameters of the sensor network, and under the assumption that
optimal network management is achievable. The set of parameters of the sensor
network under consideration includes the number of sensor nodes in the network, as
well as the area occupied by the sensor network. Issues relevant to network
management usually include packet routing, energy management, and congestion
control, which directly affect to quality of service. One of the earliest routing
protocols with Qualty of Service (QoS) impression is Sequential Assignment Routing
(SAR) [4]. SAR creates trees originating from one-hop neighborhood of sink and
takes into account two types of QoS metrics; energy resource and priority level of
each packet Multiple paths are created from a sink to source and a path may be
selected based on the QoS metrics. However, SAR is believed to suffer from overhead
for maintaining the node state. Zhi Ang Eu et al. [14] study the performance of
different medium access control (MAC) schemes based on CSMA and polling
techniques for WSNs which are solely powered by ambient energy harvesting using
energy harvesters. the study is based on (i) network throughput (S), which is the rate
Chapter 2. Literature Survey
21
of sensor data received by the sink, (ii) fairness index (F), which determines whether
the bandwidth is allocated to each sensor node equally and (iii) inter-arrival time (γ)
which measures the average time difference between two packets from a source node.
For CSMA, they compare both the slotted and unslotted variants. For polling, they
first consider identity polling. Then design a probabilistic polling protocol that takes
into account the unpredictability of the energy harvesting process to achieve good
performance. Finally, they present an optimal polling MAC protocol to determine the
theoretical maximum performance. They validate the analytical models using
extensive simulations incorporating experimental results from the characterization of
different types of energy harvesters. The performance results show that probabilistic
polling achieves high throughput and fairness as well as low inter-arrival times. Ren-
Shiou Liu et al. in [19] proposed a low-overhead MAC layer solution to address the
high contention problem to improve system throughput and reduce energy
consumption. Periods of burst transmissions with reduced contention from
neighboring nodes are exploited to efficiently clear up backlogged queues and
improve the performance of CSMA. Through analytical modeling they characterize
the expected performance improvement. Using extensive simulations on ns-2 and
experiments on the 49-node sensor network test bed (Kansei) running TinyOS it is
seen that the proposed scheme can increase the throughput by up to a factor of four.
Zhi Ang Eu et al. [53] designed a probabilistic polling protocol that takes into account
the unpredictability of the energy harvesting process to achieve good performance.
They presented an optimal polling MAC protocol to determine the theoretical
maximum performance. Validation occurred with the analytical models using
extensive simulations incorporating experimental results from the characterization of
different types of energy harvesters. The performance results showed that
probabilistic polling achieves high throughput and fairness as well as low inter-arrival
times.
2.2. Quality of Service in WSNs
Although the subject of WSNs is an active research field for the past few years, QoS
has largely been an unexplored area. QoS in a network is largely associated with
network data rate, energy efficiency in the network, and with network lifetime.
However, with the growing applications of WSNs, quality of service is now deemed
necessary to be implemented. At the network routing level, a few protocols have been
Chapter 2. Literature Survey
22
proposed so far. Much of the other QoS related work has focused on designing an
optimal number of sensor nodes from which a sink would ask for data information.
The approach is based on Gur Game theory, taking into consideration delays &
addition and removal of sensor nodes. This approach has been studied in [7] [8]. M.
Aykut Yigitel et al. [13] focuses on the QoS support at the MAC layer which forms
the basis of communication stack and has the ability to tune key QoS-specific
parameters, such as duty cycle of the sensor devices. They explore QoS challenges
and perspectives for wireless sensor networks, survey the QoS mechanisms and
classify the state of the art QoS-aware MAC protocols together with discussing their
advantages and disadvantages. According to this survey, they observe that instead of
providing deterministic QoS guarantees, majority of the protocols follow a service
differentiation approach by classifying the data packets according to their type (or
classes) and packets from different classes are treated according to their requirements
by tuning the associated network parameters at the MAC layer. Hyun Jung Choea
etal. [28] presented an efficient data reporting control scheme in a cluster-based
hierarchical wireless sensor network, which has two components (i) intra-cluster data
reporting control (IntraDRC) scheme and (ii) inter-cluster control (InterDRC) scheme.
The IntraDRC scheme controls the amount of traffic generated in a cluster by
selecting a certain number of data reporting nodes based on the desired throughput
specified by the end system. On the other hand, the InterDRC scheme offers
differentiated reporting paths from a cluster to a sink based on the traffic
characteristics. InterDRC considers two parameters: one is the hop counts to a sink to
deal with the end-to-end delay constraint while the other is the amount of traffic,
generated in a cluster and forwarded from its adjacent clusters, to deal with energy
consumption. The proposed scheme applies the block design concept from
combinatorial theory to design a novel data reporting node selection approach.
IntraDRC employs the node sets created by block designs as the initial reporting
schedule. This schedule can be updated by the request of a reporting node when its
queue size approaches a predefined threshold. They considered two network models
in their work, the first model considers homogeneous networks in which every node
has the same capabilities and adjacent cluster heads are connected in a multi-hop
manner. The second model considers heterogeneous networks in which the cluster
heads have high capabilities in terms of processing power and transmission range to
directly reach adjacent cluster heads in a single-hop manner. Simulation results show
Chapter 2. Literature Survey
23
that this scheme achieves good throughput performance while providing stable data
reporting that is independent of the network density. The scheme also allows for
energy savings by using load-balanced data reporting paths. Abinash Mahapatra et al.
[30] proposed an energy aware dual-path routing scheme for real-time traffic, which
balances node energy utilization to increase the network lifetime, takes network
congestion into account to reduce the routing delay across the network and increases
the reliability of the packets reaching the destination by introducing minimal data
redundancy. This paper also introduces an adaptive prioritized Medium Access Layer
(MAC) to provide a differentiated service model for real-time packets. Our claims are
well supported by simulation results. Jalel Ben-Othman et al. [31] proposed an
Energy Efficient and QoS aware multipath routing protocol (abbreviated shortly as
EQSR) that maximizes the network lifetime through balancing energy consumption
across multiple nodes, uses the concept of service differentiation to allow delay
sensitive traffic to reach the sink node within an acceptable delay, reduces the end to
end delay through spreading out the traffic across multiple paths, and increases the
throughput through introducing data redundancy. EQSR uses the residual energy,
node available buffer size, and Signal-to-Noise Ratio (SNR) to predict the best next
hop through the paths construction phase. Based on the concept of service
differentiation, EQSR protocol employs a queuing model to handle both real-time and
non-real-time traffic. By means of simulations, they evaluate and compare the
performance of proposed routing protocol with the MCMP (Multi-Constraint Multi-
Path) routing protocol. Simulation results have shown that this protocol achieves
lower average delay, more energy savings, and higher packet delivery ratio than the
MCMP protocol.
2.3. Network Energy Efficiency
WSNs are highly distributed self-organized systems and depend upon a particular
number of scattered low cost small devices. These devices include some strong
demerits in terms of processing, memory, communications and energy capabilities.
Sensor nodes collect measurements of interest over a given space and make them
available to external systems and networks at sink nodes. The power saving
techniques is commonly implemented to increase the independence of the individual
nodes and this technique makes the nodes to sleep most of the time. This can be
balanced with low power communications, which usually lead to multi hop data
Chapter 2. Literature Survey
24
transmission from sensor nodes to sink nodes and vice versa [54]. In order to collect
the data, WSN uses an event-driven model and depends upon the collective effort of
the sensor nodes in the network. Greater accuracy, larger coverage area and extraction
of localized features are some of the advantages of the event-driven model over the
traditional sensing. It is important that the preferred events are reliably transported to
the sink for realizing these potential gains [43]. Habitat monitoring, in-door
monitoring, target tracking and security surveillance are some of the applications
where WSNs can be used. WSNs have some problems to be overcome such as energy
conservation, congestion control, reliability data dissemination, security and
management of a WSN itself. These problems often take part in one or more layers
from application layer to physical layer and it can be studied separately in each
corresponding layer or collaboratively cross each layer. For example, congestion
control may involve only in transport layer but the energy conservation may be
related to physical layer, data link layer, network layer and higher layers [42].
At transport layers, notion of reliability exists in the Pump-slowly, Fetch-quickly: A
Reliable Transport Protocol for Sensor Networks (PSFQ) and Event-to-Sink Reliable
Transport in Wireless Sensor Networks (ESRT) protocols [5,6]. Whereas, PSFQ deals
with data flow with strict delivery guarantees, ESRT is a solution to achieve reliable
event detection with minimum energy expenditure and congestion resolution. Fuzzy
logic based management and control has been studied in the past for wireless
networks and ad hoc networks. Reliable data transport in sensor networks (RMST)
[11] is a transport layer paradigm designed to complement directed diffusion by
adding a reliable data transport service on top of it. It‟s a NACK based protocol like
PSFQ, which has primarily timer driven loss detection and repair mechanisms. It does
not provide with any congestion control mechanism. Jaesub Kim et al. [20] suggest a
transport-controlled MAC protocol (TC-MAC) that combines the transport protocol
into the MAC protocol with the aims of achieving high performance as well as energy
efficiency in multi-hop forwarding. Accordingly TC-MAC also works through a
periodic listen-and-sleep scheme, it lowers end-to-end latency by reserving data
forwarding schedules across multi-hop nodes during the listen period and by
forwarding data during the sleep period, all while increasing throughput by
piggybacking the subsequent data forwarding schedule on current data transmissions
and forwarding data consecutively. In addition, TC-MAC gives a fairness-aware
lightweight transport control mechanism based on benefits of using the MAC-layer
Chapter 2. Literature Survey
25
information. The results show that TC-MAC performs as well as an 802.11-like MAC
in end-to-end latency and throughput, and is more efficient than S-MAC in energy
consumption, with the additional advantage of supporting fairness-aware congestion
control. Sudip Misra et al. in [26] proposed a simple, least-time, energy-efficient
routing protocol with one-level data aggregation that ensures increased lifetime for
the network. This protocol was compared with popular ad hoc and sensor network
routing protocols, viz., AODV ([Royer and Perkins, 1999] and [Das et al., 2003]),
DSR (Johnson et al., 2001), DSDV (Perkins and Bhagwat, 1994), DD (Intanagon
wiwat et al., 2000) and MCF (Ye et al., 2001). It was observed that the proposed
protocol outperformed them in throughput, latency, average energy consumption and
average network lifetime. The proposed protocol uses absolute time and node energy
as the criteria for routing, this ensures reliability and congestion avoidance. Tuan Lea
et al. [29] proposes ERTP, an Energy-efficient and Reliable Transport Protocol for
Wireless Sensor Networks. ERTP is designed for data streaming applications, in
which sensor readings are transmitted from one or more sensor sources to a base
station (or sink). ERTP uses a statistical reliability metric, which ensures the number
of data packets delivered to the sink exceeds the defined threshold. Extensive discrete
event simulations and experimental evaluations shows that ERTP is significantly
more energy-efficient than current approaches and can reduce energy consumption by
more than 45% when compared to current approaches. Consequently, sensor nodes
are more energy-efficient and the lifespan of the unattended WSN is increased.
