Wireless Sensor Network, 2010, 2, 483-491
doi:10.4236/wsn.2010.26060 Published Online June 2010 (http://www.SciRP.org/journal/wsn)
Copyright © 2010 SciRes. WSN
Energy Conservation Challenges in Wireless Sensor
Networks: A Comprehensive Study
Suraiya Tarannum
Department of Telecommunication Engineering AMC Engineering College, Bangalore, India
E-mail: ssuraiya@gmail.com
Received December 31, 2009; revised February 5, 2010; accepted February 10, 2010
A Wireless Sensor Network (WSN) consists of a large number of randomly deployed sensor nodes. These
sensor nodes organize themselves into a cooperative network and perform the three basic functions of sens-
ing, computations and communications. Research in WSNs has become an extensive explorative area during
the last few years, especially due to the challenges offered, energy constraints of the sensors being one of
them. In this paper, a thorough comprehensive study of the energy conservation challenges in wireless sensor
networks is carried out. The need for effective utilization of limited power resources is also emphasized,
which becomes pre-eminent to the Wireless Sensor Networks.
Keywords: Wireless Sensor Network, Sensor Node, Communication Protocols Architecture, Energy Consumption
of Sensor Node, Energy Conservation, Communication Protocols
1. Introduction
Wireless Sensor Networks (WSNs) are a spatially dis-
tributed autonomous system which is a collection of
power-conscious wireless sensors without the support
of pre-existing infrastructure. A co-operative system is
created, formed by a group of specialized transducers
with communication infrastructure intended to monitor
and record conditions at diverse locations. A WSN is
used for information gathering, performing data-inten-
sive tasks such as habitat monitoring, seismic monitor-
ing, terrain, surveillance etc. Sensor Networks are a
gaint leap toward “Proactive Computing”, a paradigm
where computers anticipate human needs and if neces-
sary, act on their behalf. Sensor Networks and proactive
computing has the potential to improve our productivity
and enhance safety, awareness and efficiency at the
societal scale [1].
Building sensors has been made possible by the recent
advances in Micro-Electro-Mechanical System (MEMS)
technology and wireless communications technology
making it a pragmatic vision to deploy a large-scale, low
power, inexpensive wireless sensor network [2]. Such an
approach promises advantages over the traditional sens-
ing methods in many ways: large-scale, dense deploy-
ment not only extends the spatial coverage and achieves
higher resolution, but also increases the fault-tolerance
and robustness of the system.
The recent advances in MEMS (Micro Electro Me-
chanical Systems) [3], Digital Signal Processing and
Wireless Communications have led to the production of
new class of wireless, battery operated smart sensor nodes
[4]. These nodes organize themselves to form active,
full-fledged processing elements, capable of measuring
the real world phenomena, filtering, sharing and com-
bining these measurements. In such networks, the de-
vices identify themselves and each other, to route data
without possessing any prior knowledge of or assump-
tions of the network topology, which may change, run out
of power or experience shifting waves of interference.
A network formed by a web of such sensors is de-
ployed in remote areas or hostile terrains, without the
infrastructure support from the outside world. This ex-
erts serious physical constraints on the application of
single sensor, and thus, all the sensor nodes can form an
autonomous and robust data computing and communi-
cation distributed system for automated information
gathering and distributed sensing. Sensor networks are
highly distributed networks of small, lightweight nodes
termed motes, deployed in large numbers to monitor the
environment or a system by measuring physical parame-
ters such as temperature, pressure or relative humidity.
Each sensor node consists of three subsystems: the
sensor subsystem which senses the environment, the
processing subsystem which performs local computa-
tions on the sensed data, and the communication subsys-
tem which is responsible for message exchange with nei-
ghbouring sensor nodes. While individual sensors have
limited sensing region, processing power and energy,
networking a large number of sensors gives rise to a ro-
bust, reliable and accurate sensor network covering a
wider region.
