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CHAPTER 3

LITERATURE SURVEY

3.1 General Networks

The emerging trends in wireless networks accelerated the need for

scalable and efficient network support. These applications include video

conferencing, battle field, disaster management, etc. The traditional protocol

in wired networks is extremely inefficient for such group based applications,

since related issues across the network to each receiver is complex. In all

these applications, communication and coordination among a given set of

nodes are necessary. Wireless protocols play a vital role in mobile networks

to provide this communication efficiently. The studies conducted by various

authors are presented in this chapter.

Chakravorty et al (2004) presented performance optimizations for

Wireless Wide Area Networks (WWAN) and their experimental evaluation.

The approach dealt with spurious time-outs, based on a TCP sender

algorithm, and several recommendations for TCP hosts, such as enabling the

time stamp option. The system was developed in large windows for

improving the performance over wireless networks.

Zhang (2006) proposed routing challenges, and an overview of

intermittently connected mobile ad hoc networks and delay tolerant networks.

Data Transmission Networks (DTNs) abolish connections in favor of bundles,

which are messages that synthesize the entire request or response. The

networks are based on the store-and-forward mode instead of message

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passing, allowing nodes to communicate even if an instantaneous path to the

destination cannot be found. The nodes to the destination cache the bundles

on permanent storage, when the destination is unreachable.

Davy et al (2006) presented a policy-based architecture to enable

autonomic communications to implement autonomic algorithms and protocols

that manage the network. The autonomic plane should be divided into two

blocks, namely the knowledge sub-plane and the control sub-plane. The

knowledge sub-plane performs the first task, while the control sub-plane

performs the second, using policies to configure the behavior of the algorithms.

Baldauf et al (2007) proposed a survey of context-aware systems.

The network makes requests based on addresses, service and data mobility.

Whenever a service is inserted, deleted or moved, a lengthy notification

process must be undertaken. All nodes on the network that are using this

service or that cached the location must be informed about the new position.

Another problem with content being bound to an address is that, a Content

Delivery Network (CDN) like mirroring requires a non-trivial set of

mechanisms, such as the Domain Name System (DNS) which redirects and

explicit content rewriting.

3.2 Wireless Sensor Networks

WSNs are highly distributed networks of small, lightweight

wireless nodes, deployed in large numbers to monitor the environment or to

monitor a system by the measurement of physical parameters such as

temperature, pressure, humidity or related parameters. WSNs have drawn

considerable amount of research interest for their omnipresent applications in

various fields, such as environmental monitoring, spatial exploration, habitat

sensing, target tracking, and battlefield surveillance.

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Alec et al (2001) presented an Adaptive Rate Control (ARC)

scheme that monitors the injection of packets into the traffic stream as well as

route-through traffic. At each node, an estimation of the number of

downstream mote is made, and the bandwidth is split up proportionally

between locally generated and route-through traffic. The resulting bandwidth

allocation is approximately fair. The reduction in transmission rate of route-

through traffic has a backpressure effect on downstream motes, which can

then reduce their generation rates.

Sankarasubramaniam et al (2003) proposed an Event-to-Sink

Reliable Transport (ESRT) protocol. The sink uses congestion feedback from

sensor nodes to broadcast a notification to reduce reporting frequency. The

effectiveness of this method is dependent on the persistence of congestion and

the feedback latency. If congestion is transient, and feedback latency is

significantly large, the notification arrival may arrive when congestion is no

longer present. Feedback latency depends on the diameter of the network, and

the solution is not scale to huge sensor networks experiencing transient

congestion.

Ozgur et al (2005) presented a reliable transport scheme for

wireless sensor networks, the ESRT protocol. ESRT is a transport solution

developed to achieve reliable event detection in WSN with minimum energy

expenditure. This includes a congestion control component that serves the

dual purpose of achieving reliability and conserving energy. The algorithms

run on the sink, with minimal functionality required at resource constrained

sensor nodes. ESRT protocol operation is determined by the current network

state based on the reliability achieved and congestion condition in the

network.

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Mohammad et al (2005) proposed a handbook of sensor networks.

In a WSN environment, sensor networks cannot be separated from an external

network which transfers control, query, and monitoring messages. With the

rapid development of sensor technology, most of the sensor nodes have much

more resources and powerful computing capability than before. Some of the

nodes are expected to be mobile in a modern WSN.

Yogesh et al (2005) presented the requirements of a transport

protocol and proposed Sensor Transmission Control Protocol (STCP). This is

generic, scalable and reliable transport layer protocol where a majority of the

functionalities are implemented at the base station. STCP offers controlled

variable reliability, congestion detection and avoidance, and supports multiple

applications in the same network.

Chieh-Yih et al (2005) presented a simple, scalable, and robust

transport protocol named Pump-Slowly, Fetch-Quickly (PSFQ), which is

customizable to meet the needs of emerging reliable data applications in

sensor networks. This represents a simple approach and makes minimum

assumptions about the underlying routing infrastructure. This is scalable and

energy-efficient because it supports minimum signaling and thereby reduces

the communication cost for data reliability.

Krishnamurthy et al (2006) proposed a seamless access to sensor-

based services. Session Initiation Protocol (SIP) has some protocols

independence, flexible naming and support of mobility; thus, the protocol can

inherently provide a good framework for user mobility. SIP as much as

possible and integrate it with tiny TCP protocol stack together to meet the

new requirements of the wireless nodes in a WSN.

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Paramasivan et al (2006) demonstrated a new dimension of PSFQ,

making an effort to increase the protocol performance. A sensor node is

allowed to buffer a sufficiently large amount of data into the buffer, thereby

allowing simultaneous transmission of multiple packets. In order to hold more

number of data at the sensor node an additional buffer space is added at each

node.

Shu et al (2006) presented connecting heterogeneous sensor

networks with TCP based wired or wireless networks. Another approach is

using middleware to connect the sensor networks. The method to interconnect

with sensor networks was proposed using the bridge. This is difficult to

convert most kinds of protocols between sensor networks and TCP networks

using the bridge. The architecture of bridge will become more complicated

and is rather hard to implement such a complicated bridge which will easily

cause processing bottleneck problem and lead to single point failure.

Gomez et al (2006) described Ad hoc On-Demand Distance Vector

(AODV) for IEEE 802.15.4 mesh sensor networks in a real environment. The

low IPv6 IETF group is creating a lighter version of IPv6 for IEEE 802.15.4

networks. IPv6 requires a payload larger than the supported by low power

networks and LowIPv6 will define an adaptation layer that converts IP

packets to “light” IP packets. This creates a lighter version of AODV which is

adapted to mesh networks of restricted devices.

Guangjie et al (2007) proposed connecting sensor networks with IP

using a configurable tiny TCP protocol stack. A different protocol set of tiny

TCP/IP stack based on the different resource conditions of sensor nodes was

proposed. The implementation of the configurable tiny TCP protocol stack

allows the users to query, control and monitor in different WSNs.

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Lazarou et al (2007) proposed a cluster head method to allow

parallel transmission of data packets to form a schedule by arranging data

transfer at each round. The cluster head accepts request for data transfer and

assigns a slot for each node wishing to transmit. Each node of data transfer is

divided into contention, data transmission and idle period. In WSN the single

point failure is eliminated by providing a decentralized control and nodes that

have no data to send waste time slots in the contention period where idle

listening and overhearing occurs.

Ali et al (2008) formulated the problem of data transport in a WSN

as a set of operations with reliability block diagram. The operations aim at

filtering the raw data to streamline its reliable transport towards the sink.

Based on the formulation systematically define a reliability framework and

compare the reliability of existing data transmission protocol ESRT and

Reliable Multi-Segment Transport (RMST) with the new framework.

Wang-Rong et al (2008) presented the detection and tracking of

continuously moving objects, such as wild fires, biochemical materials, and

so on, by developing a Continuous Object Detection and tracking Algorithm

(CODA) based on a hybrid static or dynamic clustering technique. The

mechanism enables each sensor node to detect and track the moving

boundaries of objects in the sensing field.

