wireless sensor networks issues and applications - … · wireless sensor networks issues and...

Wireless Sensor Networks Issues and Applications

Rajkumar1, Vani B A2

, Kiran Jadhav3, Vidya S4

[email protected], [email protected], [email protected], v [email protected]

1, 2, 3, 4Sambhram Institute of Technology , Bangalore , Karnataka, India

Abstract: Wireless Sensor Networks have come to the

forefront of the scientific community recently. Current

WSNs typically communicate directly with a centralized

controller or satellite. On the other hand, a smart WSN

consists of a number o f sensors spread across a geographical

area; each sensor has wireless communication capability

and sufficient intelligence for signal processing and

networking of the data. The structures of WSNs are tightly

application-dependent, and many services are also dependent

on application semantics. Thus, there is no single typical

WSN application, and dependency on applications is higher

than in traditional distributed applications. The

application/middleware layer must provide functions that

create effective new capabilities for efficient extraction,

manipulation, transport, and representation of information

derived from sensor data. This paper provides a survey of

Wireless Sensor Networks Issues and Applications, where

the use of such sensor networks has been proposed.

Keywords : Wireless Sensor Network , Issues and Applications

I INTRODUCTION Wireless Sensor Networks have recently emerged as a

premier research topic. They have great long term economic

potential, ab ility to transform our lives, and pose many new

system-build ing challenges. Sensor networks also pose a

number of new conceptual and optimization problems, some

of these such as location, deployment, and tracking, are

fundamental issues, in that many applications rely on them for

needed information. Coverage in general, answers the

questions about quality of service (surveillance) that can be

provided by a particular sensor network. The integration of

multip le types of sensors such as seismic, acoustic, optical,

etc. in one network platform and the study of the overall

coverage of the system also presents several interesting

challenges.

With the refinement of energy harvesting techniques that can

gather useful energy from vibrations, blasts of radio energy,

and the like, self-powered circuitry is a very real possibility,

with networks of millions of nodes, deployed through

paintbrushes, injections, and aircraft. Also, the introduction of

an additional type of sensor nodes allowing the network to

self-organize and “learn”, by embedding s mart and adaptive

algorithms. On the other hand, the use of adaptive power

control in IP networks that utilizes reactive routing protocols

and sleep-mode operation, more powerful mobile agents, QoS

(Quality of Service) to guarantee delivery, security

mechanis ms, robustness and fault-tolerance. Wireless sensors

have become an excellent tool for military applicat ions

involving intrusion detection, perimeter monitoring, and

informat ion gathering and smart logistics support in an

unknown deployed area. Some other applications: sensor-

based personal health monitor, location detection with sensor

networks and movement detection.

In this paper, we try to survey the Wireless Sensor Networks

Issues and numerous Applications that utilize wireless sensor

networks and classify them in appropriate categories. As the

ongoing interest for this research area is intense, we feel that a

recording of these recent applications and trends will be useful

for perceiving new applications, or relevant research

problems, especially from the point of view of control and

systems science.

Figure 1. A wireless sensor network Architecture

II. Overview of Wireless Sensor Networks As a type of newly emerged network, WSN has many special

features comparing with traditional networks such as Internet,

wireless mesh network and wireless mobile ad-hoc network.

First of all, a sensor node after being deployed is expected to

work for days, weeks or even years without further

Rajkumar et al ,Int.J.Computer Technology & Applications,Vol 3 (5), 1667-1673

IJCTA | Sept-Oct 2012 Available [email protected]

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mailto:[email protected]




interventions. Since it is powered by the attached battery, high

efficient energy utilizat ion is necessary, which is different

from Internet as well as wireless mesh and mobile ad-hoc

network, where either constant power sources are available or

the expected lifet ime is several order of magnitude lower than

it is fo r WSNs.