The network so formed is fault-tolerant since many
nodes participate in sensing the same events. Further-
more, the nodes cooperate and collaborate on their data,
which leads to accurate sensing of events in the environ-
ment. The two most important operations in a sensor
network are data dissemination, which is the propagation
of data/queries throughout the network, and data gather-
ing where the collection of observed data takes place
from each of the sensor nodes. Finally, the aggregated
data is sent to the sink/basestation. A typical scenario of
the WSN is depicted in Figure 1.
Efficient management of energy deserves much of the
attention in the WSNs. Routing protocols designed for
WSNs must therefore effectively tackle these issues in
order to enhance the lifetime of the network. Hierarchical
routing techniques are preferable in this direction. The
arrangement of the nodes in the form of a load balanced
hierarchy proves beneficial.
In this paper, a state-of-art study of the energy con-
servation challenges in wireless sensor networks is de-
scribed. The rest of the paper is organized as follows. In
section 2, the sensor node is described. The applications
and the issues and challenges are described in sections
3 and 4 respectively. Section 5 throws light on the en-
ergy consumption details and the communication pro-
tocol architecture is described in section 6. Section 7
enlightens the energy conservation challenges in com-
munication protocols and related design issues in wire-
less sensor networks. The usual performance evaluation
metrics employed in WSNs are described in sections 8
and 9 contains the conclusions.
2. The Sensor Node
The sensor node is an atomic element of the wireless
Figure 1. A typical scenario of a Wireless Sensor Network
sensor network, which gathers data from it’s surround-
ings, and transmits them to the base station/sink enroute
the radio transmission medium. Every node is provided
with a unique ID number and has an input queue as a
buffer. At any point of time, a sensor can behave as a
transmitter node, relay node, and sink node or all them.
In many application scenarios, a myriad of sensor nodes
are spread across a large geographical area, which col-
laborate and organize themselves in order to carry out the
desired task. This implies that a sensor node forms an
integral and the most important unit of the wireless sen-
sor network and deserves understanding of its internal
A sensor node is typically made up of four basic
components as shown in Figure 2: A sensing/actuating
unit, a processing unit, transceiver section and power
supply unit. In addition to this, the sensor node may also
be equipped with location detection unit such as a Global
Positioning System (GPS), a mobilizer etc. The sensor
networks consist of different types of sensors such as
seismic, thermal, visual, and infrared and are used to
monitor a variety of ambient conditions such as tem-
perature, humidity, pressure and characteristics of ob-
jects and their motion. The sensors give these nodes their
eyes and ears. Sensor nodes can be used in military,
health, chemical processing and disaster relief scenarios.
The sensor node architecture is described in Figures 2
and 3.
2.1. Sensing Unit
The sensing unit is usually made up of two subunits, the
sensors themselves and analog-to-digital converters (AD-
Cs). The signals generated by the sensors, based on the
phenomenon to be sensed, are analog in nature and hence
need to be converted to a digital to aid further processing.
These signals are then fed to the processing unit.
2.2. Processing Unit
The processing unit forms the core of the sensor node.
Figure 2. Sensor node architecture.
Copyright © 2010 SciRes. WSN
Figure 3. A typical sensor node.
This unit in association with a small storage unit, man-
ages the procedures that make the sensor node collabo-
rate with the other nodes to carry the sensing tasks. The
processors employed in the sensor nodes include, the
Atmel AtMega Microcontroller, MSP430, Intel Strong
ARM [5], to name a few.
2.3. Communication Unit
The transceiver unit connects wirelessly through the RF
channel and is linked to an omni-directional antenna that
allows for communications in all directions. The main
task of a transceiver is to convert a bit stream arriving
from the processing unit into electromagnetic radio
waves. Some of commonly used transceivers in sensor
nodes are RFM TR family, Chipcoa CC10000 family, The
Infineon TDA 525x family etc.