Seung-Jong et al (2008) presented the problem of reliable sink-to-

sensors data delivery and several fundamental challenges that need to be

addressed and are unique to the environment of WSN. Also, proposed a

scalable framework for reliable downstream data delivery that is specifically

designed to both address and leverage the characteristics of the WSN while

achieving the reliability in an efficient manner.

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Zhan-Bo et al (2009) presented a Prediction-Based Event-to-Sink

Reliable Transport (PBESRT) protocol to improve the performance of the

networks. This can predict the flow rate of the next time interval, and then

adjust the reporting frequency rate. The performance of prediction based

ESRT protocol depends on the accuracy of predictors.

Wang et al (2009) described an energy-effective secure topology

control protocol for WSNs. The topology is made of hexagonal cells

according to node locations, security for updating sensor network topology is

provided by controlling new node securely adding to network with the help of

one way hash chain and symmetric cryptographic key. Network energy

consumption is reduced by the way that new nodes form temporal clusters.

Zaher et al (2009) proposed an Event Based-Medium Access

Control (EB-MAC) that is tailored for event based systems. Event based

systems are characterized by having long periods of inactivity and short

abrupt periods of high data contention when an event is detected. The system

arranges data transfer dynamically using an election based scheduling

technique.

Leandro et al (2009) proposed a data fusion protocol in WSNs for

controlled environment that minimizes the amount of messages exchanged in

the network and makes communication more efficient. This is implemented in

an infrastructure from which an user can access the data collected by the

sensors from any device connected to the Internet.

Ibrahim et al (2010) addressed non-trival performance problems in

contention-based wireless networks and presented a method for admission

control in contention-based networks, implemented as a component of a

performance management system. The system can be used as a tool for

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dimensioning and configuration as well as for real-time admission control.

The often unpredictable dynamics in contention-based access networks means

that continuous performance control is needed to maintain a desired QoS.

Ni et al (2010) proposed a data fusion strategy for WSNs based on

trust and cluster. In the intra-cluster, the data fusion is done by setting up the

relay node and using the trust value of the node as weight of data. The

selection principle of relay node, the process of choose the relay node and the

data fusion based on the trust are discussed. The effective and energy

consumption of the data fusion algorithm are analyzed and illustrated through

an example.

Chi-Tsun et al (2011) presented a delay-aware data collection

network structure to minimize delays in the data collection process of WSNs.

Two network formation algorithms are designed to construct the proposed

network structure in a centralized and a decentralized approach. Simulation

shows the proposed network structure is able to shorten the delays in the data

collection process significantly.

Xiaohua et al (2011) proposed an efficient distributed algorithm

that produces a collision-free schedule for data aggregation in WSN to

minimize the delay. The distributed scheduling method has an upper bound on

delay. The proposed algorithm is a constant approximate algorithm, which

significantly reduces the aggregation delay.

Chih-Kuang et al (2011) introduced a distributed and scalable

scheduling access scheme that mitigates high data loss in data-intensive

sensor networks and can handle some mobility. The approach alleviates

transmission collisions by employing virtual grids that adopt Latin Squares

characteristics to time slot assignments. The algorithm derives conflict free

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time slot allocation schedules without incurring global overhead in

scheduling.

Marjan et al (2011) formulated the problem of multiple targets

coverage in WSNs as determining the sensing range of each sensor node to

maximize total utility of the network. The utility model includes a logarithmic

function of sensing range for the utility of each sensor node as an

approximation to the number of targets it covers. A distributed price-based

algorithm is derived from dual decomposition technique for each node to

adjust its sensing range during iterations with static targets.

Yu et al (2011) proposed a novel algorithm named Loss Inference

based on Passive Measurement (LIPM), to infer WSN link loss performance.

The algorithm passively monitors the application traffic between sensor nodes

and sink, and then uses network tomography technology to infer the network

internal performance.

Ing-Ray et al (2011) developed an adaptive fault-tolerant QoS

control algorithms based on hop-by-hop data delivery utilizing „source‟ and

„path‟ redundancy, with the goal to satisfy application QoS requirements

while prolonging the lifetime of the sensor system. Also a mathematical

model for the lifetime of the sensor system as a function of system parameters

including the „source‟ and „path‟ redundancy levels utilized are developed.

Quazi et al (2011) proposed a chain construction scheme, which

creates several chains for the topology using Voronoi Tessellation (VT). The

scheme divide the target field into a number of small areas called Voronoi

cells so that in each cell, a chain is constructed. The protocol used to construct

a chain in a Voronoi cell, guarantees the summation of square to the distances

would be the lowest.

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Seong-hee et al (2011) presented a Coding-Aware Real-Time

Routing (CARTR) that schedules coded and uncoded data in a link.

Traditionally the coding-aware routing protocols have weakness regarding

delivery of time-sensitive data in lossy links. The improvement from network

coding is negligible when using real-time data delivery. The proposed

CARTR system is implemented and compared with IEEE 802.11 and the

simulation reveals that the proposed system has a throughput improvement of

20%.

Gayathri et al (2011) proposed a holistic approach to cognition in

sensor networks, which can be achieved by incorporating learning and

reasoning in the upper layers, and opportunistic spectrum access at the

physical layer. They also provide framework based on knowledge and

cognition that can be helpful to achieve end-to-end goals of application-

specific sensor networks.

Jose et al (2011) proposed an approach for interaction with real-

world devices through a web services interface, allowing users to configure

and apply various operations, including complex closed-loop techniques that

monitor and act over any actuator in the WSN. The interaction between the

client application and the motes are implemented with an AP to access

services of the motes.

Charalambos et al (2011) proposed a novel and simple Dynamic

Alternative Path Selection Scheme (DAIPaS) attempting to face congestion

by increasing capacity while attempts to maintain performance requirements.

DAIPaS can efficiently and adaptively choose an alternative routing path in

order to avoid congested nodes, by taking into consideration a number of

critical parameters that affect the performance of a WSN while maintaining

overhead in minimal levels.

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ShaoJie et al (2011) addressed the maximum support coverage

problem in WSNs. Traditionally every point on the resultant path should fall

within the sensing range of at least one sensor node. In the work, every point

on the resultant path is covered by at least „k‟ sensors while optimizing

certain objectives.

Chiu-Ching et al (2011) proposed an event Ordering By Double

Confirmation (OBDC) that could guarantee the event ordering to be correct

completely. The simulation results demonstrated that the rate of correct event

ordering of OBDC could be up to 100 %, but its event handling time and

energy consumption increased only more 3.2% and 4.4%, respectively, than

the existing Ordering By Confirmation (OBC) algorithm.

Shucheng et al (2011) proposed 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.

Ahmed et al (2011) investigated the effectiveness of cluster-based

routing protocols in extending the lifetime for energy-constrained WSN.

Routing decisions affect the number of transmissions, the distance covered

per transmission and the load placed on the intermediate nodes that participate

in relaying the messages. The study focused on common parameters of well-

known cluster based routing protocols.

Wu et al (2011) carried out a theoretical analysis of the Distance

Vector-HOP (DV-HOP) algorithm and proposed Total Least Square (TLS)

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algorithm and is applied to node localization algorithm to make further

localization accuracy. Simulations are carried out using MATrix LABoratory

(MATLAB) tool and the algorithm gives better localization accuracy results

than original algorithm.

Deng et al (2011) proposed a Mobility-Based Clustering (MBC)

protocol for WSN with mobile nodes. A sensor node elects itself as a cluster-

head based on its residual energy and mobility. A non-cluster-head node is

allocated a timeslot for data transmission in ascending order in a Time

Division Multiple Address (TDMA) schedule based on the estimated

connection time. Simulation shows that the proposed protocol can reduce the

packet loss by 25% compared with the Cluster-Based Routing (CBR) protocol

and 50% compared with the Low-Energy Adaptive Clustering Hierarchy-

mobile (LEACH-mobile) protocol.