Although a sensor node is expected to work through a long

time, it is often not required to work all the time, i.e., it senses

ambient environment, processes and transmits the collected

data; it then idles for a while until the next sensing processing-

transmitting cycle. To support fault tolerance, a location is

often covered by several sensor nodes. To avoid duplicate

sensing, while one node is performing the sensing processing-

transmitting cycle; other nodes are kept in the idle state. In

these cases, the energy consumption can be fu rther reduced by

letting the idle nodes turn to dormant state, where most of the

components (e.g., the wireless radio, sensing component and

processing unit) in a sensor node are turned off (instead of

keeping in operation as in the idle state). When the next cycle

comes (indicated by some mechanism such as an internal

timer), these components are then waken up back to the

normal (active) state again. Duty-cycle is defined as the ratio

between active period and the fu ll active/dormant period. A

low duty-cycle WSN clearly enjoys a much longer lifet ime for

operation. This feature has been explo ited in quite a few

research works [12][13]. However, as will be shown later in

this paper, the new working pattern also brings challenges to

the network design.

Another special feature related to energy consumption is to

control the transmission range of a sensor node. Previous

researches have shown that one of the major energy costs in a

sensor node comes from the wireless communicat ion, where

the main cost increases with the 2 to 6 power of the

transmission distance [14][15]. As a result, the transmission

range of a sensor node is often preferred to be adjustable and

may be dynamically adjusted to achieve better performance

and lower energy consumption.

III Types of sensor networks

Current WSNs are deployed on land, underground, and

underwater. Depending on the environment, a sensor network

faces different challenges and constraints. There are five types

of WSNs: terrestrial WSN, underground WSN, underwater

WSN, multi-media WSN, and mobile WSN.

Terrestrial WSNs [1] typically consist of hundreds to

thousands of inexpensive wireless sensor nodes deployed in a

given area, either in an ad-hoc or in a pre-p lanned manner. In

ad-hoc deployment, sensor nodes can be dropped from a p lane

and randomly placed into the target area. In pre-planned

deployment, there is grid placement, optimal placement [2], 2-

d and 3-d placement [3, 4] models.

In a terrestrial WSN, reliable communicat ion in a dense

environment is very important. Terrestrial sensor nodes must

be able to effectively communicate data back to the base

station. While battery power is limited and may not be

rechargeable, terrestrial sensor nodes however can be

equipped with a secondary power source such as solar cells. In

any case, it is important for sensor nodes to conserve energy.

For a terrestrial WSN, energy can be conserved with mult i-

hop optimal routing, short transmission range, in -network data

aggregation, eliminating data redundancy, minimizing delays,

and using low duty-cycle operations.

Underground WSNs [5, 6] consists of number of sensor nodes

buried underground or in a cave o r mine used to monitor

underground conditions. Additional sink nodes are located

above ground to relay information from the sensor nodes to

the base station. An underground WSN is more expensive than

a terrestrial WSN in terms of equipment, deployment, and

maintenance. Underground sensor nodes are expensive

because appropriate equipment parts must be selected to

ensure reliable communication through soil, rocks, water, and

other mineral contents. The underground environment makes

wireless communication a challenge due to signal losses and

high levels of attenuation. Unlike terrestrial WSNs, the

deployment of an underground WSN requires careful p lanning

and energy and cost considerations. Energy is an important

concern in underground WSNs. Like terrestrial WSN,

underground sensor nodes are equipped with a limited battery

power and once deployed into the ground, it is difficult to

recharge or replace a sensor node‟s battery. As before, a key

objective is to conserve energy in order to increase the lifetime

of network which can be achieved by implementing efficient

communicat ion protocol.

Underwater WSNs [7, 8] consist of a number of sensor nodes

and vehicles deployed underwater. As opposite to terrestrial

WSNs, underwater sensor nodes are more expensive and

fewer sensor nodes are deployed. Autonomous underwater

vehicles are used for exp loration or gathering data from sensor

nodes. Compared to a dense deployment of sensor nodes in a

terrestrial WSN, a sparse deployment of sensor nodes is

placed underwater. Typical underwater wireless

communicat ions are established through transmission of

acoustic waves. A challenge in underwater acoustic

communicat ion is the limited bandwidth, long propagation

delay, and signal fading issue. Another challenge is sensor

node failure due to environmental condit ions. Underwater

sensor nodes must be able to self-configure and adapt to harsh

ocean environment. Underwater sensor nodes are equipped

with a limited battery which cannot be replaced or recharged.

The issue of energy conservation for underwater WSNs

involves developing efficient underwater communicat ion and

networking techniques.