The transceiver unit may be passive or an active opti-
cal device or a RF device. RF communications require
modulation, band pass filtering, demodulation and multi-
plexing circuitry, which make them more complex and
expensive. Moreover, the path loss of the transmitted
signal between two sensor nodes may be as high as the
fourth order exponent of the distance between them, be-
cause the antennas of the sensor nodes are close to the
ground. Nevertheless, RF communications is preferred in
most of the ongoing sensor network research, because
the packets conveyed in sensor networks are small, data
rates are low and frequency reuse is high due to short
communication distances. These characteristics also make
it possible to use low duty cycle radio electronics for
sensor networks.
Of the three domains, a sensor node expends maxi-
mum energy in data communications. This involves both
data transmission and reception. It is found that for
short-range communications with low radiation power,
the transmission and reception energy costs are nearly
the same. Mixers, frequency synthesizers, voltage con-
trolled oscillators, phase locked loops (PLLs) and power
amplifiers, all consume valuable power in the transceiver
2.4. Power Supply Unit
The sensor nodes can be powered from energy storage
devices or by energy scavenging. The former technique
employs a variety of tiny batteries made up of thin films
of vanadium oxide and molybdenum oxide [6]. These are
fabricated using micro-machined cavities containing an
electrolyte, in addition to chemical energy storage. The
latter technique employs energy scavenging from the
environment in order that the sensor node can operate
uninterrupted. The most widely used energy scavenging
technique is the solar radiation. There is a possibility of
energy-harnessing from body heat in bio-medical appli-
The battery forms the heart of the sensor system as it
decides the lifetime of the system. The battery lifetime
needs to be prolonged to maximize the network lifetime.
Network Lifetime is defined as the maximum number of
times a certain data collection function or task can be
carried out without any node running out of energy. It is
also defined as the time elapsed until the first node in the
network is completely depleted of its energy and is de-
termined by the ability to conserve energy in the network.
The requirement is that the size of the battery should be
as small as possible, the same time being energy efficient.
Batteries with energy scavenging capabilities are being
designed to increase the lifetime of the sensor system.
Two AA sized batteries of 1.2 V each are employed in
the battery subsection [1].
Most of the sensor network routing techniques require
the knowledge of precise location of nodes that are de-
ployed in the sensor field. This requires a Global Posi-
tioning System (GPS) to carry out the tasks. A mobilizer
may sometimes be needed, especially in Heterogenene-
ous Wireless Sensor Networks (HWSNs) to move the
sensor nodes, when circumstances demand. All of these
subunits may need to fit into a matchbox-sized module
[1]. The required size may be smaller than even a cu-
bic-centimeter [4], which is light enough to be suspended
in air.
The Heterogeneous Wireless Sensor Networks (HWSN),
a class of WSNs are distributed networks consisting of
large number of tiny, typically the size of 35 mm film
canister [7,8], static, low power sensor nodes along with
a few mobile, high power nodes. These sensor nodes just
like their WSN counterparts, have sensing, processing,
co-ordinating and communicating abilities. They are
used to monitor changes in unattended regions and relay
information to the respective control center where nec-
essary action would be taken. In order to complete a
Copyright © 2010 SciRes. WSN
given task, all sensor nodes have to collaborate by ex-
changing and forwarding measurement data.
3. Applications
The Wireless Sensor Network technology has the poten-
tial to change the way we live, work and do business,
with applications in entertainment, travel, retail industry,
disaster and emergency management. It forms an in-
creasingly attractive means of monitoring environmental
conditions and to bridge the gap between the physical
and the virtual world. Application areas for WSNs in-
clude geophysical monitoring (seismic activity), preci-
sion agriculture (soil management), habitat monitoring
(tracking of animal herds), transportation (traffic moni-
toring), military systems, business process (supply chain
management) [9,10] etc.