Rocio et al (2011) relied on stochastic tools to develop selective

message forwarding schemes. The scheme will depend on parameters such as

the available battery at the node, the energy cost of retransmitting a message,

or the importance of messages. The results contribute to identify the variables

on other nodes, and have a great impact on the overall network performance.

Also, suboptimal schemes that rely on local estimation algorithms and entail

reduced computational cost are also designed.

Chakchai et al (2012) proposed animal tracking systems using

Arduino board equipped with various sensors built into a compact prototype

attached to animal collar. Location and sensor information are sent over

Global System for Mobile communication (GSM) and Radio Frequency (RF)

technology for monitoring and searching. The battery power is efficiently

utilized by using analog light sensor with motion logic.

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Jyh-How et al (2012) proposed a short term and long term wildlife

and ecological monitoring systems using WSNs. Short term system uses off-

the-shelf devices and can be easily available in the market. Before

establishing large scale wildlife or ecological monitoring network, a rapid

prototype of the targeted network is constructed.

XiaoHua et al (2012) studied periodic query scheduling for data

aggregation with minimum delay under various wireless interference models.

Also, proposed an efficient and effective real-time scheduling protocol, which

answer every job of each query task within a relative delay under resource

constraints. This is done by addressing the tightly coupled tasks like routing,

transmission plan constructions, node activity scheduling, and packet

scheduling.

Peng et al (2012) presented a novel sleep scheduling method to

reduce the delay of alarm broadcasting from any sensor node in the WSNs.

The method has two determined traffic paths for the transmission of alarm

message, and level-by-level offset based wake-up pattern according to the

paths. If critical event occurs, an alarm is transmitted to the center node

quickly, and is broadcasted along another path without collision.

Xu et al (2012) elaborated on Wireless Sensor and Robot Networks

(WSRNs) from two unique standpoints. The two standpoints are robot task

allocation and robot task fulfillment. The robot task allocation deals with

robots cooperatively deciding on the set of tasks to be individually carried out

to achieve the desired set of goals. The robot task fulfillment enables the

robots to fulfill the assigned tasks through intelligent mobility scheduling.

Ing-Ray et al (2012) analyzed the reliability of a homogeneous

WSN executing a distributed code attestation protocol with neighbor sensor

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nodes serving as code verifiers. The tradeoff between energy exhaustion and

security vulnerability for causing sensor node failures is considered, and it is

identified how often distributed code attestation should be performed as well

as how many neighbor sensors should serve as code verifiers per attestation

event to maximize the systems lifetime without compromising on the

performance. Also, the sensitivity analysis of the results with respect to the

critical model parameters is presented with physical interpretations.

Yunhuai et al (2012) proposed a probabilistic network model using

the traditional topology control algorithm to improve the energy-efficiency

and increase the communication capacity of the WSNS. The network

connectivity using network reachability is measured and defined as the

minimal of the upper limit of the end-to-end delivery ratio between any pair

of nodes in the network. Also an attempt has been made to find the minimal

transmission power for each node, while the network reachability is above a

given application-specified threshold.

3.2.1 Node Placement in WSN

Node placement is an imperative task in WSNs and is a multi-

objective combinatorial problem. The WSN design is deployed in terms of

active sensor nodes placement, clustering and communication range of the

sensors nodes. As node placement is a multi-objective problem many

evolutionary algorithm based design have been developed.

Tilaky et al (2002) studied the effect of the infrastructure

performance of a sensor network for a number of network protocols like

Dynamic Source Routing (DSR), Direct Sequence Distance Vector (DSDV),

and AODV and delivery methods that are phenomenon-driven and

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continuous. The outcome of the work showed that no appreciable differences

exist between grid-type deployment and random deployment.

Ishizuka et al (2004) evaluated the tolerance against both random

failure and battery exhaustion from the viewpoint of stochastic node

placement. This has three types of stochastic sensor placement named simple

diffusion, constant placement and R-Random placement. These placements

along with grid placement are the placements to evaluate the performance

congestion control algorithms.

Zhang et al (2006) studied the problem of determining the critical

node density for maintaining k-coverage of a given square region with three

different deployment strategies named poisson point process, uniform random

distribution and grid deployment. In order to achieve the same level of

coverage degree grid deployment requires less node density than the two

random deployments strategies.

Gun et al (2007) proposed to deploy sensors either with variable

battery capacities or with non-uniform densities in order to counterbalance the

non-uniform energy drainage, thus achieving a longer network lifetime. The

non-uniform approach entails dividing the monitored region into concentric

ring areas and deploying nodes in these areas such that the highest battery

resources are allocated to the ring where the highest energy drainage takes

place. This idea involves a considerable effort in changing the placement of

nodes and is not deemed feasible.

It is evident from the analysis of congestion types, that the

infrastructure in terms of the sensor capabilities, number of sensors, and

deployment strategy plays a significant role in determining the performance

of the network (Younis et al 2008).

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Sang et al (2012) proposed a distributed cluster head selection

algorithm that takes into account the distances from sensors to a base station

that optimally balances the energy consumption among the sensors. Network

Simulator 2 (NS 2) is used for the simulation and simulations show that the

proposed scheme outperforms existing algorithms in terms of the average

sensor node lifespan and the time to first node death.

Soumyadip et al (2012) proposed a Multi-objective Optimization

(MO) algorithm to efficiently schedule the nodes of a WSN and to achieve

maximum lifetime. Instead of traditional grid or uniform coverage, the

algorithm focused on differentiated or probabilistic coverage where different

regions require different levels of sensing. The MO algorithm helps to attain

better tradeoff among energy consumption, lifetime and coverage. The

algorithm is run to reschedule the network every time a node failure occurs

due to power failure.

Liang et al (2012) studied the coverage problem of WSNs for the

rolling terrains, and derive the expected coverage ratios under the stochastic

sensors deployment. According to different terrain features, two kinds of

terrain coverage problems are investigated namely: the regular terrain

coverage problem and the irregular terrain coverage problem. Also proposed a

Digital Elevation Model (DEM) based method to calculate the expected

coverage ratio and design an algorithm to estimate the expected coverage ratio

of an interested region by using only the contour map of the region.

Guoren et al (2012) investigated the processes of reverse skyline

queries energy efficiency in WSNs. The properties of reverse skyline query is

theoretically analyzed and proposed a skyband-based approach to tackle the

problem of reverse skyline query answering over WSNs. Also an energy-

efficient approach is proposed to minimize the communication cost among

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sensor nodes of evaluating range reverse skyline query. An optimization

mechanism is discussed to improve the performance of multiple reverse

skylines.

Xingbo et al (2012) presented a target tracking approach which

avoids the instability problem and offers superior tracking performances. An

improved noise model is proposed to incorporate both the additive noise and

multiplicative noises in distance sensing. Then a Maximum Likelihood

Estimator (MLE) for prelocalization to remove the sensing nonlinearity

before applying a standard Kalman Filter (KF) is used. The advantages of the

proposed approach are demonstrated via experimental and simulation results.

Marcello et al (2012) proposed a framework for the assessment

of WSNs based on the automated generation of analytical models. The

framework hides modeling details, and allows designers to focus on

simulation results to drive the design choices. Models are generated

starting from a high-level specification of the system and by a preliminary

characterization of its fault-free behavior, using behavioral simulators.

Tapiwa et al (2012) presented a distributed topology control

technique that enhances energy efficiency and reduces radio interference in

WSNs. Each node in the network makes local decisions about its transmission

power and the culmination of these local decisions produces a network

topology that preserves global connectivity. The topology control technique is

the novel Smart Boundary Yao Gabriel Graph (SBYaoGG) and optimizations

to ensure that all links in the network are symmetric and energy efficient.