Sandip Dalvi et al. [43] have proposed a transport protocol, which provides the
desired event reliability to the application, by distributing the load at a sensor among
its children based on their residual energies and average MAC layer data rate. The
event rate distribution happens in such a way that the application at the sink gets its
required event rate and the overall energy consumption of nodes is minimized. They
have derived a method for computing average MAC data rate for these two protocols
and using simulations they have shown that our transport protocol performs close to
optimal. Damayanti Datta et al. [47] have proposed a new protocol for reliable data
transfer in time-critical applications with zero tolerance for data loss in wireless
sensor networks, which uses less time and fewer messages in comparison to an
established protocol PSFQ. The two key features of their proposed protocol are out-
of-sequence forwarding of packets with a priority order for sending different types of
messages at nodes and delaying the requests for missing packets. They have also
Chapter 2. Literature Survey
26
presented two methods for computation of the delay in requesting missing packets.
Ching-Wen Chen et. al. [49] proposed the use of node grouping and transmission
pipelining to reduce power consumption and transmission delay. In the design of node
grouping, there are several groups in WSNs, where nodes in different groups wake up
at different time. Each sensor node is initially set to belong to one of these groups. In
contrast to the situation in which all nodes hear the control packets during the
contention period, node grouping reduce the number of nodes that overhears the
control packets at the same time to reduce power consumption. The group table
recodes the group indices of all the neighbors of that node. With looking up a group
table in a sensor node, a sender can wake up at the group time of the receiver. As a
result, two nodes belonging to different groups can communicate with other. With
regard to transmission delay of a multi-hop path in WSNs, if a sender transmits data
to the receiver and the receiver cannot send the data to the next receiver right now, the
transmission delay increases. To reduce the transmission delay, they proposed the
transmission pipelining method. Transmission pipelining makes the group number of
the nodes on a path to be continuous. Therefore, the sensor node is thus able to
transmit data to the sink node pipelining. From the simulation results, when the
number of groups is 2, the power consumed in transmitting a byte (mJ/byte) and the
transmission delay in our proposed design are better than those of SMAC by about
50%. When the number of groups is 4, although the transmission delay is only a little
better than that of SMAC, the power consumed in transmitting a byte in this design is
much less than the power consumed in SMAC by 75%. Kwan-Wu Chin et al. [50]
proposed E2MAC, an energy efficient, distributed Medium Access Control (MAC)
protocol for identifying and monitoring tags in RFID-enhanced wireless sensor
networks. E2MAC exploits the low power capability of a ultra-wideband transceiver
and distinct pulses to address the reader collision problem. In addition, it uses
ResMon, an enhanced dynamic frame slotted Aloha protocol to read and monitor tags.
Lastly, E2MAC uses a novel load-balancing algorithm to amortize the cost of reading
and monitoring tags to multiple readers. These E2MAC features ensure that
the contention level at each reader is kept at a minimum and distributed fairly. As a
result, E2MAC has a high reading rate and low energy consumption. In addition,
E2MAC helps in minimizing the impact of the tag orientation problem, where a tag
becomes unreadable if its antenna is parallel to a reader‟s field lines. In particular, the
use of multiple readers increases spatial diversity and hence increases the likelihood
Chapter 2. Literature Survey
27
that a tag is readable by at least one reader. Our simulation results show E2MAC to
have very low energy consumption, reading delay and per-reader collision. More
importantly, system designers have the flexibility to lower these metrics further with
additional readers, bigger frame sizes, or by dividing tags into small groups. Marcel
Busse et al. [52] proposed two forwarding schemes termed single-link and multi-link
energy-efficient forwarding that trade off delivery ratios against energy costs. Multi-
link forwarding improves the network performance substantially by addressing
multiple receivers at once during the packet forwarding process. If the first
forwarding node does not receive a packet correctly, other nodes may act as backup
nodes and perform the forwarding instead. By means of mathematical analyses, it is
derived, how the energy efficiency of a forwarding path can be computed and how a
forwarding tree is established. Routing cycles are explicitly taken into account and
prevented by means of sequence numbers. Simulations and real-world experiments
provide a comparison to other reference strategies, showing a superior performance of
the forwarding scheme in terms of energy efficiency.
2.4. Traffic Contention and Network Congestion
Basically following factors are the key factors to control traffic contention and
network congestion.
2.4.1. Reliable Data Transport
The problem of reliable transport over wireless multi-hop networks like WSNs is not
an easy one to solve. Three main sources of packet losses can be found
1. Wireless channel is inclined to introduce transmission errors. Either transmission
from different nodes can collide or other failures in nodes can produce package
losses.
2. Packets can be discarded in the network due to congestion, i.e., intermediate
nodes‟ overload.
3. The receiver might discard packets because they arrive too quickly, implying a
failure in flow control.
2.4.2. Congestion Control
There are two major causes of congestion in WSNs. The first is when the packet
arrival rate exceeds the packet service rate. This is more likely to occur at sensor
Chapter 2. Literature Survey
28
nodes near the sink, since normally they carry more upstream traffic. The second
cause relates to performance aspects of the link layer such as contention, interference,
and bit error rate.
Congestion in WSNs has a direct impact on energetic efficiency and on QoS
parameters. For example, congestion may cause buffer overflow, which could lead to
large queuing delays and higher loss rates. Packet loss not only degrades the
reliability and QoS of the application, but also wastes node energy. The congestion
can also degrade the link utilization. Furthermore, link-level congestion results in
transmission collisions in contention-based link protocols such as CSMA. Collisions
during transmissions increase packet service time and waste energy, so congestion in
WSNs must be efficiently controlled, either to suppress it or to decrease its harmful
effects. Typically, there are three mechanisms for controlling congestion: congestion
detection, congestion notification, and rate adjustment.
2.4.3. Congestion detection
In TCP, the congestion is observed or deduced by end nodes based on a timeout or
redundant acknowledgments. In WSNs, proactive methods are preferred. A common
mechanism would be to concrete a queue length (Hull et al., 2004; Wan et al., 2003),
a service time (Wang et al., 2006), or the packet service time ratio over packet inter
arrival time at the intermediate nodes (Wang et al., 2006). In WSNs with collision-
based MAC protocols such as CSMA, the channel load can be measured and used as a
congestion indication.
2.4.4. Congestion notification
After detecting congestion in the network, the trans- port protocol needs to propagate
data about congestion from the congested nodes to the upstream or source nodes that
contribute to the congestion. The approach to disseminating congestion data can be
classified into implicit congestion notification and explicit congestion notification.
Explicit congestion notification uses special control messages to notify the involved
nodes that congestion is occurring, by means of suppression messages. Implicit
congestion notification is included in normal data packets, usually a bit inside the
packet.
Chapter 2. Literature Survey
29
2.4.5. Rate adjustment
When receiving a congestion indication, the node can adjust its data transmission rate.
If a single congestion notification (CN) bit is used, an additive-
increase/multiplicative-decrease (AIMD) scheme or one of its variants can be applied.
If the protocol implements additional information about congestion, more accurate
rate adjustment schemes can be adopted.
2.5. Medium Access Protocols for WSNs
Several authors have suggested medium access schemes for WSNs, some of which
are modifications of existing protocols for wireless ad hoc networks. This is still a
growing area of research calling attention to several open issues yet to be addressed.
Several recently proposed schemes are discussed below.
2.5.1. Contention-Based Protocols
Sensor MAC (S-MAC): S-MAC is a contention-based MAC protocol explicitly
designed for wireless sensor networks. While reducing energy consumption is the
primary goal in those networks, this protocol has also achieved good scalability and
collision avoidance by using a combined scheduling and contention scheme.
To achieve the primary goal of energy efficiency, the main sources that cause the
inefficient use of energy as well as what trade-offs can be made to reduce energy
consumption need to be identified. In this way, the following major sources of energy
waste are identified:
Collisions: When a transmitted packet is corrupted, it has to be discarded; the
follow-up retransmissions increase energy consumption. Collisions not only waste
energy, but they increase latency as well.
Overhearing: A node can pick up packets intended for other nodes.
Control packet overhead: Sending and receiving control packets also consumes
energy.
Idle listening: Listening to receive possible traffic that was not sent can be the
biggest cause of inefficiency, especially in many sensor network applications when
nodes are in the idle state most of the time. Most sensor networks are designed to
operate over a long period of time; since the nodes are idle for a long time, idle
listening can be a dominant factor behind energy waste in such cases.
Chapter 2. Literature Survey
30
Hongwei Zhanga et al. [27] addresses the challenges of bursty converge cast in multi-
hop wireless sensor networks, where a large burst of packets from different locations
needs to be transported reliably and in real-time to a base station. Via experiments on
a 49 MICA2 mote sensor network using a realistic traffic trace, they determined the
primary issues in bursty converge cast, and accordingly design a protocol, RBC (for
Reliable Bursty converge cast), to address these issues: To improve channel
utilization and to reduce ack-loss, they design a window-less block acknowledgment
scheme that guarantees continuous packet forwarding and replicates the
acknowledgment for a packet; to alleviate retransmission-incurred channel contention,
and introduce differentiated contention control. Moreover, they design mechanisms to
handle varying ack-delay and to reduce delay in timer-based retransmissions. They
evaluate RBC, again via experiments, and show that compared to a commonly used
implicit-ack scheme, RBC doubles packet delivery ratio and reduces end-to-end delay
by an order of magnitude, as a result of which RBC achieves a close-to-optimal
goodput. Hongwei Zhang et al. [39] have designed a window-less block
acknowledgment scheme to improve channel utilization and to reduce
acknowledgment loss that guarantees continuous packet forwarding and replicates the
acknowledgment for a packet They have introduced a differentiated contention
control to alleviate retransmission-incurred channel contention. Moreover, they have
designed mechanisms to handle varying ack delay and to reduce delay in timer based
retransmissions. Ajit Warrier et al. [40] have presented a new hybrid MAC scheme Z-
MAC, for sensor networks. Z-MAC is robust topology changes and clock
synchronization errors; in the worst case its performance falls back to that of CSMA.
They have implemented Z-MAC in Tiny OS and evaluated its channel utilization,
energy, latency and fairness over single-hop, two hop and multi-hop sensor network
topologies constructed using Mica2. Their results have shown that Z-MAC has
remarkably better data throughput than existing sensor MAC protocols while
consuming comparable energy. Yao-Nan Lien et al. [45] has proposed the Hop-by-
Hop TCP protocol for sensor networks aiming to accelerate reliable packet delivery.