With continued advances in Micro-Electro-Mechanical
Systems (MEMS), Wireless Sensor Networks (WSNs)
have and will play a vital role in our daily lives. Humans
have relied on wired sensors for years, for simple tasks
such as temperature monitoring, to complex tasks such as
monitoring life-signs in hospital patients. Wireless Sen-
sor Networks provide unforseen applications in this new
field of design [1]. From military applications such as
battlefield mapping and target surveillance, to creating
context-aware homes [11] where sensors can monitor
safety and provide automated services tailored to the
individual user; the number of applications are endless.
Smart Dust is an example of one such application [12,13].
However this new technology poses many design goals,
[1] that up until recently, have not been considered feasi-
ble for these applications.
1) The sensor networks are used in a variety of appli-
cations which require constant monitoring and detection
of specific events. The military applications include bat-
tle field surveillance and monitoring, guidance systems
of intelligent missiles and detection of attack by weapons
of mass destruction, such as chemical, biological or nu-
clear [14].
2) The WSNs are employed in environmental applica-
tions [15] such as forest-fire and flood detection and ha-
bitat exploration of animals [16-19].
3) Sensors are extremely useful in patient diagnosis
and monitoring. Bio-sensors are implanted in the human
body to monitor the patient’s physiological parameters
such as heart beat or blood pressure. The data so col-
lected is sent regularly to alert the concerned doctor on
detection of an anamoly. Such an arrangement provides
patients a greater freedom of movement instead of being
constantly confined to the hospital bed. Rapid advance-
ments in MEMS technology has made bio-sensors so
sophisticated as to enable correct identification of aller-
gies and associated diagnosis [1,20].
4. Issues and Challenges
The WSN is subjected to various resource constraints.
The constraints are energy, bandwidth, memory and
processing ability. Among them, energy is of prime con-
cern, since it is severely constrained at sensor nodes and
it is not feasible to either replace or recharge the batteries
of sensor nodes that are often deployed in hostile envi-
ronment. As a result, these constraints impose an impor-
tant requirement on any QoS support mechanism in
WSNs. Energy efficiency is a critical design issue in
WSNs, where each sensor node relies on its limited bat-
tery power for data acquisition, processing, transmission
and reception.
As the sensor nodes are typically very small and pow-
ered by irreplaceable battery, energy control becomes
primary and also the most challenging problem in de-
signing sensor networks [21]. In WSNs, each sensor
node has different energy consumption rate due to ine-
quality in event sensing and distance from Base Station.
This leads to energy disparity among sensor nodes in the
network which in turn shortens the lifetime of the net-
Another important issue in WSN is satisfying the QoS
parameters. QoS parameters are used for evaluating the
performance of networks. The various QoS parameters
under considerations are latency, throughput and reliabil-
ity. Security is a major concern in wireless communica-
tions. Sensor network is susceptible to a variety of at-
tacks, including node capture, physical tampering and
denial of service while prompting a range of fundamental
research challenges. The QoS parameters and energy
conservation are the prime factors affecting the lifespan
of sensor network. Energy efficient routing mechanisms
are inculcated to boost the performance of the sensor
network. Wireless sensor networks pose certain design
challenges due to the following reasons,
1) The sensor nodes are randomly deployed and hence
do not fit into any regular topology. Once deployed, they
usually do not require human intervention. This implies
that setup and maintenance need to be autonomous.
2) Sensor networks are infrastructureless. Therefore,
all routing and maintenance algorithms need to be dis-
3) An important bottleneck in the operation of sensor
nodes is the available energy. Sensors usually rely on
their battery for power, which in many cases should be
considered as a major constraint while designing proto-
cols. The wireless sensor node, being a micro-electronic
device, can only be equipped with a limited power
source. In most application scenarios, replenishment of
power resources might become impossible. The sensor
node lifetime, therefore, shows a strong dependence on
battery lifetime.
4) Hardware design for sensor nodes should also con-
sider energy efficiency as a primary requirement. The
Copyright © 2010 SciRes. WSN
micro-controller, operating system, and application soft-
ware should be designed to conserve power.