Guang et al (2012) investigated the problem of decomposing a

given network into a minimum number of Greedily Routable Components

(GRCs), where greedy routing is guaranteed to work. The approach is

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considered as an approximate version of the problem in a continuous domain,

with a central concept called the Greedily Routable Region (GRR). The

characterization of GRR is considered by its geometric properties and routing

capability.

Anahit et al (2012) presented the performance evaluation of an

algorithm for preserving temporal relationships of events in Wireless Sensor

Actor Networks (WSANs). The algorithm consists of two modules, which

deal with the problems of temporal event ordering and time synchronization.

The two problems are approached as a whole as they complement each other.

The goal of the event ordering algorithm for WSANs is to reduce the

overhead in terms of energy dissipation and delay. A tunable time

synchronization algorithm employing a hybrid synchronization scheme suited

for clustered topology is also proposed. The algorithm utilizes the message

exchange necessary for event ordering and routing for synchronization

purposes by piggybacking messages with synchronization pulses and replies

to reduce the communication cost of synchronization.

Hanjiang et al (2012) presented an innovative solution for ship

intrusion detection. The system is equipped with three-axis accelerometer

sensors, and an experimental WSN is deployed on the sea‟s surface to detect

ships. Using signal processing techniques and cooperative signal processing,

any passing ships by distinguishing the ship-generated waves from the ocean

waves are detected. Evaluations are provided on real data collection and

performed quantitative analysis of the detection system.

Mohamed et al (2012) defined a hotspot phenomenon that causes

an obvious inconsistency in the network traffic pattern due to the large

volume of packets originating from a small area. A realistic adversary model

assuming that the adversary can monitor the network traffic in multiple areas,

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rather than the entire network or only on one area is developed. The model

introduces a novel attack called Hotspot-Locating where the adversary uses

traffic analysis techniques to locate hotspots. A cloud-based scheme for

efficiently protecting source nodes location privacy against Hotspot-Locating

attack by creating a cloud with an irregular shape of fake traffic, to counteract

the inconsistency in the traffic pattern and camouflage the source node in the

nodes forming the cloud is also proposed.

Yaxiong et al (2012) proposed a sleep-scheduling technique called

Virtual Backbone Scheduling (VBS). VBS is designed for WSNs which has

redundant sensor nodes. VBS forms multiple overlapped backbones which

work alternatively to prolong the network lifetime. In VBS, traffic is only

forwarded by backbone sensor nodes, and the rest of the sensor nodes turn off

their radios to save energy. The rotation of multiple backbones makes sure

that the energy consumption of all sensor nodes is balanced, which fully

utilizes the energy and achieves a longer network lifetime compared to the

existing techniques. The scheduling problem of VBS is formulated as a

Maximum Lifetime Backbone Scheduling (MLBS) problem.

Muhammad et al (2012) proposed two advancements in the existing

bloom filter based traceback schemes for WSNs. The two advancements are

the support of directed queries, and a way of reducing the false positive rate at

the nodes nearer to the sink. The simulation analysis shows that the proposed

mechanism can efficiently traceback the packets with very few false positives.

Chia-Hung et al (2012) observed many monitoring applications for

WSNs that adopted Path-Connected-Cluster (PCC) topology, in which the

regions to be monitored are deployed with clusters of sensor nodes. The

clusters might be physically separated; paths of sensor nodes are used to

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connect them together. The work also showed that the address assignment

scheme defined by ZigBee will perform poorly in terms of address utilization.

Ravi et al (2012) described a computationally efficient secure

localization algorithm that withstands such attacks. The proposed algorithm

combines the iterative gradient descent with selective pruning of inconsistent

measurements, to achieve high localization accuracy. The proposed secure

localization algorithm can also be used in mobile sensor networks, where all

nodes are moving, to estimate the relative locations of the nodes without

relying on the anchor nodes. The analysis shows that the proposed algorithm

can find the relative location map of the entire mobile sensor network even

when some nodes are compromised and transmit false information.

3.2.2 Cross-Layer Protocol in WSN

Severe energy constraints of battery-powered wireless sensor nodes

require energy-efficient communication protocols, in order to accomplish the

application objectives of WSNs. The greater part of the accessible solutions is

based on the traditional layered protocols approach. It is much more resource-

efficient to have a combined system, which reduces the frequent protocol

layer functionalities into a cross-layer unit for resource-constrained wireless

sensor nodes.

Wang et al (2003) surveyed the existing cross-layer signaling

methods, which most closely correspond to the explicit interfaces

architecture. Also, they proposed a Cross-LAyer Signaling Shortcuts

(CLASS) architecture that allows the propagation of cross-layer messages

through out-of-band signaling.

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Sichitiu (2004) proposed a cross-layer TDMA-based protocol that

guarantees collision-free communication, by scheduling slots for each node,

and produces significant energy savings. This is the main challenge to

determine the collision-free slots that are to be assigned to wireless nodes in a

multiple-hop network.

Srivastava et al (2005) presented a survey on cross-layer design. A

simplified version of IP or a new networking architecture and the location of

the node is more important than its identifier, creating the need for service and

data location protocols that use geographic information to identify the most

suitable data source. Further, the need for extremely low power consumption

frequently leads to cross-layer approaches or to radically new networking

architecture.

Winter et al (2006) proposed a cross-layer architecture for mobile

ad hoc networks that aims to provide a global view of the network to

individual nodes. Cross talk disseminates information over a full path,

accumulating information about sensor nodes along the way. Information is

provided to all protocols in the stack. Even though cross talk provides only

pieces of information along data paths, the network view is above 95 %

accurate.

Akyildiz et al (2006) presented a cross-layer protocol for WSNs.

The location of the node is more important than its identifier, creating the

need for service and data location protocols that use geographic information

to identify the most suitable data source. Further, the need for extremely low

power consumption frequently leads to cross-layer approaches or to radically

new networking architectures.

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Shuqiang et al (2008) presented a routing protocol called cross-

layer AODV, based on a cross-layer design and ad hoc on-demand distance

vector routing protocol. The proposed protocol adopts two mechanisms called

delaying transmission and efficient broadcasting to address the broadcast

storm problems in WSNs such as high probability of collisions and

redundancy of broadcasting.

Jun et al (2009) proposed an Energy-Balancing, Cross Layer Data

Gathering (EBCLDG) protocol for WSNs. This is used for monitoring and

periodic reporting applications and the network is organized into concentric

tiers around the sink. The energy-balancing algorithm is employed to mitigate

the hot spot problems. The MAC and routing of networks are integrated to

minimize the protocol overhead.

Morteza et al (2012) developed a parallelized distributed algorithm

which scales well in the network size and exhibits low computational

complexity. An online implementation is also proposed and tested. The

numerical examples verify that the novel design and highlight the

performance advantage over state-of-the-art alternatives.

3.2.3 Energy Consumption in WSN

When the environmental circumstances are stable, a WSN

application can sense and develop very related or regular data values for

lengthy durations. The important behavior of WSN nodes is to exploit the

power consumption to a minimum. The lifetime, scalability, response time

and effective sampling frequency are important parameters of WSNs, and are

closely related to essential resource constraint. This is related to power

consumption and is a very hard problem to satisfy.

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Intanagonwiwat et al (2000) presented that directed diffusion is

data-centric, in that all communication is for named data. All nodes in a

directed diffusion-based WSN are application-aware. This enables diffusion

to achieve energy savings by selecting empirically good paths and by caching

and processing data in-network.

Thomas et al (2006) presented a two-tier WSN and address the

network lifetime problem for upper-tier aggregation and forwarding nodes.

Existing routing solutions require aggregation and forwarding nodes to split

flows to different paths during transmission, which is called multisession flow

routing solutions. A multisession flow routing solution requires a packet-level

power control at the aggregation and forwarding nodes so as to conserve

energy. To achieve optimal network lifetime by power control on a much

larger timescale with the so-called single-session flow routing solutions,

under which the packet-level power control and strict requirements on

synchronization are not necessary.

