WIRELESS SENSOR NETWORKS ROUTINGTECHNIQUES IN WIRELESS SENSOR
Wireless Sensor Networks Issues and Applications - … · Wireless Sensor Networks Issues and...
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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
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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.
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.
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])
Rajkumar et al ,Int.J.Computer Technology & Applications,Vol 3 (5), 1667-1673
IJCTA | Sept-Oct 2012 Available [email protected]
1673
ISSN:2229-6093