5) Sensor nodes should be able to synchronize with
each other in a completely distributed manner, so that
TDMA schedules can be imposed and temporal ordering
of detected events can be performed without ambiguity.
6) A sensor network should also be capable of adapt-
ing to changing connectivity due to the failure of nodes,
or new nodes powering up. The routing protocols should
also be able to dynamically include or avoid sensor nodes
in their paths.
7) Real-time communication over sensor networks must
be supported through provision of guarantees on maxi-
mum delay, minimum bandwidth, or other QoS parame-
5. Energy Consumption of Sensor Node
The sensor nodes operate in the three modes of sensing,
computing and communications, and all of which con-
sume energy. Of the three modes, maximum energy is
expended for the communications process. The sensing
unit is entrusted with the responsibility to detect the
physical characteristics of the environment and has an
energy consumption that varies with the hardware nature
and applications. However, sensing energy represents a
meagre percentage of the entire energy consumption
within the entire WSN. In comparison, computations en-
ergy is much more. The communication unit consists of a
short-range RF circuit which performs the transmission
and reception tasks.
Communication energy contributes to data forwarding
and it is determined with the transmission range that in-
creases with the signal propagation in an exponential
way. The energy consumption model includes the five
states: Acquisition, Transmission, Reception, Listen and
Sleep [22]. These states are described in Table 1.
Since the sensor nodes can be in any of three main op-
erations of sensing, computations and communications,
each of them could be in different states depending on
the component nature. Accordingly different levels of
energy are expended in each of them.
Table 1. States of the energy consumption model.
(i) Acquistion: The acquistion state includes sensing, A/D conver-
sion, preprocessing and eventually storage of these data.
(ii) Transmission: The transmission state includes processing,
packet forming, encoding, framing, queuing and base band adapt-
ing to RF circuits.
(iii) Reception: This state is responsible for low noise amplifica-
tion, down converter oscillator, filtering, detection, decoding, error
detection, address checking and random reception.
(iv) Listen: The listen state is similar to reception and involves the
processes of low noise amplification, down convertor oscillator,
filtering and terminates at detection.
Sleep: The sleep state expends least energy as compared to the
other states.
6. Wireless Sensor Networks
Communication Protocols Architecture
Figure 4 depicts the communication protocol stack ar-
chitecture of the WSN. The energy consumed in one
sensor node is influenced by protocol layers structure
and the way each layer manages the sensing data.
The protocol layers stack used by the sink and nodes
within the network includes the application layer, trans-
port layer, network layer, data link layer, physical layer,
power management plane, mobility management plane
and task management plane and described in Table 2.
7. Energy Conservation Challenges in
Communication Protocols and Design
Issues in WSNs
Despite the innumerable applications of WSNs, these
networks have several restrictions, e.g., limited energy
supply, limited computing power, and limited bandwidth
of the wireless links connecting sensor nodes. One of the
main design goals of WSNs is to carry out data commu-
nication while trying to prolong the lifetime of the net-
work and prevent connectivity degradation by employing
aggressive energy management techniques.
The design of routing protocols in WSNs is influenced
by many challenging factors. These factors must be over-
come before efficient communication can be achieved. In
Table 2. Protocol layer stack.
(i) Application Layer: This supports different softwares for ap-
plications depending on the sensing tasks. There are three types of
protocols defined for this layer:
SMP - Sensor Management Protocol
TADAP - Task Assignment and Data Advertisment Protocol
SQDDP - Sensor Query and Data Dissemination Protocol
(ii) Transport Layer: This layer helps to maintain the data flow
when the application layer is in need. The protocol development on
this layer is a real challenge because sensors are influenced by
many factors and constraints such as limited power and memory.