Tongying et al (2009) proposed that information fusion is an

effective way to reduce the communication data, and to save the energy

consumption and thereby extend the life of the WSN. This is done with the

specific analysis of an information fusion algorithm based on rough set.

Fengyuan et al (2011) designed an Energy-Balanced Routing

Protocol (EBRP) by constructing a mixed virtual potential field in terms of

depth, energy density and residual energy. The goal of this approach is to

force packets to move towards the sink through the dense energy area so as to

protect the nodes with relatively low residual energy. The integrated

performance of the full potential-based energy-balanced routing algorithm is

evaluated through numerous simulations in a random deployed network

running event-driven applications.

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Kazi Ashrafuzzaman (2012) explored the boundaries of optimal

operations pertaining to maximum throughput and minimum energy

consumption for WSNs that adopt low-power slotted Carrier Sense Multiple

Access/Collision Avoidance (CSMA/CA) as the medium access control

scheme. A non-trivial analysis of energy consumption is performed in the

process referring to existing model frameworks that are shown to be

equivalent. Optimal points of operations are mapped onto backoff parameters

to help wastes of bandwidth due to heightened collisions during periods of

traffic intensity and prolong network lifetime by properly reducing attempts to

send sensed data for energy conservation during uneventful periods.

Imad et al (2012) proposed a routing method for WSNs to extend

network lifetime using a combination of a fuzzy approach and an A-star

algorithm. The proposal is to determine an optimal routing path from the

source to the destination by favoring the highest remaining battery power,

minimum number of hops, and minimum traffic loads. For the effectiveness

of proposed method in terms of balancing energy consumption and

maximization of network lifetime, A-star search algorithm and fuzzy

approach is compared using the same routing criteria in two different

topographical areas.

Ritwik et al (2012) presented application of WSNs in distributed

generation. In order to have a reliable communication with minimal end-to-

end delay during the event of next hop node failure, a Find Reliable Link

(FRL) scheme is proposed. Power sharing is improved significantly in a

conventional decentralized power control by correcting the distributed

generators reference signals with WSNs. Even a low bandwidth

communication among the distributed generators and control center can

overcome the system operation challenges posed by the network line

parameters, failure of distributed generators and power shortage in the system.

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3.2.4 Multipath Routing in WSN

Multipath routing can decrease the requirement for route updates,

balance the traffic load and increase the data transfer rate of WSNs,

improving the consumption of the limited energy to sensor nodes. Traditional

methods use flooding for route discovery, and transmit data with maximum

power even when not needed, which leads to a waste of energy. Using

multipath in WSNs can decrease regular routing update, and improve the data

transmission rates. Moreover, multipath routing provides an even distribution

of traffic load over the network and gives great benefit to balancing the

energy consumption for extending the network‟s lifetime.

Xiuli et al (2005) proposed a multipath disjoint routing algorithm

and compared it with tow on-demand protocols, DSR and AODV. The

proposed algorithm improves the packet delivery ratio, saves power and

reduces the end-to-end delay. The system does not bring paralysis because of

the routing breakdown.

Muhammad et al (2008) described a multipath routing protocol in

WSNs to achieve higher reliability and load balancing. This is an efficient

scheme to control multipath congestion, so that the sink can get priority based

throughput for heterogeneous data. The packet service ratio is used for

detecting congestion as well as performed hop-by-hop multipath congestion

control based on that metric.

Thirumal et al (2009) proposed an Adaptive Topological

Knowledge (ATK) algorithm, used periodically and locally to determine the

topological knowledge range of each solar-powered node, based on an

estimated energy budget. The multipath routing scheme is used for route

selection based on the round-robin model. Energy optimization is attained

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during real-time route discovery and packet forwarding, by reducing

congestion in the network.

Kuang et al (2009) observed a multipath based on the cache

notification algorithm in WSNs. Multipath is a primary method for

guaranteeing transmission reliability in WSNs. This aims at node congestion,

and performance is evaluated with reliability and throughput.

Mande et al (2010) proposed a multipath routing algorithm for

wireless multimedia sensor networks. In this, a set of node-disjoint routing

paths is found by a mazing search algorithm, to reduce the number of energy

consumption gathering nodes, and high risk nodes of network congestion.

Multipath selection employs a type of congestion control message, a gradual

increase strategy based on a path, and a gradual increase strategy based on a

flow, to balance the load and energy.

3.3 Congestion Control

Congestion can occur in WSNs due to several reasons; interference

between concurrent data transmissions, the addition or removal of sensor

nodes in the network, or bursts of messages because of the occurrence of

some events (Yu et al 2006). Congestion in the network can lead to two

serious outcomes. As congestion spreads, buffer drops will increase quickly,

and become the dominant reason for packet loss. Significant delay can also be

observed when congestion occurs. Another consequence of congestion is the

growing expenditure of resources per packet. Fewer packets can be

transmitted with the same amount of energy as before. Thus, alleviating

congestion can be helpful in achieving reliable data delivery. The design and

implementation of a congestion control protocol is challenging in the WSN

domain due to the following reasons.

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i. Firstly, the wireless channel itself is lossy and uncertain, which

makes distributed data flow control a challenge.

ii. Secondly, contention for the wireless channel can be observed at

both the sender and receiver sides.

iii. Finally, it is difficult to optimize channel utilization and fairness at

the same time.

For sensor networks with a single sink node, mitigating congestion

is mainly done by employing passive approaches. Rate control is the most

commonly-used method (Wang et al 2006).

Wan et al (2002) presented a reliable transport protocol suitable for

reliable data applications called PSFQ. This takes a different approach and

supports a simple, robust and scalable transport, that is customizable to meet

the needs of different reliable data applications. The combination of

COngestion Detection and Avoidance (CODA) and PSFQ may achieve both

congestion control and reliability.

Again, Wan et al (2003) proposed an energy efficient congestion

control scheme called CODA. This detects the receiver congestion and is

based on the queue length, as well as wireless channel load at intermediate

nodes. The approach also uses explicit congestion notification, and an AIMD

rate adjustment technique.

Chieh-Yih et al (2003) described an energy efficient congestion

control scheme for sensor networks called CODA with three mechanisms,

namely, receiver-based congestion detection, open-loop hop-by-hop

backpressure, and closed-loop multi-source regulation. CODA was

implemented and analyzed with the performance metrics, energy tax and

fidelity penalty on sensing applications.

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Rajendran et al (2003) proposed TRaffic Adaptive Medium Access

(TRAMA) Protocol that organizes time into frames and uses a distributed

election scheme based on traffic information at each node to determine which

node can transmit at a particular slot. TRAMA uses a distributed hash

function to determine a collision-free slot assignment and builds a scheduling

scheme when a node has data to send. This random scheduling scheme

increases the queuing delays.

Grieco (2004) focused on two rate-based congestion control

algorithm that has been proposed for streaming flows over the Internet. The

two rate-based algorithms are the TCP-Friendly Rate Control (TFRC) and the

Adaptive Rate Control (ARC). Metrics like efficiency, fairness and

friendliness of ARC and TFRC algorithms have been evaluated using

Network Simulator 2 (NS 2). ARC has remarkable goodput, fairness and

friendliness compared with TFRC.

Bret et al (2004) examined three techniques to improve congestion

control in the wired networks. The three techniques are hop-by-hop flow

control, rate limiting source traffic when transit traffic and a prioritized

medium access control protocol.

Cheng et al (2004) proposed a distributed and scalable algorithm

that eliminates congestion within a sensor network, and ensures the fair

delivery of packets to a central node or a base station. This routing structures

often results in the sensors closer to the base station experiencing congestion,

which inevitably cause packets originating from sensors to have a higher

probability of being dropped.

Kyriakos et al (2005) presented COngestion control for MUlti-class

Traffic (COMUT), a distributed cluster-based mechanism for supporting

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multiple classes of traffic in sensor networks. COMUT is based on the self-

organization of the network into clusters each of which autonomously and

proactively monitors congestion within its localized scope. The clusters then

exchange appropriate information to facilitate system wide rate control.