Multi-media WSNs [9] have been proposed to enable

monitoring and tracking of events in the form of mult imedia

such as video, audio, and imaging. Mult i-media WSNs consist

of a number of low cost sensor nodes equipped with cameras

and microphones. These sensor nodes interconnect with each

other over a wireless connection for data retrieval, process,

correlation, and compression. Multi-media sensor nodes are

deployed in a pre-p lanned manner into the environment to

guarantee coverage. Challenges in mult i-media WSN include

high bandwidth demand, high energy consumption, quality of

service (QoS) provisioning, data processing and compressing

techniques, and cross-layer design. Multi-media content such

as a video stream requires high bandwidth in order for the



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content to be delivered. As a result, h igh data rate leads to

high energy consumption. Transmission techniques that

support high bandwidth and low energy consumption have to

be developed. QoS provisioning is a challenging task in a

multi-media WSN due to the variable delay and variable

channel capacity.

It is important that a certain level of QoS must be achieved for

reliable content delivery. In-network processing, filtering, and

compression can significantly improve network performance

in terms of filtering and extract ing redundant information and

merging contents. Similarly, cross-layer interaction among the

layers can improve the processing and the delivery process.

Mobile WSNs consist of a collection of sensor nodes that can

move on their own and interact with the physical environment.

Mobile nodes have the ability to sense, compute, and

communicate like static nodes. A key difference is mobile

nodes have the ability to reposition and organize itself in the

network. A mobile WSN can start off with some init ial

deployment and nodes can then spread out to gather

informat ion. Informat ion gathered by a mobile node can be

communicated to another mobile node when they are within

range of each other. Another key difference is data

distribution. In a static WSN, data can be distributed using

fixed routing or flooding while dynamic routing is used in a

mobile WSN. Challenges in mobile WSN include deployment,

localization, self-organizat ion, navigation and control,

coverage, energy, maintenance, and data process.

For environmental monitoring in disaster areas, manual

deployment might not be possible. With mobile sensor nodes,

they can move to areas of events after deployment to provide

the required coverage. In military surveillance and tracking,

mobile sensor nodes can collaborate and make decisions based

on the target. Mobile sensor nodes can achieve a higher degree

of coverage and connectivity compared to static sensor nodes.

In the presence of obstacles in the field, mobile sensor nodes

can plan ahead and move appropriately to obstructed regions

to increase target exposure.

IV Various Issues

The major issues that affect the design and performance of a

wireless sensor network are as follows:

1) Hardware and Operating System for WSN

2) W ireless Radio Communication Characteristics

3) Medium Access Schemes

4) Deployment

5) Localizat ion

6) Synchronizat ion

7) Calibration

8) Network Layer

9) Transport Layer

10) Data Aggregation and Data Dissemination

11) Database Centric and Query ing

12) Architecture

13) Programming Models for Sensor Networks

14) Middleware

15) Quality of Service

16) Security

V Open research issues The design of a WSN platform must deal with challenges in

energy efficiency, cost, and application requirements. It

requires the optimizat ion of both the hardware and software to

make a WSN efficient. Hardware includes using low cost tiny

sensor nodes while software addresses issues such as network

lifetime, robustness, self-organization, security, fault

tolerance, and middleware. Application requirements vary in

terms of computation, storage, and user interface and

consequently there is no single p latform that can be applied to

all applications. Existing platforms discussed here include a

Bluetooth-based sensor system [10] and a detection-and-

classification system [11]. Future work in this area entails

examining a more pract ical platform solution for problems in

new applications. Storage capacity in low-end sensor nodes is

limited. Rather than sending large amounts of raw data to the

base station, a local sensor node‟s storage space is used as a

distributed database to which queries can send to retrieve data.

Existing approaches [16–18] present data structures that can

efficiently manage and store the data. Nevertheless , energy-

efficient storage data structure is still an open area of research

that requires optimizing various types of database queries both

with respect to performance and energy efficiency.

Performance studies provide valuable information for

developing tools and solutions to improve system

performance. Crit ical factors that influence system

performance include scalability, communication, protocols at

different layers, failures, and network management. Scalability

issues can degrade system performance. Communication

protocols are still try ing to achieve a reasonable throughput

when the size of the network increases. Optimizing and

analyzing protocols at different layers can improve system

performance and determine their benefits and limitations.

Sensor nodes can fail at any time due to hardware, software, or

communicat ion reasons. It is important that there are services

to handle these failures before and after they occur.