(iii) Network Layer: The network layer allows routing of data
through the wireless communication channel. There are several
methods and strategies to route data such as routing power cost
with available energy based on the energy metric and data-centric
routing based on interest dissemination and attribute based naming
(iv) Data Link Layer: This layer is responsible for the multiplex-
ing of data streams, data frame detection, medium access control
(MAC) and error detection and correction. The design issues of the
MAC layer protocol must take into account the different con-
straints such as power conservation, mobility management and
recovery failure strategies.
(v) Physical Layer: This is the lower-most layer and is responsible
for frequency selection, carrier frequency generation, signal detec-
tion, modulation and data encryption.
Copyright © 2010 SciRes. WSN
Figure 4. Wireless Sensor Network protocol stack.
the following subsections, some of the routing challenges
and design issues that affect routing process in WSNs,
are summarized [1,24,25,26].
1) Node Deployment
Node deployment in WSNs is application dependent
and affects the performance of the routing protocol. The
deployment can be either deterministic or randomized. In
deterministic deployment, the sensors are manually placed
and data is routed through pre-determined paths. How-
ever, in random node deployment, the sensor nodes are
scattered randomly creating an infrastructure in an ad
hoc manner.
If the resultant distribution of nodes is not uniform,
optimal clustering becomes necessary to allow connec-
tivity and enable energy efficient network operation.
Inter-sensor communication is normally within short trans-
mission ranges due to energy and bandwidth limitations.
Therefore, it is most likely that a route will consist of
multiple wireless hops.
2) Energy Consumption without Losing Accuracy
The sensor nodes can use up their limited supply of
energy performing computations and transmitting infor-
mation in a wireless environment. As such, energy-con-
serving forms of communication and computation are
essential. Sensor node lifetime shows a strong depend-
ence on the battery lifetime [1].
In a multihop WSN, each node plays a dual role as
data sender and data router. The malfunctioning of some
sensor nodes due to power failure can cause significant
topological changes and might require re-routing of
packets and reorganization of the network.
3) Data Reporting Model
Data sensing and reporting in WSNs is dependent on
the application and the time criticality of the data report-
ing. Data reporting can be categorized as either time-
driven (continuous), event-driven, query-driven, and hy-
brid [27]. The time-driven delivery model is suitable for
applications that require periodic data monitoring. As
such, sensor nodes will periodically switch on their sen-
sors and transmitters, sense the environment and transmit
the data of interest at constant periodic time intervals.
In event-driven and query-driven models, sensor nodes
react immediately to sudden and drastic changes in the
value of a sensed attribute due to the occurrence of a
certain event or a query is generated by the BS. As such,
these are well suited for time critical applications. A
combination of the previous models is also possible. The
routing protocol is highly influenced by the data report-
ing model with regard to energy consumption and route
4) Node/Link Heterogeneity
In many studies, all sensor nodes are assumed to be
homogeneous, i.e., having equal capacity in terms of
computation, communication, and power. However, de-
pending on the application a sensor node can have dif-
ferent role or capability. The existence of heterogeneous
set of sensors raises many technical issues related to data
routing. For example, some applications might require a
diverse mixture of sensors for monitoring temperature,
pressure and humidity of the surrounding environment,
detecting motion via acoustic signatures, and capturing
the image or video tracking of moving objects.
These special sensors can be either deployed inde-
pendently or the different functionalities can be included
in the same sensor nodes. Even data reading and report-
ing can be generated from these sensors at different rates,
subject to diverse quality of service constraints, and can
follow multiple data reporting models. For example, hi-
erarchical protocols designate a cluster-head node dif-
ferent from the normal sensors. These cluster-heads can
be chosen from the deployed sensors or can be more
powerful than other sensor nodes in terms of energy,
bandwidth, and memory. Hence, the burden of transmis-
sion to the BS is handled by the set of cluster-heads [28].