Chonggang et al (2006) proposed a node priority-based congestion

control protocol for WSNs. In this, the node priority index is introduced to

reflect the importance of each node and uses packet inter-arrival time along

with packet service time to measure a parameter defined as congestion degree

and imposes hop-by-hop control based measurement as well as node priority

index.

Wang et al (2007) proposed a novel upstream congestion control

protocol for WSNs named PCCP, which introduced node priority index to

reflect the importance of each sensor node. This utilizes a cross-layer

optimization and imposes a hop-by-hop approach to control congestion.

Mohammad et al (2008) presented a new Queue based Congestion

Control Protocol (QCCP) with priority support, using the queue length as an

indication of congestion degree. In this approach, the rate assignment to each

traffic source is based on its priority index as well as its current congestion

degree.

Liqiang et al (2009) proposed an energy efficient congestion

control scheme for sensor networks called Enhanced Congestion Detection

and Avoidance which comprises of three mechanisms. First, the approach

uses buffer and weighted buffer difference for congestion detection.

Secondly, proposed a bottleneck-node-based source data sending rate control

scheme and finally uses a flexible queue scheduler for packets transfer.

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Young et al (2009) suggested an adaptive rate control for

congestion avoidance in Wireless Body Area Networks (WBANs). The

scheme performs rate control dynamically each node based on a predication

model which uses rate function including congestion risk degree and

valuation function, without requiring congestion detection and congestion

notification steps.

Again, Young et al (2009) suggested a self-adjustable rate control

in WBANs. The scheme performs rate control dynamically each node based

on a predication model which uses rate function including congestion risk

degree and valuation function, without requiring congestion detection and

congestion notification steps.

Vasos et al (2009) examined the performance of Sensor

Transmission Control Protocol (SenTCP), Directed Diffusion and

Hierarchical Tree Alternative Path (HTAP), with respect to their ability to

maintain low delays, to support the required data rates and to minimize packet

losses under different topologies. The topologies used as simple diffusion,

constant placement, R-random placement and grid placement. The congestion

control performance in sensor networks can significantly be improved to

forward the data in case of congestion.

Guangxue et al (2009) proposed an Upstream Hop-by-hop

Congestion Control (UHCC) protocol based on cross-layer design. Unlike

existing congestion control protocol, the UHCC protocol takes advantage of

unoccupied buffer size and traffic rate at MAC layer of each node as

congestion level indication, based on which, every upstream traffic rate is

adjusted with its node priority to mitigate congestion hop-by-hop.

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Swastik et al (2010) proposed a distributed congestion control

algorithm for tree based communications in WSNs that seeks to adaptively

assign a fair and efficient transmission rate to each node. Each node monitors

its aggregate output and input traffic rates. The working of congestion control

is independent of the underlying routing algorithm and is designed to adapt

the changes in the underlying routing topology.

Wang et al (2011) proposed a novel network cognition and

congestion control model based on Neuroendocrine-immune system that

introduced natural inspired computation concept and is designed to improve

the overall transmission performance of heterogeneous network.

Seshadri et al (2011) described a router-assisted congestion control

protocol called Quick Flow Control Protocol (QFCP). The QFCP can

significantly shorten the flow completion time, fairly allocate bandwidth

resource, and is robust to non-congestion related losses. Also an extensive

study on quality of service for real time multimedia application is made and

results showed an improved QoS for specified TCP based scenarios.

3.3.1 Congestion Detection

When congestion is detected in the network, sensor nodes limit

their reporting rate, and given an opportunity to the congested nodes to drain

their queue. Many researchers have studied such rate control methods, and

focused on how to dynamically adjust the reporting rate in the context of

various congestion situations. Another method that can be used to alleviate

congestion is packet dropping. When the receiver node has already used up all

the buffer space due to congestion, it clearly has to drop either the newly-

arrived packet or an old one. In this case, evaluating the importance of

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different packets becomes important, as this can help a node to make better

dropping decisions to avoid wasting resources.

Two fundamental methods have been proposed so far to detect

congestion in sensor networks. Based on the observation that congestion can

result in excessive queueing, the first method is to compare the instantaneous

buffer occupancy with a certain watermark. If the water mark is exceeded, a

congestion state is diagnosed. This method is simple to implement. However,

its accuracy is questionable, especially when packets are already lost on the

channel. Another way to detect congestion is through channel sampling. As

used in CODA (Wan et al 2003), when a packet is waiting to be sent, the

sensor node samples the state of the channel at a fixed interval. Based on the

number of times the channel is sensed busy, a utilization factor can be

calculated to deduce the congestion level of the network.

In WSNs, the sink is normally considered to have unlimited

resources, and is able to have a more extensive view of the network behavior

than a normal sensor node. Thus, in some protocols such as Rate Controlled

Reliable Transport (RCRT) (Peak et al 2007), the sink makes all the

congestion detection and rate allocation decisions.

3.3.2 Congestion Notification

When network congestion is detected, the congestion notification

information needs to be conveyed from the congested nodes to their

neighbors, or to the source nodes, or destination nodes. The method for the

delivery of notification information should be carefully designed, since

sending new messages into an already congested network could only

aggravate the situation. The congestion information can be sent in the form of

a CN bit in the packet header or in a more comprehensive format, that

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includes the congestion degree or allowable data rate. The congestion

information can be sent in an explicit control message to notify the relevant

nodes. It can also be sent in an implicit way by including control information

in a regular data packet. For example, in ESRT (Sankarasubramaniam et al

2003), when congestion is detected, the sensor node sets a CN bit in the

header of the packet being forwarded. By checking the header of an incoming

packet, the receiver (sink) can learn the congestion status of the network.

3.3.3 Rate Adjustment

A straightforward way of alleviating congestion is to simply stop

sending packets into the network, or to send them at a lower rate. The rate

adjustment decision can be made by the congested nodes themselves, by a

node outside the congested area (sink node), or by a predetermined policy.

When a single CN bit is used to notify congestion, one option is for the nodes

to adjust their sending rate according to an Additive Increase Multiplicative

Decrease (AIMD) scheme, as in RCRT (Peak et al 2007). On the other hand,

if additional congestion information is provided such as congestion degree or

allowable data rate, the nodes can implement a more accurate rate adjustment

as in Congestion Control and Fairness (CCF) (Ee et al 2004).

3.4 Multi-Agent Systems

A multi-agent system provides a domain independent

componentized and reusable substratum, to allow heterogeneous agents to co-

ordinate in a variety of ways, and to enable a single agent to be part of a

multi-agent infrastructure. Agents act as wrappers around the incompatible

systems, and use this communication and coordination infrastructure to grant

an abstraction layer for exchanging data and messages.

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Macedonia et al (1994) presented the ability to attach a viewing

screen to an entity in the simulation, this give the viewpoint from the entity, in

the current user display. However, if one wants to view a simulation yet not

directly interact with any of the entities, then so-called “stealth” or “god”

viewers are commonly used by simulation managers, and give a third person

view into the simulation.

Brunett et al (1997) presented that the Dissemination (DIS)

Protocol Data Units (PDU) packets transmitted to other simulation nodes tend

to overwhelm a network bandwidth. Since, the DIS protocol works in

stateless manner, all information of each entities and events is transmitted to

other simulation nodes even though there is no status change in those entities

or events. There is a need of a “middle” agent between each distributed

simulation nodes so that its network traffic is reduced and the members of

military organizations are able to simulate a very large DIS network.

Sycara et al (2001) proposed a software agent generally defined as

an intelligent, collaborative software object that achieves a higher level of

artificial intelligence through a global communication structure involving

other software agents. MAS assume that no single agent can or does know all

of the information in its domain, and that only through collaboration with

other agents they can achieve overall knowledge.