Hop-by-Hop TCP makes every intermediate node in the transmission path execute a
lightweight local TCP to guarantee the transmission of each packet on each link. It
takes less time in average to deliver a packet in an error-prone environment.Another
QoS based routing protocol was presented by Akkaya [3], which classifies the traffic
on the basis of real-time and non-real time application data. This protocol further
Chapter 2. Literature Survey
31
makes use of a cumulative link-cost for each link and end-to-end delay and chooses
the least-cost link, though the weightages allocated to different QoS parameters are
not mentioned in detail. In [12], authors presented a fuzzy congestion control
approach for ad-hoc networks, in which a theoretical fuzzy logic based concept is
used to control the congestion. A good number of transport layer protocols have been
proposed for WSNs. These works aim to provide reliability guarantee either by
congestion detection & control or by congestion avoidance [1] [9] [10]. ESRT [6]
allocates transmission rate to sensors such that an application-defined number of
sensor readings is received at a base station, while ensuring that the network is not
congested. On reception of packets with congestion notification a bit high, sink node
regulates the reporting rate by broadcasting a high-energy control signal so that it
could reach the all sources. This high-powered congestion control signal may disrupt
some other transmissions. Also the assumption of congestion notification by the sink
node is very optimistic. In CODA [1], they present a detailed study on congestion
avoidance in sensor networks. The basic idea is that as soon as congestion occurs, the
source (or an intermediate node)‟s sending rates must be reduced to quickly release
the congestion. In the simple case, as soon as a node detects congestion, it broadcasts
a backpressure message upstream. An upstream node that receives the backpressure
can decide to drop packets, preventing its queue from building up and thus controlling
congestion. If multiple sources are sending packets to a sink, CODA also provides a
method of asserting congestion control over these multiple sources by requiring
constant feedback (ACKs) from the sinks. If a source does not receive the ACKs at
predefined times, it will start throttling the sending rates.CODA uses a combination of
the present and past channel loading conditions and the current buffer occupancy, to
infer accurate detection of congestion at each receiver with low cost. As long as a
node detects congestion, it sends backpressure messages to upstream nodes for
controlling reporting rate hop-by hop. It is also capable of asserting congestion
control over multiple sources from a single sink in the event of persistent congestion.
Even though it overcomes some of the limitations of ESRT, it doesn‟t consider the
event fairness and packet reliability at all. PSFQ [5] is scalable and reliable transport
protocol that deals with strict data delivery guarantees rather than desired event
reliability as it is done in ESRT. However, this approach involves highly specialized
parameter tuning and accurate timing configuration that makes it unsuitable for many
applications. As defined in Many-to-One Routing [2], event fairness is achieved when
Chapter 2. Literature Survey
32
equal number of packets is received from each node. Topology Aware Resource
Adaptation to Alleviate Congestion in Sensor Networks (TARA) [10] discusses the
network hotspot problem and presents a topology aware resource adaptation strategy
to alleviate congestion in sensor network. In these two key reasons of packet losses
have been taken into account: loss due to collision and loss due to buffer overflow,
taking care of hierarchical medium access and, thereby, reduces packet drops due to
collision. WRRF controls the number of packets to be received from upstream nodes
in each round (single-hop control). Estimating buffer status at each individual
downstream node using Exponential Moving Average (EWMA) controls round
operation. A downstream node allows packet from its upstream nodes only if there is
available buffer and thereby avoid drops due to buffer overflow. According to
Mohammad HosseinYaghmaeeetal.[15]to support quality of service (QoS)
requirements for multimedia applications having a reliable and fair transport protocol
is necessary. One of the main objectives of the transport layer in WMSNs is
congestion control. In their research they observe that the information provided might
have different levels of importance and argue that sensor networks should be willing
to spend more resources in disseminating packets carrying more important
information. Some applications of WMSNs may need to send real time traffic toward
the sink node. This real time traffic requires low latency and high reliability so that
immediate remedial and defensive actions can be taken when needed. Therefore,
similar to wired networks, service differentiation in wireless sensor networks is also
an important issue. They present a priority-based rate control mechanism for
congestion control and service differentiation in WMSNs. Also a distinguish theory
between high priority real time traffic from low priority non-real time traffic, and
service the input traffic based on its priority is presented. With Simulation results, the
superior performance of the proposed model with respect to delays, delay variation
and loss probability is confirmed. Guohua Zhanga et al. [16] explicitly model link
capacities to be time varying and investigate congestion control problems in multi-
hop wireless networks. They propose a primal–dual congestion control algorithm
which is proved to be trajectory stable in the absence of feedback delay. Different
from system stability around a single equilibrium point, trajectory stability guarantees
the system is stable around a time varying reference trajectory. They also obtain
sufficient conditions for the scheme to be locally stable in the presence of delay. The
key technique is to model time variations of capacities as perturbations to a constant
Chapter 2. Literature Survey
33
link. They also investigate the sensitivity of the control scheme and through
simulations to study the tradeoff between stability and sensitivity. Gungora et al. [17]
comprehensively investigate the interactions between contention resolution and
congestion control mechanisms as well as the physical layer effects in WSAN. An
extensive set of simulations is performed in order to quantify the impacts of several
network parameters on the overall network performance. The results of this analysis
reveal that the interdependency between network parameters calls for adaptive cross-
layer mechanisms for efficient data delivery in WSAN. Bjorn Scheuermann et al. [18]
present a novel hop-by-hop congestion control protocol that has been tailored to the
specific properties of the shared medium. In this scheme, backpressure towards the
source node is established implicitly, by passively observing the medium. A
lightweight error detection and correction mechanism guarantees a fast reaction to
changing medium conditions and low overhead. This approach is equally applicable
to TCP and UDP-like data streams. They demonstrate the performance of this
approach by an in-depth simulation study. These findings are underlined by testbed
results obtained using an implementation of our protocol on real hardware.Ben-Jye
Changae et al. [21] suggests that TCP/IP transmissions, the TCP congestion control
operates well in the wired network, but it is difficult to determine an accurate
congestion window in a heterogeneous wireless network that consists of the wired
Internet and various types of wireless networks. The primary reason is that TCP
connections are impacted by not only networks congestion but also error wireless
links. This paper thus proposes a novel adaptive window congestion control namely
Logarithmic Increase Adaptive Decrease, LIAD for TCP connections in
heterogeneous wireless networks. The proposed RTT-based LIAD has the capability
to increase throughput while achieving competitive fairness among connections with
the same TCP congestion mechanism and supporting friendliness among connections
with different TCP congestion control mechanisms. In the Congestion Avoidance
(CA) phase, an optimal shrink factor is first proposed for Adaptive Decreasing cwnd
rather than a static decreasing mechanism used by most approaches. Second, they
adopt a Logarithmic Increase algorithm to increase cwnd while receiving each ACK
after causing three duplicate ACKs. The analyses of congestion window and
throughput under different packet loss rate are analyzed. Furthermore, the state
transition diagram of LIAD is detailed. Numerical results demonstrate that the
proposed LIAD outperforms other approaches in goodput, fairness, and friendliness
Chapter 2. Literature Survey
34
under diverse heterogeneous wireless topologies. Especially, in the case of 10%
packet loss rate in wireless links, the proposed approach increases goodput up to
156% and 1136% as compared with LogWestwood+ and NewReno, respectively.
Vivek Raghunathan et al. in [22] studied the interaction between TCP congestion
control and wireless interference. One of the triumphs of wireline network research of
the last decade has been the casting of the Internet congestion control problem within
an optimization framework based on utility functions. Such an approach has provided
a sound theoretical understanding of the underlying stability and fairness issues, as
well as a post-facto justification of the scalability and stability of TCP-like additive-
increase multiplicative-decrease (AIMD) algorithms. This paper provides
counterexamples showing that the same result cannot be extended to wireless
networks, at least not in a straightforward manner. The fundamental difference is that
wireless networks are of a broadcast nature. There is no strict notion of a „link‟, since
transmissions from nearby nodes interfere with each other. They consider a fairly
general model of interference in wireless networks, and present a counterexample of a
wireless network in which the congestion control mechanism has an unstable
equilibrium point at the desired fair solution. NS-2 simulations of this counterexample
manifest an oscillatory throughput behavior that is orders of magnitude worse than the
corresponding wired networks. Surprisingly, this oscillatory throughput behavior
appears to be fairly typical of simulations in wireless networks, with almost all
randomly chosen network simulation examples manifesting it. This loss of stability
leads us to suggest that perhaps TCP should be modified for use in wireless networks,
and that a cross-layer redesign of wireless TCP and MAC is needed to explicitly
account for the effects of the wireless nature of interference. Iradj Ouveysia et al. [24]
proposed linearized congestion minimization schemes with working and protection
paths (LCM–WP), in which a mixed integer linear program is formulated to choose
the optimal working and protection paths for every OD pair such that the network
congestion is minimized. In particular, the objective is to minimize the maximum
amount of traffic on the links. To solve realistically sized problems, they consider a
restricted version of the LCM–WP, in which only limited sets of candidate working
and protection paths are considered. A simple algorithm is developed to find
candidate working and protection paths for each origin-destination (OD) pair.
Implementation of our LCM–WP schemes demonstrates the efficiency of this
approach in terms of the number of constraints and solution time. It also shows that
Chapter 2. Literature Survey
35
this approach is applicable to realistically sized networks. RamanujaVedanthama et
al. [25] focus on providing congestion control from the sink to the sensors in a sensor
field. They identify the different reasons for congestion from the sink to the sensors
and show the uniqueness of the problem in sensor network environments. They
proposed a generic framework that addresses congestion from the sink to the sensors
in a sensor network. Through ns2-based simulations, they evaluate the proposed
approach and compare its performance with three baseline approaches. Young-Duk
Kim et al. [32]proposed Distance Adaptive Contention Window (DACW) modified
IEEE 802.15.4 standard. The key mechanism of DCAW is a dynamical channel
access MAC protocol, which is to adjust Contention Window (CW) according to the
hop count distance to sink and traffic condition. With DACW, each sensor node can
achieve self-routing capability with low overhead and performance enhancement.
Furthermore, DACW can be easily applied to existing routing protocols without
additional overhead and shows that its performance is better than the existing MAC
protocol by the simulation result.Md. Mamun-Or-Rashid et al. [33] proposed an
energy efficient congestion avoidance protocol that includes source count based
hierarchical and load adaptive medium access control. The proposed mechanism
ensures load adaptive media access to the nodes and thus achieves fairness in event
detection. The results of simulation show that this scheme exhibits more than 90%
delivery ratio with retry limit 1, even under bursty traffic condition, which is good
enough for reliable event perception. According to Hull et al. [36] network congestion
occurs when offered traffic load exceeds available capacity at any point in a network.
In wireless sensor networks, congestion causes overall channel quality to degrade and
loss rates to rise, leads to buffer drops and increased delays (as in wired networks),
and tends to be grossly unfair toward nodes whose data has to traverse a larger
number of radio hops. Congestion control in wired networks is usually done using
end-to-end and network-layer mechanisms acting in concert. However, this approach
does not solve the problem in wireless networks because concurrent radio
transmissions on different “links” interact with and affect each other, and because
radio channel quality shows high variability over multiple time-scales. We examine
three techniques that span different layers of the traditional protocol stack: hop-by-
hop flow control, rate limiting source traffic when transit traffic is present, and a
prioritized medium access control (MAC) protocol. They implemented these
techniques and present experimental results from a 55-node in-building wireless
Chapter 2. Literature Survey
36
sensor network. They also demonstrate the combination of these techniques, Fusion,
can improve network efficiency by a factor of three under realistic workloads. Ian F.