5) Fault-Tolerance
Some sensor nodes may fail or be blocked due to lack
of power, physical damage, or environmental interfer-
ence. The failure of sensor nodes should not affect the
overall task of the sensor network. If many nodes fail,
MAC and routing protocols must accommodate forma-
tion of new links and routes to the data collection base
stations. This may require actively adjusting transmit
powers and signalling rates on the existing links to re-
duce energy consumption, or rerouting packets through
regions of the network where more energy is available
[29]. Therefore, multiple levels of redundancy may be
needed in a fault-tolerant sensor network.
6) Scalability
The number of sensor nodes deployed in the sensing
area may be in the order of hundreds or thousands, or
more. Any routing scheme must be able to work with this
huge number of sensor nodes. In addition, sensor net-
work routing protocols should be scalable enough to re-
Copyright © 2010 SciRes. WSN
spond to events in the environment. Until an event oc-
curs, most of the sensors can remain in the sleep state,
with data from the few remaining sensors providing a
coarse quality.
7) Network Dynamics
Most of the network architectures assume that sensor
nodes are stationary. However, mobility of both BSs and
sensor nodes is sometimes necessary in many applica-
tions [19]. Routing messages from or to moving nodes is
more challenging since route stability becomes an im-
portant issue, in addition to energy, bandwidth etc.
Moreover, the sensed phenomenon can be either dy-
namic or static depending on the application, e.g., it is
dynamic in a target detection/tracking application, while
it is static in forest monitoring for early fire prevention.
Monitoring static events allows the network to work in a
reactive mode, simply generating traffic when reporting.
Dynamic events in most applications require periodic
reporting and consequently generate significant traffic to
be routed to the BS.
8) Transmission Media
In a multi-hop sensor network, communicating nodes
are linked by a wireless medium. The traditional prob-
lems associated with a wireless channel (e.g., fading,
high error rate) may also affect the operation of the sen-
sor network. In general, the required bandwidth of sensor
data will be low, on the order of 1-100 kbps. Related to
the transmission media is the design of medium access
control (MAC). One approach of MAC design for sensor
networks is to use TDMA based protocols that conserve
more energy compared to contention based protocols like
CSMA (e.g., IEEE 802.11).
9) Connectivity
High node density in sensor networks precludes them
from being completely isolated from each other. There-
fore, sensor nodes are expected to be highly connected.
This, however, may not prevent the network topology
from being variable and the network size from being
shrinking due to sensor node failures. In addition, con-
nectivity depends on the possibly random distribution of
10) Coverage
In WSNs, each sensor node obtains a certain view of
the environment. A given sensors view of the environ-
ment is limited both in range and in accuracy; it can only
cover a limited physical area of the environment. Hence,
area coverage is also an important design parameter in
11) Data Aggregation/Fusion
Since sensor nodes may generate significant redundant
data, similar packets from multiple nodes can be aggre-
gated so that the number of transmissions is reduced.
Data aggregation is the combination of data from differ-
ent sources according to a certain aggregation function,
e.g., duplicate suppression, minima, maxima and average.
This technique has been used to achieve energy effi-
ciency and data transfer optimization in a number of
routing protocols. Signal processing methods can also be
used for data aggregation. In this case, it is referred to as
data fusion where a node is capable of producing a more
accurate output signal by using some techniques such as
beamforming to combine the incoming signals and re-
ducing the noise in these signals.
12) Quality of Service
In some applications, data should be delivered within a
certain period of time from the moment it is sensed; oth-
erwise the data will be useless. Therefore bounded la-
tency for data delivery is another condition for time-
constrained applications. However, in many applications,
conservation of energy, which is directly related to net-
work lifetime, is considered relatively more important
than the quality of data sent.
As the energy gets depleted, the network may be re-
quired to reduce the quality of the results in order to re-
duce the energy dissipation in the nodes and hence leng-
then the total network lifetime. Hence, energy-aware rout-
ing protocols are required to capture this requirement.