Lewis et al (2002) presented a high quality 3-Dimensional (3D)

graphics that have been solely the domain of expensive UNIX workstations.

The new video cards provide features such as texture shading, dynamic

lighting, and many other computationally expensive 3D tasks. Also, presented

a Unreal Tournament Semi-Automated Force (UTSAF) based on the Unreal

Tournament (UT) engine generic modifications can easily be incorporated.

These include Cave like 3D projective displays and high fidelity robot

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simulations. The presentation of views from any point in the simulation

UTSAF provides a powerful training and testing tool ranging from

inexpensive immersive displays for ground forces to out-the-canopy views for

aircraft and other vehicles.

Zyda et al (2003) proposed mobile agents and smart networks for

distributed simulations. DIS PDUs transmitted between simulation nodes tend

to overwhelm network bandwidth. Since, the DIS protocol works in a

stateless manner, all information for each entity and event is transmitted to

other simulation nodes, even though there are no status changes in those

entities or events.

Cheung et al (2006) proposed performance enhancing proxy for

interactive 3 Generation (3G) network gaming. To improve the timely

delivery of network game data in 3G wireless networks is possible to use a

Performance Enhancing Proxy (PEP) called Wireless Interactive Network

Gaming proxy (WING). WING is located inside IP Multimedia Subsystem

(IMS) as an application service on top of the myriad of services that IMS

already provides. The Session Initiation Protocol (SIP-based IMS) provides a

multitude of multimedia services: from establishing connections from the

legacy telephone networks to the new IP core network using Voice over IP

(VoIP), to delivering streaming services such as video as a value-added

service to mobile users.

Qun et al (2006) presented a mobile agent approach to dynamic

architecture-based software adaptation. The adaptation in this approach is

verified and validated in the architectural level before to be applied on the

application. This approach is used by the adaptation takes advantage of both

meta-architecture and the mobile agents. This uses an architectural model to

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guarantee the safety of the reconfiguration, while using mobile agents to

automate the adaptation process in a flexible way.

Lasheng et al (2008) presented studies on hierarchical

reinforcement learning in multi-agent environment. Reinforcement learning is

quite often used to choose optimal action to achieve final goal. When the

reward of action is lag and the state transition and the immediate reward are

unknown, Q-reinforcement learning is very fit for the agent to estimate the lag

rewards about every state. According to reinforcement learning, the offer

should be considered as an action that the agent takes to transfer its own state

under center state.

Chouarfia et al (2009) proposed an architectural approach for the

dynamic adaptation of components-based software using multi agents system.

The dynamic adaptation independently of the nature of the system is to be

adapted and also an agent based system to perform the adaptation. The agent

system is an architectural description of the adapted application. The

adaptation mechanism is implemented in the connectors using the flexibility

offered by the java scripting programming technique.

3.4.1 Multi-Agent Architectural Approach

The multi agent uses a multi domain, MAS, in which each agent

performs a specific task and interacts with other agents through a standard

infrastructure to achieve interoperability between incompatible systems. The

MAS allows processing to be distributed among a group of agents with

preexisting infrastructure for communication and coordination.

Nagata et al (2001) proposed MAS for power system restoration.

The problem of predictive reconfiguration consists of various complex sub-

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problems, such as weapon damage assessment, pre-hit probabilistic

reconfiguration, failure assessment after weapon hit, determination of de-

energized loads, and determination of restoration actions, system analysis, and

load shedding. MAS allow simultaneous complex tasks to be performed in

real time. The basic approach behind MAS is to decompose a complex

problem into a number of sub-problems. Each sub-problem falls under the

responsibility of an agent.

Esauro et al (2002) presented pricing in agent economies using

multi-agent Q-learning. Reinforcement Learning (RL) is a way of teaching

agents (decision-makers) near-optimal control policies. This is accomplished

by assigning rewards and punishments for the actions based on the temporal

feedback information obtained during active interactions of the learning

agents with dynamic systems. The agent should choose actions that tend to

improve the measure of system performance. Such an incremental learning

procedure suitable for prediction and control problems was developed and is

referred to as Temporal-Difference (TD) methods.

Oliveira et al (2006) proposed a multi-agent based framework

model for supporting learning in adaptive automated negotiation. RL often

used to choose optimal action to achieve final goal by learning. The state

transition and the immediate reward are unknown for this method and the Q-

reinforcement learning is very fit for the agent to estimate the lag rewards

about every state.

Partalas et al (2007) proposed multi-agent RL using strategies and

voting. RL often used to choose optimal action to achieve final goal by

learning. When the reward of action is lag and the state transition and the

immediate reward are unknown, Q-reinforcement learning is very fit for the

agent to estimate the lag rewards about every state.

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Jin-gang et al (2009) presented multi-agent automated negotiation

based on RL in electronic commerce. This builds an open and dynamic

automated negotiation system model and imports

Q-learning into the negotiation to quicken the process of negotiation.

Compared with no learning mechanism in negotiation, the negotiation

efficiency of the model has been improved and the negotiation results are

standardized.

3.4.2 Dynamic Adaptation of Component-based Software using MAS

The recent research on agent technologies can serve the scopes of

the component based software systems. In a short time, the web has become

the dominant database for information retrieval. Due to the exponential

growth of the web and the information it provides, finding accurate

information is becoming more and more difficult. In the near future, access to

the web will be mediated by intelligent applications, and MAS, that will assist

users in finding accurate information and complete transactions successfully.

Concisely, the semantic web means ontologies and semantics, software agents

means MAS, and other agent platforms with their own standards and web

services mean eXtensible Markup Language (XML). In this section an

attempt is made to investigate the way these technologies can cooperate

without changing their specifications.

Ludwig et al (2000) presented the retransmission ambiguity

requires extra information in ACKnowledgements (ACKs) that the sender can

use to unambiguously distinguish an ACK for the original transmission of a

segment from that of a retransmission. The sender stores the timestamp value,

the current congestion-window-size, and the slow-start threshold. When using

the timestamp option the TCP sender writes the current value of a “time

stamp clock” into the header of each outgoing segment.

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Langley et al (2001) presented discovery of infrastructure in MAS.

Agents schemes that represent physical robot capabilities help determine role

assignments of the robots in a variety of heterogeneous team-coordination

tasks. A location discovery infrastructure, e.g. local and hierarchical agent

name service, local and wide area discovery, and viral agent-to-agent

community formation services provide agents with a variety of ways to find

each other in different network topologies and administrative domains.

Saverio et al (2001) presented TCP Westwood which provides

bandwidth estimation for enhanced transport over wireless links. TCP

Westwood is also a sender-side modification of TCP Reno. This tries to avoid

the drastic reduction in the transmission rate caused by random link errors.

This computes an end-to-end bandwidth estimation by monitoring the rate of

returning ACKs, and uses estimation to compute the slow start threshold and

congestion window after a congestion indication. Adjusting the window to the

estimated available bandwidth makes TCP Westwood more robust to wireless

losses, as the transmission rate is not reduced to half, but is adapted to the

most recent bandwidth estimation instead.

Dutta et al (2001) proposed an active proxy based architecture for

TCP in heterogeneous variable bandwidth networks. The solution proposes

use of PEP at the edges of unfriendly networks, in order to control the TCP

flows passing through them. The idea is that high link delays make TCP flows

less responsive to bandwidth variations. PEPs are in charge of monitoring the

available bandwidth to the flows, and manipulating the ACKs to speed up or

slow down the TCP senders. PEP can send a premature ACK in order to

simulate a lower RTT and make the TCP sender respond faster. PEPs must

also recover any lost packets in the path between themselves and the

destinations, thus preventing the sender from invoking congestion avoidance

algorithms in the presence of random losses.

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Chandran et al (2001) presented a feedback-based scheme for

improving TCP performance in ad hoc wireless networks. The TCP

performance over a wireless ad-hoc network can be enhanced if intermediate

nodes can send explicit link failure notifications to the TCP sender.