Akyildiz et al. [37] have developed a unified cross-layer protocol, which replaces the
entire traditional layered protocol architecture that has been used so far in WSNs. The
objective of their proposed cross layer protocol is highly reliable communication with
minimal energy consumption, adaptive communication decisions and local congestion
avoidance. Their protocol operation is governed by the new concept of initiative
determination. Based on this concept, the cross-layer protocol performs received
based contention, local congestion control, and distributed duty cycle operation in
order to realize efficient and reliable communication in WSNs. Wafa Ben Jaballah et
al. [38] have enhanced the QoS in sensor networks. They have presented an approach,
which takes advantage of the standard 802.11e EDCA protocol that ensures effective
end-to-end delay and good quality of traffic. They have tried to improve the provision
of quality of service in sensor networks by offering a new approach, which aims to
improve the mechanism of service differentiation, implemented in the
802.11e.Muhammad MostafaMonowar et al. [41] have proposed an efficient scheme
to control multi-path congestion so that the sink can get priority based throughput for
heterogeneous data. They have used packet service ratio for detecting congestion as
well as performed hop-by-hop multi-path congestion control based on that metric.
Their simulation results have demonstrated the effectiveness of their proposed
approach. Chonggang Wang et al. [42] have proposed a node priority-based
congestion control protocol (PCCP) for wireless sensor networks. In PCCP, node
priority index has been introduced to reflect the importance of each node. PCCP uses
packet inter-arrival time along with packet service time to measure a parameter
defined as congestion degree and furthermore imposes hop-by-hop control based on
the measured congestion degree as well as the node priority index. PCCP controls
congestion faster and more energy-efficiently than other known techniques. Nurcan
Tezcan et al. [44] have addressed the problem of reliable data transferring by first
defining event reliability and query reliability to match the unique characteristics of
WSNs. They have considered event delivery in conjunction with query delivery. They
have proposed an energy-aware sensor classification algorithm to construct a network
topology that is composed of sensors in providing desired level of event and query
reliability. They have analyzed their approach by taking asymmetric traffic
characteristics into account and incorporating a distributed congestion control
Chapter 2. Literature Survey
37
mechanism. They have evaluated the performance of their proposed approach through
an ns-2 based simulation and show that significant savings on communication costs
are attainable while achieving event and query reliability. Sunil Kumar et al. [46]
have studied the performance of ESRT in the presence of over-demanding event
reliability, using both the analytical and simulation approaches. They have shown that
the ESRT protocol does not achieve optimum reliability and begins to fluctuate
between two inefficient network states. With insights from update mechanism in
ESRT, they have proposed a new algorithm, called enhanced ESRT (E2SRT), to solve
the over-demanding event reliability problem and to stabilize the network. Their
simulation results show that their E2SRT outperforms ESRT in terms of both
reliability and energy consumption in the presence of over-demanding event
reliability. It also ensures robust convergence in the presence of dynamic network
environments. Ilker Demirkol et al. [51] showed the significance of using a realistic
and application-specific packet traffic model by comparing the performance of a well-
known WSNs protocol under the Surveillance WSNs packet traffic model (SPTM), as
well as under periodic and binomial traffic models. A packet traffic framework
specific to surveillance applications is proposed which is then used for deriving
SPTM analytically. In order to be adaptable and flexible, SPTM incorporates a
probabilistic and parametric sensor detection model. Simulation results show that to
employ an application-specific packet traffic model has significant impact on the
performance evaluation of the WSN and ordinary traffic models may overestimate the
capacity of the WSN.
2.6. Network Routing
In WSNs, reliability is a design goal of a primary concern. To build a comprehensive
reliable system, it is essential to consider node failures and intruder attacks as
unavoidable phenomena. Y. Challalet et al. present a new intrusion-fault tolerant
routing scheme offering a high level of reliability through a secure multipath routing
construction. Unlike existing intrusion-fault tolerant solutions, our protocol is based
on a distributed and in-network verification scheme, which does not require any
referring to the base station. Furthermore, it employs a new multipath selection
scheme seeking to enhance the tolerance of the network and conserve the energy of
sensors. Extensive analysis and simulations using Tiny-OS showed that our approach
Chapter 2. Literature Survey
38
improves many important performance metrics such as: the mean time to failure of
the network, detection overhead of some security attacks, energy consumption, and
resilience [56]. WSNs with nodes spreading in a target area have abilities of sensing,
computing, and communication. Since the GPS device is expensive, Tai-Jung Chang
et al. [57] used a small number of fixed anchor nodes that are aware of their locations
to help estimate the locations of sensor nodes in WSNs. To efficiently route sensed
data to the destination, the server, identifying the location of each sensor node can be
of great help. This work adopts a range-free color-theory based dynamic localization
approach, to help identify the location of each sensor node. Since sensor nodes are
battery-powered, we propose an efficient color-theory-based energy efficient routing
(CEER) algorithm to prolong the lifetime of each sensor node. The uniqueness of this
approach is that by comparing the associated RGB values among neighboring nodes,
a better routing path with energy awareness can be efficiently chosen. Besides, the
CEER has no topology hole problem. Simulation results have shown that CEER
algorithm can save up to 50–60% energy than ESDSR in mobile wireless sensor
networks. In addition, the latency per packet of CEER is 50% less than that of
ESDSR.
Routing protocols for WSNs face two challenges. One is an efficient bandwidth
usage, which requires minimum delay between transfers of packets. Establishing
permanent routes from the source to destination addresses this challenge since the
received packet can be immediately transmitted to the next node. However, any
disruption on the established path either causes packet loss, lowering the delivery rate,
or invokes a costly process of creating an alternative path. The second challenge is the
ability to tolerate permanent and transient failures of nodes and links, especially since
such failures are frequent in sensor networks. Protocols that chose the forwarding
node at each hop of a packet are resilient to such failures, but incur the delay caused
by selection of the forwarding node at each hop of the multi-hop path. Thomas A.
Babbitt et al. [58] present a novel wireless sensor routing protocol, self-selecting
reliable path routing (SRP) for wireless sensor network (WSN) routing that addresses
both challenges at once. This protocol evolved from the self-selecting routing (SSR)
protocol, which is essentially memory-less. In the first generation of SSR protocols
each packet selects the forwarding node at each hop on its path from the source to
destination. The protocol takes advantage of broadcast communication commonly
used in WSNs as a communication primitive. It also uses a prioritized transmission
Chapter 2. Literature Survey
39
back-off delay to uniquely identify the neighbor of the forwarder that will forward the
packet As a result, the protocol is resistant to node or link failures as long as an
alternative path exists from the current forwarder to the destination. The second
generation of SSR protocols, called self-healing routing (SHR) added the route repair
procedure, invoked when no neighbor of the forwarder closer to the destination is
alive. In a series of transmissions, a packet trapped at the current forwarder by failures
of its neighbors is capable of backing-off towards the source to find an alternative
route, if such exists, to the destination. The main contribution of this work [58] is the
third generation of SSR protocols, termed self-selecting reliable path routing, SRP. It
preserves SHRs dynamic path selection in face of failure. Yet it also enables packets
to follow established paths without selection delay if failures do not occur. The
important change in the protocol is to make it memorize the successfully traversed
path and attempt to reuse it for subsequent packets flowing to the same destination.
The interesting behavior of SRP arising from this property is that if a path from the
source to destination exists on which no transient failures occur, SRP would converge
its routing to such a reliable path. A novel element of the SRP protocol that resulted in
the desired properties is described in this work. Using simulation, SRP protocol with
the representatives of the two other approaches: AODV as the route-based protocol,
and GRAB and SHR as the hop-selection protocols are compared. Considering severe
resources constraints and security threat of wireless sensor networks, [59] proposed a
novel hierarchical routing protocol algorithm. The proposed routing protocol
algorithm can adopt suitable routing technology for the nodes according to the
distance of nodes to the base station, density of nodes distribution, and residual
energy of nodes. Comparing the proposed routing protocol algorithm with simple
direction diffusion routing technology, cluster-based routing mechanisms, and simple
hierarchical routing protocol algorithm through comprehensive analysis and
simulation in terms of the energy usage, packet latency, and security in the presence
of node compromise attacks, the results show that the proposed routing protocol
algorithm is more efficient for wireless sensor networks.
WSNs are collection of wireless sensor nodes forming a temporary network without
the aid of any established infrastructure or centralized administration. In such an
environment, due to the limited range of each node‟s wireless transmissions, it may be
necessary for one sensor node to ask for the aid of other sensor nodes in forwarding a
packet to its destination, usually the base station. One important issue when designing
Chapter 2. Literature Survey
40
wireless sensor network is the routing protocol that makes the best use of the severely
limited resource presented by WSN, especially the energy limitation. Another import
factor required attention from researchers is providing as much security to the
application as possible. Nidal Nasser et al. [60] proposed routing protocols in the
literature focus either only on increasing lifetime of network or only on addressing
security issues while consuming much power. None of them combine solutions to the
two challenges. In there this work a new routing protocol called SEEM: Secure and
Energy-Efficient multipath Routing protocol is proposed. SEEM uses multipath
alternately as the path for communicating between two nodes thus prolongs the
lifetime of the network. On the other hand, SEEM is effectively resistive to some
specific attacks that have the character of pulling all traffic through the malicious
nodes by advertising an attractive route to the destination. The performance of this
protocol is compared to the Directed Diffusion protocol. Simulation results show that
this protocol surpasses the Directed Diffusion protocol in terms of throughput, control
overhead and network lifetime.
A new class of WSNs that harvest power from the environment is emerging because
of its intrinsic capability of providing unbounded lifetime. While a lot of research has
been focused on energy-aware routing schemes tailored to battery-operated networks,
the problem of optimal routing for energy harvesting wireless sensor networks (EH-
WSNs) has never been explored. The objective of routing optimization in this context
is not extending network lifetime, but maximizing the workload that can be
autonomously sustained by the network.
In [61] Emanuele Lattanzi et al. present a methodology for assessing the energy
efficiency of routing algorithms for networks whose nodes drain power from the
environment. A methodology that makes use of graph algorithms and network
simulations for evaluating the MESW starting from a network topology, a routing
algorithm and a distribution of the environmental power available at each node is
proposed. A tool flow implementing the proposed methodology is presented and
comparative results achieved on several routing algorithms is shown. Experimental
results highlight that routing strategies that do not take into account environmental
power do not provide optimal results in terms of workload sustainability. Using
optimal routing algorithms may lead to sizeable enhancements of the maximum
sustainable workload. Since environmental power sources change over time, the
Chapter 2. Literature Survey
41
results prompt for a new class of routing algorithms for EH-WSNs that are able to
dynamically adapt to time-varying environmental conditions.
Shun-Yu Chuang et al. [62] propose a simple and scalable approach to multisink
routing scheme in WSNs. These networks are a rapidly growing discipline, with new
technologies emerging and new applications under development. In addition to
providing light and temperature measurements, wireless sensor nodes have
applications such as security surveillance, environmental monitoring, and wildlife
watching. One potential problem in a sensor network is how to transmit packets
efficiently from single-source to multi-sinks, i.e., to gather data from a single sensor
node and deliver it to multiple clients who are interested in the data. The difficulty of
such a scenario is finding the minimum-cost multiple transmission paths. Many
routing algorithms have been proposed to solve this problem. Most current algorithms
address the reduction of power consumption, and potentially introduce a large delay.
This work proposes a novel multi-path routing algorithm, called hop count based
routing (HCR) algorithm, which considers energy cost and transmission delay
simultaneously. A hop count vector (HCV) is introduced to support routing decision.