Development of network management tools enables

monitoring of system performance and configuring of sensor

nodes.

VI Applications

From [20], in the recent past, wireless sensor networks have

found their way into a wide variety of applications and

systems with vastly varying requirements and characteristics.

As a consequence, it is becoming increasingly difficult to

discuss typical requirements regarding hardware issues and

software support.

This is part icularly p roblematic in a multidiscip linary research

area such as wireless sensor networks, where close

collaboration between users, application domain experts,

hardware designers, and software developers is needed to

implement efficient systems.



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TABLE 1: Some applications for different areas

A classificat ion of sample applicat ions according to the

design space is presented, considering deployment, mobility,

resources, cost, energy, heterogeneity, modality,

infrastructure, topology, coverage, connectivity, size, lifet ime

and QoS. These sample applicat ions are: Great Duck (bird

observation on Great Duck island), ZebraNet, Glacier (glacier

monitoring), Herding (cattle herding), Bathymetry, Ocean

(ocean water monitoring), Grape (grape monitoring), Cold

Chain (co ld chain management), Avalanche (rescue of

avalanche victims), Vital Sign (vital sign monitoring), Power

(power monitoring), Assembly (parts assembly), Tracking

(tracking military vehicles), Mines (self-healing mine field)

and sniper (sniper localizat ion) [20].

Many researchers are currently engaged in developing the

technologies needed for different layers of the sensor networks

protocol stack. A list of current sensor network research

projects is given. Along with the current research projects, we

encourage more insight into the problems and intend to

motivate a search for solutions to the open research issues

described. These current research projects are (Project name):

SensorNet, WINS, SPINS, SINA, mAMPS, LEACH,

SmartDust, SCADDS, PicoRadio, PACMAN, Dynamic

Sensor Networks, Aware Home COUGAR and Device

Database Project DataSpace [21]. Some applicat ions for

different areas are shown in table 1.

VII CONCLUS ION

The flexib ility, fault tolerance, h igh sensing fidelity, low-cost

and rapid deployment characteristics of sensor networks create

many new and excit ing application areas for remote sensing.

In the future, this wide range of application areas will make

sensor networks an integral part of our lives. Many researchers

are currently engaged in developing the technologies needed

for different layers of the sensor networks protocol stack. A

list of current sensor networks research projects is g iven in

Table 2. Along with the current research projects, we

encourage more insight into the problems and more

development in solutions to the open research issues as

described in this paper.

Area Applications

Industrial Monitoring and control of industrial equipment

(LRWPAN [22]). Factory process control and industrial automation [25]. Manufacturing monitoring [24].

Military Military situation awareness [25].

Sensing intruders on bases, detection of enemy units movements on land/sea, chemical/biological threats and offering logistics in urban warfare [19].

Battlefield surveillance [24]. Command, control, communications, computing, intelligence, surveillance, reconnaissance, and targeting systems [26].

Location Location awareness (LR-WPAN and Bluetooth [2]). Person locator [24].

Mobile wireless lowrate

networks for precision location

Tracking of assets, people, or anything that can move in various environments, including industrial, retail,

hospital, residential, and office environments, while maintaining low-rate data communications for monitoring, messaging, and control [22].

Physical world Monitor and control the physical world: deployment of

densely distributed sensor/actuator networks for a wide range of biological and environmental monitoring applications, from marine to soil and atmospheric

contexts; observation of biological, environmental, and artificial systems; environmental monitoring of water and soil, tagging small animals unobtrusively, and tagging small and lightweight objects in a factory or

hospital setting [27]. Public safety Sensing and location determination at disaster sites

[22,23]. Automotive Tire pressure monitoring [22,23].

Active mobility [20]. Coordinated vehicle tracking [25].

Airports Smart badges and tags [22,23]. Wireless luggage tags [22].

Passive mobility (e.g., attached to a moving object not under the control of the sensor node) [20].

Agriculture Sensing of soil moisture, pesticide, herbicide, pH levels [22,23].

Emergency situations

Hazardous chemical levels and fires (petroleum sector) [22]. Fire/water detectors [19].

Monitoring disaster areas [21]. Rotating machinery

Monitoring and maintenance (electric sector) [22].

Seismic Warning systems [19].

Commercial Managing inventory, monitoring product quality [24,26].

Medical/ Health

Monitoring people‟s locations and health conditions [24].