Collectively, these performance enhancement mechanisms are called TCP

PEP. This refers to the category of techniques that intermediate nodes in the

network interact with the TCP layer and influence its end-to-end behavior.

Annoni et at (2002) proposed interworking between Multilayer

Internet Protocol (ML-IPsec) and secure multicast services over Gene

Expression Omnibus (GEO) satellites. To solve the problem is based on the

layering architecture for network security protocols. The approach may have the

right mix to provide both security and extensibility in one unified platform.

Certainly, that through protocol design and system implementation ML-IPsec

can easily be added to an existing IPsec system and that its overhead is low. ML-

IPsec has achieved the goal of granting trusted intermediate routers a secure,

controlled, and limited access to selected portions of IP datagram‟s, while

preserving the end-to-end security protection to user data. Currently, ML-IPsec

approach is being adopted in several all-IP satellite networks.

Kolding et al (2002) presented performance aspects of Wideband

Code Division Multiple Access (WCDMA) systems with High Speed

Downlink Packet Access (HSDPA). In Adaptive Modulation Coding (AMC),

the base station determines the Modulation and Coding Scheme (MCS) to use

towards a particular user based on power measurements, network load,

available resources and Channel Quality Indications (CQI). This periodically

sent by the user equipment and they reflect the MCS level that the terminal is

able to support under the current channel conditions. This adaptive

modulation control can cover a range of variation of 20 dB and can be further

expanded through the use of multi-coding.

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Dottling et al (2002) proposed Hybrid Automatic Repeat Request

(H-ARQ) and adaptive modulation and coding schemes for high speed

downlink packet access. Transmitting at the highest rates to improve spectral

efficiency involves a significant increase in the block error rate. There is a

need for an advanced link layer mechanism to reduce the delay introduced by

multiple retransmissions of corrupted packets, and to increase the

retransmission efficiency. The retransmission scheme used is H-ARQ, which

combines forward error correcting with a Stop-And-Wait (SAW) protocol.

Ren et al (2003) presented TCP startup performance in large

bandwidth delay networks the performance of TCP on connection startup is

analyzed along with the importance of setting an adequate slow start

threshold. Too low thresholds relative to the Bandwidth Delay Product (BDP)

cause a premature exit of the slow start phase, which reduces the growth of

the sending rate and leads to poor startup utilization. On the contrary, too high

thresholds might cause many packet drops and retransmissions. There are

many proposed solutions to choose an adequate threshold, like fast start,

which uses cached values of the threshold.

Schwamb et al (2003) proposed working with Modular Semi-

Automated Forces (ModSAF), an interface for programs and users. An

interface for intelligent agents to ModSAF simulation, the interface provides

intelligence to ModSAF to simulate a human behavior using a soar agent. The

soar agent models a human behavior in a cockpit in an air-to-air combat

simulated by ModSAF. The interface and soar are integrated to ModSAF into

a single process. This requires a modification to other ModSAF nodes and

also this does not provide multi-agent environments to support heterogeneous

intelligence.

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Chemiakina et al (2004) presented radio network based adaptation

method for packet switched services over 3G mobile systems. To design,

analyze and simulate a proxy-based solution, called Radio Network Feedback

(RNF), to improve the performance of TCP over 3G cellular networks. This

improve the resource utilization and maximize the transmission rates while

maintaining the shortest response time possible, by taking advantage of

feedback information provided by the network.

Moller et al (2005) proposed using RNF to improve TCP

performance over cellular networks. A proxy-based scheme can improve both

the user experience of wireless Internet, and the utilization of existing

infrastructure. To give general idea of the scheme is possible to contemplate

the presence of one proxy that resides between the Internet and the cellular

system. With an appropriate custom protocol between the RNC of the cellular

system and the proxy, the data-link layer within the RNC provides

information to the transport layer.

Huang et al (2006) presents runtime recovery and manipulation of

software architecture of component-based systems. An approach to recover

software architecture from component based systems at runtime and changing

the runtime systems via manipulating the recovered software architecture. As

soon as software architecture is recovered, the runtime system can be

observed, reasoned and adapted through its architecture views. The approach

supports the addition, deletion and replacement of the components and

connectors.

3.4.3 RETSINA Multi-Agent System

Agents in a multi-agent system can get the advantage of the group

effort of additional agents in the system. RETSINA is a multi-agent

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communications system, that is intended to explain the problem of integration

of information from distributed heterogeneous information sources, and make

use of the results efficiently in decision support, and in an open and dynamic

environment.

Rectenwald (2003) presented RETSINA as an example software

agent test bed and development system. RETSINA is a mature MAS which

has been at the core of many successful agent-based projects. GameBots is a

modification of the UT video game, that allows control of game players

through a normal TCP/IP socket.

3.5 Research Gap

From the literature various research contributions has been made in

the WSN. Among this congestion control is an important area. Several

congestion control protocols have been proposed for WSNS (Wang et al

2006). Earlier most of the research was concentrated on downstream

congestion control. But currently the demand for uploading the content is

more than the downloading needs. Because of this the more congestion will

be created along the upstream path. So it is necessary to provide a better

congestion control mechanism over the upstream path. The congestion in

WSN not only creates packet loss but also leads to excessive power

consumption, and this will reduce the lifetime of the system. Also the

importance to provide fairness and better QoS for multimedia applications is

lagging. The CCF protocol (Ee et al 2004) uses packet service time to reduce

the available service rate and detects congestion in the intermediate wireless

sensor nodes. Congestion information like the packet service time in CCF is

implicitly indicated and CCF controls congestion in a hop-by-hop manner.

Each node uses exact rate adjustment based on the available service rate and

child node number. But the rate adjustment in CCF relies only on packet

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service time that will lead to low utilization when some sensor nodes will not

have enough traffic or proper packet error rate.

3.6 Inferences from Literature Survey

The WSN adapts to situations that differ from the one originally

designed for, in which the TCP is prepared to work in MAS with reasonably

low delays and with low link error rates. The WSN considers a loss indication

as a sign of congestion, and takes action accordingly. However, there is an

increasing number of situations, where this assumption is no longer valid.

The protocols are unable to distinguish a loss due to congestion,

where decreasing the sending rate is necessary to alleviate the congested link

from a random loss, and reducing the rate is not only useless, but,

counterproductive as well.

Such a situation causes the sender protocol to cut the sending rate

by half repeatedly leading to a serious degradation of its performance. In this

work, by sharing the agent capacity among all the users in a particular cell,

the adaptability to significant bandwidth variations is achieved, to which the

standalone protocol is normally unable to adapt. To use a protocol without an

agent technology that contains the MAS link will decrease the average link

utilization, increase the latency of the connection, and in general, an overall

under-utilization of the often scarce network resources.

The aim of the research is to design, analyze and simulate a multi

agent-based solution to improve the performance of the WSN. The proposed

MAS solution is aimed to improve the resource utilization and maximize the

transmission rates, while maintaining the shortest response time possible, by

taking advantage of the feedback information provided by the network itself.

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This can add on significantly to the performance improvements brought about

by the released MAS, which provides broadband support for downlink

packet-based traffic.

This should also overcome the problems that TCP connections face

over agent links. To share the connection between a server and a mobile user

through the introduction of an agent, this terminates both the connections.

This is capable of adapting the parameters to get the available resources.

The TCP sender needs to infer in order to perform, that congestion

control is already known by the agent network. It can be transmitted to the

agent to feed its adaptation algorithm, as the case of the bandwidth available

for a determined connection or the network load. The MAS solution focuses

on the problems introduced by variable bandwidth and delay, agent based

link‟s utilization and sporadic disconnections of mobile terminals.

This assumes reasonably that all the possible link errors are

recovered by the link level protocols, and that the transmission is properly

dimensioned. However, WSN implementation in the agent retains the TCP

functionalities with good improvements, and thus, can work in adverse

situations also.