Moreover, an additional pruning vector (PV) can further enhance routing
performance. The proposed algorithm also provides a maintenance mechanism to
handle the consequence of faulty nodes. A failure of a node leads to an inaccurate
HCV. Therefore, an efficient correction algorithm is necessary. An Aid-TREE (A-
TREE) is applied to facilitate restricted flooding. This correction mechanism is more
efficient than full-scale flooding for correcting the limited inaccurate HCVs. Finally,
the impact of failed nodes is studied, and an algorithm, called Lazy-Grouping, is
proposed to enhance the robustness of HCR.
Rumor routing [63] is a classic routing algorithm based on agents‟ random walk. This
work proposes a novel approach based on this routing algorithm. Hamid Shokrzadeh
et al. tried to improve the latency and energy consumption of the traditional algorithm
using propagation of query and event agents within straight lines, instead of using
purely random walk paths. Result showed that this method improves the delivery ratio
of the queries, which is a drawback of traditional rumor routing. Due to the reduction
of final path length between source and destination, they introduced a second layer
geographical routing. Moreover, a method is proposed to reduce the cost of
localization equipments by using cheaper equipments like AoA antennas. In order to
Chapter 2. Literature Survey
42
compare the performance measures with traditional algorithm, a simulation
framework is developed and extensive simulations are performed.
2.7. Security and Data Management in WSNs.
The rapid progress of wireless communication and embedded micro-sensing MEMS
technologies has made WSNs possible. WSNs normally come a large area with many
inexpensive, tiny sensor nodes, each capable of collecting, processing, and storing
environmental information, and communicating with neighboring nodes. In the past,
sensors were connected by wired lines but nowadays, ad hoc networking technologies
can much simplify the network formation task. Installation and configuration of a
wireless sensor network are thus effortless. Many applications of wireless sensor
networks have been proposed, including field data collection, remote monitoring and
control, smart home, factory automation, security, etc. Security is sometimes viewed
as a standalone component of a system‟s architecture, where a separate module
provides security. This separation is, however, usually a flawed approach to network
security. To achieve a secure system, security must be integrated into every
component, since components designed without security can become a point of attack.
Consequently, security must pervade every aspect of system design.[34]
Dai Zhi-Feng et al. [101] considered the features of limited energy and densely
deployed sensor nodes; the key challenge in wireless sensor networks is to reduce the
power consumption and the high information redundancy. Furthermore,
communication is widely viewed as the dominating power cost in many sensor
networks applications, so it is obvious that it will save energy if data is aggregated
before being sent to the sink node. As the illustrative examples are shown, rough set
theory is a useful tool for local redundancy information de-correlation and can
eliminate redundant transmission so as to provide a solution to uncertain data
management for wireless sensor networks. In [99] Steffen Peter et al. present an
overview of end-to-end encryption solutions for converges cast traffic in wireless
sensor networks that support in-network processing at forwarding intermediate nodes.
Other than hop-by-hop based encryption approaches, aggregator nodes can perform
in-network processing on encrypted data. Since it is not required to decrypt the
incoming ciphers before aggregating, substantial advantages are 1) neither keys nor
plaintext is available at aggregating nodes, 2) the overall energy consumption of the
backbone can be reduced, 3) the system is more flexible with respect to changing
Chapter 2. Literature Survey
43
routes, and finally 4) the overall system security increases. A qualitative comparison
of available approaches, point out their strengths, respectively weaknesses, and
investigate opportunities for further research is provided. Wireless Sensor Network
has been widely used and its real-time data processing capability is very limited. [98]
puts forward a new solution, which is the novel wireless sensor network node design
with hyperchaos encryption based on FPGA. With the wide application on Wireless
Sensor Network based on ZigBee protocol, authors encrypt the transported data in the
network using hyper chaos by FPGA, which combines their flexibility in rapid real-
time data processing with free configuration in FPGA and enhances the security of the
transported data. According to Arif Selcuk et al. [100] designing cost-efficient, secure
network protocols for WSNs is a challenging problem because sensors are resource-
limited wireless devices. Since the communication cost is the most dominant factor in
a sensor's energy consumption, we introduce an energy-efficient Virtual Energy-
Based Encryption and Keying (VEBEK) scheme for WSNs that significantly reduces
the number of transmissions needed for rekeying to avoid stale keys. In addition to the
goal of saving energy, minimal transmission is imperative for some military
applications of WSNs where an adversary could be monitoring the wireless spectrum.
VEBEK is a secure communication framework where sensed data is encoded using a
scheme based on a permutation code generated via the RC4 encryption mechanism.
The key to the RC4 encryption mechanism dynamically changes as a function of the
residual virtual energy of the sensor. Thus, a one-time dynamic key is employed for
one packet only and different keys are used for the successive packets of the stream.
The intermediate nodes along the path to the sink are able to verify the authenticity
and integrity of the incoming packets using a predicted value of the key generated by
the sender's virtual energy, thus requiring no need for specific rekeying messages.
VEBEK is able to efficiently detect and filter false data injected into the network by
malicious outsiders. The VEBEK framework consists of two operational modes
(VEBEK-I and VEBEK-II), each of which is optimal for different scenarios. In
VEBEK-I, each node monitors its one-hop neighbors where VEBEK-II statistically
monitors downstream nodes. In this work VEBEK's feasibility and performance
analytically and through simulations is evaluated. The results show that VEBEK,
without incurring transmission overhead (increasing packet size or sending control
messages for rekeying), is able to eliminate malicious data from the network in an
energy-efficient manner. It is also shown that this framework performs better than
Chapter 2. Literature Survey
44
other comparable schemes in the literature with an overall 60-100 percent
improvement in energy savings without the assumption of a reliable medium access
control layer. Recent advances in distributed in-network data storage and access
control have led to active research in efficient and robust data management in wireless
sensor networks (WSNs). Although numerous schemes have been proposed this far,
most of them do not provide enough attention towards exploiting user hierarchy and
sensor heterogeneity, which is quite a practical issue especially when deploying
WSNs in mission-critical application scenarios. [95] propose an efficient secret-key
cryptography-based (SKC) fine-grained data access control scheme for securing both
distributed data storage and retrieval. In this design, secret keying information for
data encryption and decryption are constructed based on the scheme of Blundo et al.
with information-theoretic security. To further enhance the security strength, author
then propose an efficient user revocation scheme based on the idea of blinded Merkle
hash tree construction. Extensive performance analysis shows that the proposed
schemes are very efficient and practical for WSNs. [92] proposes a new lightweight
authenticated encryption mechanism based on Rabbit stream cipher referred to as
Rabbit-MAC, for wireless sensor networks (WSNs) that fulfils both requirements of
security as well as energy efficiency. The proposed scheme provides data
authentication, confidentiality and integrity in WSNs. Rabbit based MAC function is
constructed, which can be used for data authentication and data integrity. A security
protocol is an idea for resource constrained WSNs is proposed, and can be widely
used in the applications of secure communication where the communication nodes
have limited processing and storage capabilities while requiring sufficient levels of
security. The features of Rabbit-MAC scheme conclude that this particular scheme
might be more efficient than the existing schemes in terms of security and resource
consumption. The security comes more to the fore. [91] presents SecSens, an
architecture that provides basic security components for wireless sensor networks.
Since robust and strong security features require powerful nodes, it uses a
heterogeneous sensor network. In addition to a large number of simple (cheap) sensor
nodes providing the actual sensor tasks, there are a few powerful nodes (cluster
nodes) that implement the required security features. The basic component of SecSens
offers authenticated broadcasts to allow recipients to authenticate the sender of a
message. To protect the sensor network against routing attacks, SecSens includes a
probabilistic multi-path routing protocol, which supports the key management and the
Chapter 2. Literature Survey
45
authenticated broadcasts. SecSens also provides functions to detect forged sensor data
by verifying data reports en-route. SecSens is successfully evaluated in a real test
environment with two different kinds of sensor boards.Routing in wireless sensor
networks is different from that in commonsense mobile ad-hoc networks. It mainly
needs to support reverse multicast traffic to one particular destination in a multihop
manner. For such a communication pattern, end-to-end encryption is a challenging
problem. To save the overall energy resources of the network sensed, data needs to be
consolidated and aggregated on its way to the final destination. [96] presents an
approach that 1) conceals sensed data end-to-end by 2) still providing efficient and
flexible in-network data aggregation. The aggregating intermediate nodes are not
required to operate on the sensed plaintext data. A particular class of encryption
transformations and discuss techniques for computing the aggregation functions
"average” and "movement detection” is applied. It shows that the approach is feasible
for the class of "going down” routing protocols. The risk of corrupted sensor nodes by
proposing a key pre-distribution algorithm that limits an attacker's gain and show how
key pre-distribution and a key-ID sensitive "going down” routing protocol help
increase the robustness and reliability of the connected backbone is considered.
S.Muhammad et al. [87] defined that sensor network comprises of scattered sensor
nodes with limited computational capabilities and battery power. The present security
solutions for traditional wireless networks cannot be used because of the constraints
associated with sensor network. A secure sink node architecture as two-tiered scheme
for sensor network security is presented. The architecture protects the sink node from
unauthorized access by surrounding it with two protection layers. Sink nodes listen to
only inner layer nodes and inner nodes are allowed to communicate with only outer
layer nodes. These protection layers are formed in an intelligent manner without
violating constraints specific to sensor network. In order to enhance security,
protection layers are re-adjusted in case of an attack. Statistical analysis to elucidate
the performance of proposed architecture is also presented.
Wireless sensor networks are researched extensively over the past few years. They
were first used by the military for surveillance purposes and have since expanded into
industrial and civilian uses such as weather, pollution, traffic control, and healthcare.
One aspect of wireless sensor networks on which research conducted is the security of
wireless sensor networks. These networks are vulnerable to hackers who might go
into the network with the intent of rendering it useless. An example of this would be
Chapter 2. Literature Survey
46
an enemy commandeering a drone and getting it to attack friendly forces. In this
paper, we review the security of wireless sensor networks. Areas that are covered
include: architectures and routing protocols, security issues that include context and
design as well as confidentiality, integrity, and authenticity, algorithms, and
performance issues for wireless sensor network design. Performance of the Self-
Originating Wireless Sensor Network (SOWSN), Practical Algorithm for Data
Security (PADS), and mechanisms for in-network processing were investigated in
further detail with SOWSN having the best performance as a result of it being based
on realistic scenarios [88]. The security solutions for generic wireless sensor networks
cannot be directly used in smart grid WSNs. In [89] the applications of sensor
networks in electric power systems are discussed and analyzed first. Then, the
characteristics of smart grid WSNs are summarized. Threats and security
requirements special for wireless sensor networks used in smart grid systems are
presented. Based on these works, reference security architecture was proposed to
guide the development and the design of the security solutions of wireless sensor
networks in smart grid systems, considering the information security requirements of
electric power systems. Moreover, open security issues needed solve to protect WSNs
applied in smart grid, and research challenges are introduced. A wireless network
solution can support mobility and flexibility of nodes in a network. Especially in
sensor networks, it has many advantages to replace cables with wireless logical links.