Sensors for: blood flow, respiratory rate, ECG (Electrocardiogram), pulse oxymeter, blood pressure, and oxygen measurement [28]. Monitor patients and assist disabled patients [26].

Ocean Monitoring fish [24].



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TABLE 2: Current research projects

Project name Research area HTTP location

SensoNet [29] Transport, network, data link and physical layers Power control, mobility

and task management planes http://www.ece.gatech.edu/research/

labs/bwn/ WINS [30,31] Distributed network and Internet access to sensors, controls, and

processors http://www.janet.ucla.edu/WINS/

SPIN [48] Data dissemination protocols http://nms.lcs.mil.edu/projects/leach SPINS [50] Security protocol http://paris.cs.berkeley.edu/

_perrig/projects.html SINA [51,52] Information networking architecture http://www.eecis.udel.edu/_cshen/ lAMPS [32] Framework for implementing adaptive energy-aware distributed

microsensors http://www-mtl.mil.edu/research/

icsystems/uamps/ LEACH [33] Cluster formation protocol http://nms.lcs.mit.edu/projects/leach Smart dust [34] Laser communication from a cubic millimeter

Mote delivery

SubmicroWatt electronics Power sources MacroMotes (COTS Dust)

http://robotics.eecs.berkeley.edu/ _pister/SmartDust/

SCADDS

[43,44,35,30,45,46,47,49,53]

Scalable coordination architectures for deeply distributed

and dynamic system

http://www.isi.edu/scadds/

PicoRadio [36,37] Develop a „„system-on-chip‟‟ implementation of a PicoNode http://bwrc.eecs.berkeley.edu/Research/ Pico_Radio/PicoNode.htm

PACMAN [38] Mathematical framework that incorporates key features of computing nodes and networking elements

http://pacman.usc.edu

Dynamic sensor networks [39]

Routing and power aware sensor management Network services API

http://www.east.isi.edu/DIVl0/dsn/

Aware home [40] Requisite technologies to create a home environment that can both perceive and assist its occupants

http://www.cc.gatech.edu/fce/ahri

COUGARdevice database project [41]

Distributed query processing http://www.cs.cornell.edu/database/ cougar/index.htm

DataSpace [42] Distributed query processing http://www.cs.rutgers.edu/dataman

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Rajkumar is native of Bidar,

Karnataka, India. He received his

B.E Degree in Computer Science

and Engineering from VEC,

Bellary, Gulbarga University

Gulbarga and M.Tech in Computer

Engineering from SJCE Mysore,

Visvesvaraya Technological

University Belgaum . Presently he

is serving as Assistant Professor in

the department of Information Science and Engineering at

Sambhram Institute Of Technology, Bangalore. His areas of

interest are wireless communication, sensor networks.

([email protected])

Vani B. A is native of Davangere, Karnataka, India. She

received her B.E Degree in Computer

Science and Engineering from Bapuji

Institute of Technology Davangere,

Kuvempu University and M.Tech

Degree in Computer Science &

Engineering from Bapuji Institute of

Technology Davangere, Visveswaraiah

Technological University Belgaum.

Presently she is serving as Assistant

Professor in the department of Informat ion Science and

Engineering at Sambhram Institute Of Technology,

Bangalore. Her areas of interest are wireless communication,

sensor networks. ([email protected])

Kiran Jadhav is native of Bangalore,

Karnataka, India. She received her B.E

Degree in Computer Science and

Engineering from VEC, Bellary,

Visvesvaraya Technological University

Belgaum, and M.Tech Degree in

Information Science & Engineering

from M S Ramaiah Institute of

Technology Bangalore, Visveswaraiah

Technological University Belgaum.

Presently she is serving as Senior Lecturer in the department

of Informat ion Science and Engineering at Sambhram

Institute Of Technology, Bangalore. Her areas of interest are

wireless communication, sensor networks.

([email protected])

Vidya S Biradar is native of Bangalore, Karnataka, India.

She received her B.E Degree in Informat ion Science and

Engineering from CIT, Ponnampet,

Visvesvaraya Technological University

Belgaum, and presently she is serving as

Lecturer in the department of

Information Science and Engineering at

Sambhram Institute of Technology,

Bangalore. Her areas of interest are

wireless communication, sensor

networks. ([email protected])



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