On the other hand, Bluetooth is generally considered as a promising short-range
wireless technology because of its inexpensive cost, low power and small size, and
thus Bluetooth has been gaining increasing interest from various industries. For the
above reasons, we adopt Bluetooth technology for a wireless sensor network, which is
designed for security systems. Since Bluetooth will continue to be a feature found in
many devices, it is worthwhile to investigate its use in wireless sensor networks. In
[90] a Bluetooth wireless sensor network for security systems is described, which
includes the implementation issues about system architecture, power management,
self-configuration of network, and routing. The methods or algorithms described in
this paper can be easily applied to other embedded Bluetooth applications for wireless
networks.
Distributed sensor data storage and retrieval have gained increasing popularity in
recent years for supporting various applications. While distributed architecture enjoys
a more robust and fault-tolerant WSNs, such architecture also poses a number of
Chapter 2. Literature Survey
47
security challenges especially when applied in mission-critical applications such as
battlefield and e-healthcare. First, as sensor data are stored and maintained by
individual sensors and unattended sensors are easily subject to strong attacks such as
physical compromise, it is significantly harder to ensure data security. Second, in
many mission-critical applications, fine-grained data access control is a must as
illegal access to the sensitive data may cause disastrous results and/or be prohibited
by the law. Last, sensor nodes usually are resource-constrained, which limits the
direct adoption of expensive cryptographic primitives. To address the above
challenges, in [97] a distributed data access control scheme that is able to enforce
fine-grained access control over sensor data and is resilient against strong attacks such
as sensor compromise and user colluding. The proposed scheme exploits a novel
cryptographic primitive called attribute-based encryption (ABE), tailors, and adapts it
for WSNs with respect to both performance and security requirements. The feasibility
of the scheme is demonstrated by experiments on real sensor platforms.
Wireless multimedia sensor networks (WMSNs) support many acoustic applications
for audio surveillance, animal tracking/vocalization, human health monitoring, etc.
However, resource constraints in sensor networks (such as limited battery power,
bandwidth/computation capability, etc.) pose challenges for the quality and security
of audio data transmission and processing. The security is a critical issue since audio
information can be accessed or even manipulated in WMSNs. In order to ensure
security, audio quality and energy efficiency, an index-based selective audio
encryption scheme for WMSNs is proposed. The scheme protects data transmissions
by incorporating both resource allocation and selective encryption based on modified
discrete cosine transform (MDCT). In this proposed work, the audio data importance
is leveraged using the MDCT audio index, and wireless audio data transmission
proceeds with energy efficient selective encryption. The simulation results depicts
that the proposed approach offers a significant gain in terms of energy efficiency,
encryption performance and audio transmission quality.
Some wireless sensor networks (WSNs) must transmit the data to users securely and
quickly. The sensor nodes just have limited computation, communication and storage
capabilities. Moreover adversary can easily eavesdrop in the process of message
transmission, the data is protected through encryption. In [93] present a new data
encryption scheme for the transmission, which uses the lightweight cryptography and
lets several sensor nodes to encrypt and transmit data cooperatively, thus it can
Chapter 2. Literature Survey
48
lighten the load of single sensor node, ensure the communication security, and
provide good load balancing and a higher lifetime of the network.
2.8. Communication Protocol Architectures
The protocol stack used in wireless sensor networks combines power and routing
awareness, integrates data with networking protocols, communicates power
efficiently through the wireless medium, and promotes cooperative efforts of sensor
nodes. The protocol stack consists of the application layer, transport layer, network
layer, data link layer, physical layer, power management plane, mobility management
plane, and task management plane as show below.
Figure 2.1 WSNs Protocol Stack
_____________________________________________________________________
2.8.1. Physical Layer
The physical layer is the first level of the protocol stack. It performs services
requested by the data link layer. The physical layer is the most basic network layer,
providing only the means for transmitting raw bits rather than packets over a physical
data link connecting network nodes. No packet headers or trailers are consequently
added to the data by the physical layer. The bit stream may be grouped into code
words or symbols and converted to a physical signal that is transmitted over a
physical transmission medium, which is the wireless medium in WSNs. The physical
layer provides an electrical, mechanical, and procedural interface to the transmission
Chapter 2. Literature Survey
49
medium. Broadcast frequencies, the modulation scheme used, and similar low-level
features are specified in the physical layer. The physical layer determines the bit rate,
also known as the channel capacity, digital bandwidth, maximum throughput, or
connection speed.
A variety of physical layer wireless transmission technologies are used in traditional
wireless networks. Considering the specific physical-layer requirements of wireless
sensor networks and taking into consideration the particular characteristics and usage
scenarios, it can be inferred that spread-spectrum technologies meet the requirements
much better than narrowband technologies. Besides, ultra wideband technologies are
found to be a promising emerging alternative.
2.8.2. Data Link Layer
The data link layer is responsible for multiplexing data streams, data frame detection,
medium access, and error control. It ensures reliable point-to-point and point-to-
multipoint connections in a communication network. The most important tasks of the
link layer are the formation and maintenance of direct communication associations
(„„links‟‟) between neighboring nodes and the reliable and efficient transfer of
information across these links. Reliability has to be achieved despite time-variable
error conditions on the wireless link. Nevertheless, the collaborative and application-
oriented nature of the sensor networks and the physical constraints of the nodes, such
as energy and processing limitations, determine the way in which these
responsibilities are fulfilled.
This layer is subdivided into Logical Link Control (LLC) and Medium Access
Control (MAC). In WSNs the fundamental design issue is the MAC. MAC protocols
solve a seemingly simple task of coordinating when a number of nodes access a
shared communication medium. We will explain the specific requirements and
problems of a WSNs MAC layer and present the fundamental MAC protocols.
2.8.3. MAC Requirements for WSNs
Medium Access Control design in sensor networks is very different from traditional
wireless MAC schemes due to the inherent WSNs limitation, among them the energy
constraint. The MAC protocol in a wireless multi-hop, self-organizing sensor network
must achieve two main goals
Chapter 2. Literature Survey
50
1. Creating network infrastructure: Since thousands of sensor nodes are densely
scattered in a sensor field, the MAC scheme should establish communication links
for data transfer. This forms the basic infrastructure needed for wireless
communication and gives the sensor network self-organizing ability.
2. Efficiently using and sharing energy and communication resources between
sensor nodes: Novel protocols and algorithms are needed to effectively tackle the
unique resource constraints and application requirements of sensor net- works,
which means that MAC schemes in other wireless networks cannot be adopted into
the sensor network scenarios. Mobility also poses unique challenges to MAC
protocol design since weak mobility implies topology changes, while strong
mobility means new nodes or node failures.
WSNs requirements are different from those of traditional wireless networks. The
additional requirements come principally from the need to save energy. The
importance of energy efficiency for MAC protocols design is relatively new; thus,
many of the classical protocols like ALOHA and CSMA (Carrier Sense Multiple
Access) do not take this requirement into account. Other typical performance
characteristics such as fairness, throughput, or delay have played a minor role in
WSNs, yet recently they have been receiving more attention.
In WSNs, scalability and robustness requirements are confronted with the frequent
changes in the topology, which are generally produced by temporary power decreases
in nodes, node mobility, new node deployment, or „„death‟‟ of existing nodes. The
need for scalability is evident when considering very dense WSNs with dozens or
thousands of nodes.
Good collision management is also important since it can be useful for saving energy,
both in transmission from the source node and in reception at the destination node.
Collisions should be avoided by design (fixed assignments/ TDMA or assignments
under demand protocols) or by suitable collision suppression procedures to offset the
hidden-terminal problem in CSMA protocols.
Low complexity must be fulfilled by the MAC protocol for WSNs related to energy
savings. Because the nodes used in WSNs are simple, they should not consume an
exceptional amount of resources such as memory, energy, or processing power.
Accordingly, computationally expensive operations, such as complex scheduling
algorithms, should be discarded.
Chapter 2. Literature Survey
51
Most of the MAC protocols are classified in two groups: contention-based or
schedule-based. The difference is the number of contestants that have the option of
transmitting to a node at a given instant:
In contention-based protocols, any node can try to transmit with the risk of
collisions. As all nodes have to contend for the communication channel,
collisions are possible and are one of the major causes of energy inefficiency.
Consequently, these protocols have several mechanisms to suppress collisions or
to reduce the probability of occurrence. In a contention-based wireless sensor
network, since nodes can directly transmit information to the base station at any
time, idle listening can also occur. This is one of the main sources of energy
waste in these networks since the nodes normally remain inactive for a long time
without transmitting. The benefit of these protocols is their simplicity and
robustness.
In schedule-based or polling-based protocols, only one neighbor has the
opportunity to transmit at any given time, thus eliminating collisions. These
protocols usually have a TDMA component, which also provides an implicit
mechanism of passive listening suppression. When a node knows the slots it has
been assigned, it is sure that the communication, both transmission and reception,
will only be produced at these slots; otherwise, the receptor can be deactivated.
This scheme is much more complicated since the base station must poll the nodes
and then gives each one a time to transmit. The constraint of these protocols is
the large amount of data transmitted to set up the network structure. However,
once the structure is created, there is no chance of collisions and nodes can save
energy in their operation.
2.8.4. Network Layer
The network layer is the third level in the WSNs protocol stack. It responds to service
requests from the transport layer and issues service requests to the data link layer. In
essence, the network layer is responsible for end-to-end, i.e., source-to-destination,
packet delivery, whereas the data link layer is responsible for node-to-node, i.e., hop-
to-hop, packet delivery. The network layer provides the functional and procedural
means of transferring variable-length data sequences from a source to a destination
via one or more networks while maintaining the quality of service requested by the
Chapter 2. Literature Survey
52
transport layer. The network layer performs network routing, flow control, network
segmentation/de-segmentation, and error control functions.
Due to the deployment characteristics of WSNs, multi-hop communication may be a
good choice for sensor networks with strict consumption and trans- mission power
level requirements. In a multi-hop network, intermediate nodes have to relay packets
from the source to the destination node. Those intermediate nodes have to decide
which neighbor to forward to. The construction and maintenance of the routing tables
needed for reaching the destination node is the crucial task of a distributed routing
protocol. This section discusses some mechanisms for routing and forwarding that can
be implemented by WSNs routing protocols. These mechanisms take into account
whether a unique node identifier identifies the packet, by a set of such identifiers, or
by all nodes in the network.
The network layer of the WSNs is usually designed according to the following
principles:
Energy efficiency is always an important consideration.
WSNs are mostly data-centric. Sensors do not usually have a unique ID, because
the overhead of ID maintenance is high. The data themselves are usually more
important than knowing which nodes send data.
An ideal WSN has attribute-based addressing and location awareness.
Data aggregation: Depending on the application, this can be useful although the
energy needed for data aggregation is sometimes higher than the savings.
The routing protocol needs to be easily integrated with other networks.
In some cases, the routing protocol must be QoS-aware, thus having specific
mechanisms related to the delay and reliability of the traffic flow.
These design principles serve as a guideline when designing a routing protocol
for sensor networks and are further explained to emphasize their importance.
2.8.5. Transport Layer
The second-highest layer in the WSNs protocol stack, the transport layer responds to
the services requested from the application layer and issues service requests to the
network layer. The transport layer provides dependable data transfers between hosts.
It is usually responsible for end-to-end error recovery and flow control and for
ensuring complete data transfer.
Chapter 2. Literature Survey
53
The purpose of the transport layer is to provide reliable data transfer services between
end users, thus relieving the upper layers‟ responsibility for providing reliable and
cost-effective data transfer. The transport layer usually turns the unreliable and very
basic service provided by the network layer into a more powerful one. There is a long
list of services that can be optionally provided at this level, although none is
compulsory. Since not all applications require all services available, some can be
wasted overhead or even counterproductive in some cases.
Some of the particular challenges for transport protocols in WSNs include the
following:
WSNs are multi-hop wireless networks with homogeneous/heterogeneous nodes.
TCP has several drawbacks when used over wireless channels; thus, a WSNs is not
an easy environment for TCP.
Any transport protocol must adapt to the stringent energy constraints, memory
constraints or computational constraints of sensor nodes. Significant engineering
efforts would be required to run heavyweight protocols like TCP on such nodes.
Generally, transport protocols do not have good behavior with dynamic topologies.
2.8.6. Application Layer
The application layer is the last level of the WSNs protocol stack. It interfaces directly
with the application, performs common services for the application processes, and
issues requests to the transport layer. The common application layer services provide
semantic conversion between associated application processes. The application layer
of the five-layer WSN protocol stack corresponds to the application layer, the
presentation layer, and the session layer in the seven-layer OSI model.
Although many application areas for sensor networks have been defined and
proposed, potential application layer protocols for sensor networks remain a largely
unexplored region. Some application protocols for WSNs are the Sensor Management
Protocol (SMP), the Task Assignment and Data Advertisement Protocol (TADAP),
and the Sensor Query and Data Dissemination Protocol (SQDDP).
2.9. Various Middleware WSNs Approaches
Different middleware approaches were selected and classified taking the
programming models used into account.
Chapter 2. Literature Survey
54
Programming Wireless Sensor Networks
Programming Abstraction Programming Support
1. Global Behavior
2. Local Behavior
1. Virtual Machine
2. Data Base
3. Modules
4. Application Driven
5. Message-oriented
6. Middleware
Figure 2.2. Programming Models
Programming sensor networks includes two major classes shown in Figure 2.2.The
first one is programming support, which manages the providing systems, services, and
run-time mechanisms, such as reliable code distribution, safe code execution, and
application-specific services. The second one is programming abstraction, which is
related to the way a sensor network is viewed and presents concepts and ideas of
sensor nodes and sensor data.
2.10. Programming Support
The programming support class consists of five approaches virtual machine–based,
modular programming–based, database-based, application-driven, and message-
oriented middleware as shown in Figure 2.2.
2.10.1. Virtual Machine
This approach consists of virtual machines (VM), interpreters, and mobile agents. Its
main characteristic is flexibility, allowing developers to write applications in divided
small modules, which are injected and distributed through the network by the system
using tailored algorithms and then interpreted by the VM. Those tailored algorithms
minimize the overall energy expenditure as well as resource use. However, the
technology is complex and the instructions introduce overhead.
2.10.2. Modular Programming (Mobile Agents)
The use of mobile code facilitates the injection and distribution through the network
and leads to application modularity. Less energy is necessary when broadcasting
Chapter 2. Literature Survey
55
small modules instead of the complete application.
2.10.3. Database
This approach observes the entire network as a virtual database system, offering an
easy-to-use interface that permits the user to extract data of interest and issue queries
about the sensor network. Nevertheless, this approach does not support real-time
applications, as it provides only approximate results and the detection of spatial-
temporal relationships between events is not possible.
2.10.4. Application-Driven
This approach establishes a new, innovative aspect in middleware research by
complementing an architecture that accomplishes the network protocol stack,
enabling programmers to adjust the network according to the exact application
requirements. It provides a QoS advantage since the applications determine the
network operations management.
2.10.5. Message-Oriented Middleware (MOM)
This approach is essentially a communication model in a distributed-sensor network.
The system facilitates message exchange between nodes and the sink nodes by means
of a publish-subscribe mechanism. This model supports asynchronous
communication, making movable combinations between the sender and receiver
possible.
2.10.6. Characteristics of WSNs Middleware
WSNs middleware should support the implementation and basic operation of a sensor
network while taking into consideration some of the unique characteristics of WSNs:
Sensor nodes are small-scale devices (with volumes approaching a cubic
millimeter in the near future).
Sensors are limited in the amount of energy stored and/or harvested from the
environment.
Sensors are likely to fail, due to depleted batteries or to environmental
influences.
Sensors have restricted resources (CPU performance, memory, wireless
Chapter 2. Literature Survey
56
communication bandwidth and range).
Node mobility, node failures, and environmental obstructions cause frequent network
topology changes. Communication failures are also a typical problem in wireless
sensor networks. Another issue is heterogeneity since the network may consist of a
large number of rather different nodes in terms of sensors, computing power, and
memory. On the one hand, the large number raises scalability issues; on the other
hand, it provides a high level of redundancy. Nodes also have to be able to operate in
unattended mode since it is impossible to service a large number of nodes in remote
or inaccessible locations. In order to deal with the characteristics outlined above,
WSNs middleware must face the following challenges:
Supporting the development, maintenance, deployment, and execution of
sensing based applications. This includes mechanisms for defining complex,
high-level sensing tasks, communicating these tasks to the WSNs, coordinating
sensor nodes to split and distribute the tasks to each node, gathering data to
merge the sensor readings of the individual sensor nodes into a high-level result,
and reporting the results back to the task issuer.
Working in a network with a great number of wirelessly connected nodes
(sensors).
Providing abstraction of the network for heterogeneity among the different
components of the WSNs.
Fulfilling the main requirements of WSNs, namely, energy efficiency, reliability,
and scalability, allowing event-based or periodic communications. These
approaches represent the characteristics of the WSNs better than the traditional
scheme (based on requests and responses). Providing support for automatic
configuration and fault management, which are necessary for the unattended
way the nodes operate.
Paying attention to the concepts of time and location. These are the key elements
for unifying the information obtained by the different sensors.
Providing application knowledge in nodes. Middleware for WSNs has to provide
mechanisms for injecting application knowledge into the infrastructure and the
WSNs.
Chapter 2. Literature Survey
57
2.11. WSNs Topologies and Deployment Methodologies
The term „„topology‟‟ refers to the physical disposition in which the nodes of a
network (in this case, a WSNs) are connected to one another. Network topology only
refers to node connections. The distance between nodes, physical inter- connections,
transmission rates, or types of signals do not belong in this category, although they
can be influenced by the topology. However, a good WSNs design takes the topology
into account when improving several performance factors such as energy efficiency,
robustness, or general QoS parameters.
Figure 2.3. Types of Sink
To understand the topologies of a WSNs, the types of nodes that form the network
first need to be introduced. WSNs contain both sources and sinks. A source can be
any entity in the network that is able to provide information. It is usually a sensor
node, but it can also be an actuator node that provides feedback about an operation.
On the other hand, a sink is the entity that requires information. There are two
possibilities for a sink: It can belong to the WSNs and be just another sensor/actuator
node, or it can be an external entity. If the sink is an actuator belonging to the WSNs,
it could be, for example, a laptop used to interact with the sensor nodes. If it is an
external element, the sink may be a gateway to another network such as the Internet,
where the information requests come from some external device/node indirectly
connected to the WSNs. These main types of sinks are illustrated in Figure 2.3.
Chapter 2. Literature Survey
58
The types of network topologies can be classified according to several criteria. In
addition, the network hierarchy should be taken into account when selecting a suitable
and efficient routing scheme. In fact, the main WSN topology division is based on the
existence or absence of hierarchy among network elements.
In flat networks or those networks without hierarchy each node has the same
capabilities. Thus, control over the routes and channels must be performed in a
distributed fashion.
In hierarchical networks, some nodes will have different capabilities than others.
These capabilities are divided into two areas: physical, where the nodes or links have
different physical characteristics, and logical, in which the nodes have different
functions in the network.
The most common and representative WSNs topologies are the following
Figure 2.4. WSN topologies, (a) Ad-Hoc Network, (b) Clustered Network, (c) Overlay Network
2.11.1. Ad hoc without Hierarchy
In this case, all nodes are equal. They are their own service providers, and thus data
pass from node to node to reach a sink. A common example of this type of network is
the mobile ad hoc networks (MANETs), although this scheme can also be valid for
networks formed by nodes of low or no mobility.
2.11.2. Hierarchical network by Clustering
The idea of hierarchy implies assigning some nodes with a special role, for example,
controlling neighboring nodes. In this sense, local groups or clusters can be formed;
Chapter 2. Literature Survey
59
the „„controllers‟‟ of such groups are often referred to as cluster heads. The major
functions of the cluster heads are local resource arbitration (i.e., in MAC protocols),
making routing tables more stable since all traffic is routed through the cluster heads,
and making higher-layer protocols more scalable since the higher layer perceives a
less complex network due to clustering. Furthermore, cluster heads are the usual
places where the traffic is aggregated and com- pressed to converge to a single sink.
2.11.3. Overlay networks
This type of clustering network has both physical and logical hierarchies. Nodes that
assume special control functions are thus more powerful and/or have privileged
capacities with respect to the rest. This way, the more powerful nodes may form a
network on their own allowing higher scalability. An example of this type of network
is a cellular network, where base stations, which, in turn, are connected to a wired
infrastructure for, control cells inter cell routing. Another possible topology in a
network is called a mesh topology. In this case, all sensor nodes are identical and can
communicate directly with each other, providing a high level of redundancy in the
data paths between nodes. In a mesh WSNs, every node should be in the area of radio
coverage of any other node, which is a disadvantage since nodes in WSNs have,
limited power. This reduced available energy makes it unviable to implement a WSNs
with mesh topology if the coverage area exceeds certain dimensions, for example in
environmental or agriculture applications, or if it has a strong attenuation, such as
inside buildings. Therefore, in many situations it is necessary to accept a topology that
is not completely meshed, having some nodes route the information of others without
being the packet destination. This is known as multi-hop routing.
Figure 2.5. Multi-hop Routing
A topology with multi-hop routing has both advantages and disadvantages with
Chapter 2. Literature Survey
60
respect to a meshed topology. Some advantages are the following:
Not only is the multi-hop routing a functional solution for solving problems with
large distances or obstacles, but it also has been used for improving energy
efficiency in communications. The radio channel attenuation increases at least at
a quadratic rate with the distance in most environments, thus wasting less energy
with a multi-hop architecture than with single-hop topologies. The global power
consumption is lower if the nodes transmit to other neighboring nodes than in a
hypothetical situation in which every node transmits directly to a sink or
gateway.
If the density of intermediate nodes (relays of the information) is larger, the
reutilization frequency distance is shorter. Therefore, the global capacity for data
transmissions increases.
For several applications it is very convenient to carry out data aggregation in the
intermediate nodes instead of transmitting all the raw data generated by nodes. A
multi-hop routing allows the nodes that route the information to aggregate the
data received with their own data and transmit only the summarized or
aggregated information. Sending less information increases both the global
information transmission capacity of the network and its lifetime, thus saving
energy.
Among the disadvantages of multi-hop routing are (1) the larger delay between
generating the information and its reception by the sink and (2) some applications‟
requirement for controlled delays. The reasons for these issues are the following:
Each packet will be queued inside each of the nodes through which it is routed,
producing larger and, in general, variable delays. If the percentage of resource
use in the WSNs is low, this delay may not be significant, although the
application determines what it significant and what is not.
Even if queuing delays are not significant, the functioning of the MAC protocol
in each hop may add an important amount of time. For instance, in many MAC
protocols, the nodes switch from states of low or null activity with very low
energy consumption to activity states in which they may send or receive data.
Waiting for an active period of a neighboring node in order to send messages
may be a source of delay. Another example can be found in MAC protocols
designed for star topologies, in which a node must join the master of the
neighboring star nearest the destination prior to forwarding a packet. Allowing a
Chapter 2. Literature Survey
61
node to be in the influential area of more than one master node may alleviate this
problem.
2.12. Design Strategies and Operation of WSNs Software
A proposal for possible WSNs software design cycle can be found in Blumenthal et
al. (2003). They proposed a software organization with an intermediation software
layer (middleware) above the operating system. Its aim is to provide services to the
applications. Blocks represent all the components of this architecture. Figure 2.6
shows the proposed software development cycle for these types of network.
Figure 2.6 Software Development Cycle
The structure of the running software per Blumenthal et al.‟s proposal is shown in
Figure 2.7 with continuing advancements in sensor node design and increasingly
complex applications, an interest in design automation of sensor network applications
is inevitable. The objective is to eventually enable domain experts to be able to design
and analyze algorithms, and automatically synthesizes programs for an abstract
machine model of the underlying system, without requiring knowledge of low-level
networking aspects of the deployment.
Component
Design & Edit
Compilation/linked
Evaluation
Node Software
Identify and include each block component
Interconnect components & resolve dependencies. Optimize parameters
Executable creation
Run. Monitor & Evaluate
Chapter 2. Literature Survey
62
Figure 2.7. Software Architecture
2.13. Software Architecture in WSNs
In typical WSNs deployments, several types of applications and software coexist in
different hardware platforms, such as sensor nodes, server nodes, or gateways, and
client equipment. WSN architecture has different layers, as shown in Figure 2.7.
Several authors coincide with this layer decomposition, although there are some
differences in nomenclature.
As depicted in Figure 2.8 the software architecture consists of three different layers:
the mote layer, the server layer, and the client layer.
Figure 2.8. WSNs Software Architecture
The mote layer is composed of the motes with their sensors. In this layer, the
Chapter 2. Literature Survey
63
software needs to include the light operating system executed and the
corresponding applications necessary to obtain a service, i.e., environment
monitoring or intruder tracking. These applications are usually developed for the
specific hardware on which they are going to run, and the programming
language used must fit well with highly restricted devices; nesC can be an
adequate programming language. nesC was created specifically to adapt
application programming in embedded network systems, a category that includes
WSNs. The main characteristics of nesC‟s design were inspired by the TinyOS
operating system: event-based execution, incorporation of a concurrence model,
and component-based application design. In fact, TinyOS has been
reimplemented in the nesC language.
The server layer receives the information from the WSN by using proprietary
protocols and stores it in databases. It also offers services, usually by means of
TCP/IP interfaces, in order to allow interested clients to access this information.
In this case, the programming languages are selected not based on the ack of
resources, but rather on their portability to execute in machines with different
characteristics and different general-purpose operating systems for different
platforms.
The client layer includes a graphical user interface that allows the information,
topology, and state of the WSNs as well as its management to be seen. This
software must provide the user with the information needed for managing the
WSNs, interpreting the large amounts of information generated, and monitoring
the network‟s health.
2.14. Network Simulation and Commonly used Simulators
Wireless sensor networks have tremendous potential to monitor, study, and analyze
phenomena in the physical world in detail never before available, in places too far,
too deep, too high, or too dangerous for researchers to go. Simulation can be of great
help to ensure the shortest possible time to market and to minimize the overall cost of
WSNs design. Being that the cost, time, and complexity involved in deploying and
constantly changing large-scale WSNs are prohibitively high, simulation is a cost-
effective choice for the rapid exploration and validation of WSNs applications.
Simulation provides controlled and repeatable environmental conditions for
Chapter 2. Literature Survey
64
evaluating and optimizing the design parameters and/or the configuration alternatives.
It also offers very good insight into the effects of the various parameters and thus
helps identify those that have the greatest importance for the system‟s operation.
The simulation of wireless networks is inherently different from that of wired
networks. The signal interference and attenuation concerns are more complicated for
wireless media than for wired media. The broadcast nature of wireless radio
transmission also makes communication topology in simulation models relatively
denser than for an equivalent wired network. Specific features like node mobility,
distributed behavior, power, and terrain models dramatically increase the computation
effort of WSNs simulators. Consequently, accurate fine-grained WSNs simulations
present a significant challenge.
The sensor nodes detect the stimuli, i.e., signals, generated by the target nodes over a
sensor channel and forward the detected information to the sink nodes over a wireless
channel. Two different models for signal propagation are therefore included: a sensor
propagation model and a wire- less propagation model.
The existing simulator tools are either commercial or open source and are mainly
developed in Java or C++. These simulators strongly differ with respect to features
such as
Scalability: The capacity to simulate a high number of nodes with sufficient
precision in a finite time.
Real-time requirement: Real-time or close to real-time simulation can be related to
the simulation architecture and the programming language and to some other
features as well.
Software emulation: The possibility to run the various software during simulation
as if they were running on the real processor also called „„software in the loop.‟‟
Hardware emulation: The ability to accurately simulate the behavior of various
hardware parts of the node and to evaluate some important parameters such as
consumption, memory use, collisions, etc.
Model fidelity: Availability of detailed models for various aspects of net- working
such as propagation, protocols, mobility, etc.
Reliability study: Possibility to simulate the occurrence of various faults or defects,
i.e., hardware, physical, noise, etc., during the network simulation and to visualize
their effects on the network‟s behavior or on some parameters such as
Chapter 2. Literature Survey
65
consumption, delays, etc.
X in the loop: The possibility of connecting other (hardware and/or software)
devices to the simulator for various purposes; simulation-time reduction by
replacing a hardware simulation model by a real hardware system, either existing
or simulated on an FPGA or other hardware platform, performance analysis of
protocols by injection of real signals, reliability study in the presence of real
perturbations, etc.
In communication and computer network research, network simulation is a technique
where a program models the behavior of a network either by calculating the
interaction between the different network entities (hosts/routers, data links, packets,
etc) using mathematical formulas, or actually capturing and playing back observations
from a production network. The behavior of the network and the various applications
and services it supports can then be observed in a test lab; various attributes of the
environment can also be modified in a controlled manner to assess how the network
would behave under different conditions. When a simulation program is used in
conjunction with live applications and services in order to observe end-to-end
performance to the user desktop, this technique is also referred to as network
emulation.
Positive Train Control (PTC) refers to microprocessor-based communication
technologies that are capable of preventing train collisions, derailments, and injuries
to workers operating within the railroad system. In North America, there are 11
competing PTC projects in various stages of refinement. The North American Joint
Positive Train Control Project (NAJPTC) is one of those efforts, based on the
Advanced Train Control System (ATCS) protocol. NAJPTC is a joint development
project of the Association of American Railroads, the Federal Railroad
Administration, and the Illinois Department of Transportation. Paul Vincent et al.[63]
used a network simulation system (NS-2) to model NAJPTC ATCS communications
to determine the feasibility of that PTC system when overlaid onto geographic data
from Google Earth, and digital elevation data from the United States Geological
Survey. The Simulations were useful in observing and tracking the signal strength of
a moving train, resolving packet collisions via strategic base station placement, and
modeling and mitigating communication losses.
A network simulator is a software program that imitates the working of a computer
network. In simulators, the computer network is typically modeled with devices,
Chapter 2. Literature Survey
66
traffic etc and the performance is analyzed. Typically, users can then customize the
simulator to fulfill their specific analysis needs. Simulators typically come with
support for the most popular protocols in use today, such as WLAN, Wi-Max, UDP,
and TCP.
Some of the most relevant academic WSN simulators are presented below. Often
these simulators are still under development; some of them are well suited to help
with research.
OMNeT++
OMNeT++ is an open-source tool that shares many concepts, solutions, and features
with OPNET OMNeT++is a discrete-event, component-based, modular, and open-
architecture simulation environment with strong GUI support and an embeddable
simulation kernel.OMNeT++provides component architecture for models.
Components, i.e., modules, are programmed in Cþþand then assembled into larger
components and models using a high-level language (NED).
GloMoSim (Global Mobile Information System Simulator)
A simulation environment for purely wireless mobile networks, GloMoSim was
designed as a set of modules in architecture structured into eight layers. Each module
simulates a specific protocol in the protocol stack. GloMoSim has been designed
using the parallel discrete-event simulation capability provided by PARSEC
(PARSEC), a C-based sequential and parallel simulation language that can be used to
program new modules that can be added to GloMoSim. GloMoSim offers different
protocols to model node mobility and radio communication. GloMoSim has already
been used to simulate networks with thousands of wireless nodes and provides a rich
set of models for both existing and novel protocols at multiple layers of the protocol
stack. Apparently, GloMoSim does not offer environment or power models.
Ptolemy
This is an ongoing project at UC Berkeley that studies the modeling, discrete-event
simulation, and design of concurrent real-time embedded systems. The key
underlying principle in Ptolemy is the ability to use multiple computation models
(e.g., continuous-time, data flow, finite state machine) in a hierarchical heterogeneous
design environment. Ptolemy does not support network emulation but does support
both wireless network and sensor network simulations.
Chapter 2. Literature Survey
67
NS-2
This is a discrete-event simulator that provides support for TCP, routing, and
multicast protocols, among many others. The support for wireless and mobile network
simulation provides various modules for mobile wireless network simulation, such as
radio propagation models, the IEEE 802.11 MAC protocol, mobility models, different
ad hoc routing protocols (e.g., AODV and DSR), and Mobile IP. The latest version of
ns-2 supports the simulation of pure wireless LANs, multiple-hop ad hoc networks,
and combined simulation of wired and wireless (known as „„wired-cum-wireless‟‟)
networks. Maintaining real code in ns-2 is not transparent.
QualNet
QualNet is a network simulation tool that simulates wireless and wired packet mode
communication networks. QualNet Developer is a discrete event simulator used in the
simulation of MANET, WiMAX networks, satellite networks and sensor networks,
among others. QualNet has models for common network protocols that are provided
in source form and are organized around the OSI Stack. QualNet is a commercial tool
derived from GloMoSim that was first released in 2000 by Scalable Network
Technologies (SNT). Today, the main differences between QualNet and GloMoSim
are
QualNet is based on C++; GloMoSim is based on PARSEC C (a C based parallel
simulation language).
QualNet is a commercial product; GloMoSim is distributed under an academic open
source license.
QualNet is maintained by SNT; UCLA Parallel Computing Lab maintains
GloMoSim.
2.15 Summary
In this chapter review of related published literature of various authors of the field of
WSNs is presented. Basic theoretical concepts and factors related to field in question
is also explained. From this chapter we also get the idea of new researches and
experiments, which will be helpful to compare and evaluate our work. In the next
chapter system architecture of our 3 L-S model will be presented.