8. Performance Evaluation Metrics
In order to study the challenges offered by the energy con-
strained wireless sensor nodes and to evaluate the per-
formance and the QoS offered by the network, the per-
formance metrics under consideration are discussed in
Table 3.
The previous sections threw light on the WSNs, their
characteristics, issues, challenges and applications. In order
to understand their performance and behavior, the OM-
NET++ (Objective Modular Network Test-bed in C++)
simulator may be employed. OMNET++ is a discrete-
Table 3. Performance metrics.
Energy Consumption per successful data report This gives
a good measure of the network lifetime. A routing algorithm which
maximizes the lifetime of network, is desirable. This metric also
shows how efficient the algorithm is, in energy consumption. This
metric is an indication of the energy cost incurred to realize the
achieved performance.
Network Lifetime
Network Lifetime is defined as the time elapsed until the first
node in the network is completed drained of its energy (dies).
Network Throughput
This is defined as the total number of packets received at the sink
divided by the simulation time.
Latency is defined as the average time that a packet moves on the
Delivery Ratio
Delivery ratio of the network is specified in terms of the number of
packets received at the sink divided by the number of packets
generated at the source.
Copyright © 2010 SciRes. WSN
event simulator for WSNs [30]. It is a public-source, com-
ponent-based, modular simulation frame work and used
to simulate communication networks and other distrib-
uted systems.
Discrete-event simulation is a trusted platform for mod-
elling and simulating a variety of systems. The design of
WSNs requires the simultaneous consideration of the
effects of several factors such as energy efficiency, fault-
tolerance, Quality of Service (QoS) demands, synchro-
nization, scheduling strategies, system topology, com-
munications and coordination protocols.
9. Conclusions
A WSN is composed of tens to thousands of sensor
nodes which communicate through a wireless channel for
information sharing and processing. The sensors are de-
ployed on a large scale for environmental monitoring and
habitat study, for military surveillance, in emergent en-
vironments for search and rescue, in buildings for infra-
structure health monitoring, in homes to realize a smart
environment. WSNs have been made viable by the con-
vergence of micro-electro-mechanical systems technol-
ogy, wireless communications and digital electronics.
The energy conservation challenges and related issues
emphasize the need for energy saving and optimizing
protocols to increase the lifetime of sensor networks.
10. References
[1] I. F. Akylidiz, W. L. Su, Y. Sankarasubramaniam and E.
Cayirci, “Wireless Sensor Networks: A Survey on Sensor
Networks,” IEEE Communications Magazine, Vol. 40,
No. 8, 2002, pp. 102-114.
[2] D. Estrin, R. Govindan, J. Heidemann and S. Kumar,
“Next Century Challenges: Scalable Coordination in Sen-
sor Networks,” Proceedings of the ACM/IEEE Interna-
tional Conference on Mobile Computing and Networking,
Seattle, Washington, USA, August 1999, pp. 263-270.
[3] P. B. Chun, N. R. Lo, E. Berg and K. S. J. Pister, “Optical
Communication Using Micro-Corner Cube Reflectors,”
Proceedings of the 10th IEEE International Micro Elec-
tro Mechanical Systems Conference (MEMS’97), Vol. 40,
No. 8, 1997, pp. 350-355.
[4] G. Pottie and W. Kaiser, “Wireless Integrated Network
Sensors,” Communications of the ACM, Vol. 43, No. 5,
2000, pp. 51-58.
[5] H. Hashemi, “The Indoor Radio Propagation Channel,”
Proceedings of IEEE, Vol. 81, No. 7, 1993, pp. 943-968.
[6] H. Baltes et al., “Micromachined Thermally Based CMOS
Microsensors,” Proceedings of IEEE, Vol. 86, No. 8,
1998, pp. 1660-1678.
[7] D. Culler, D. Estrin and M. Srivastava, “Overview of
Sensor Networks,” IEEE Computer, Vol. 37, No. 8, 2004,
pp. 41-49.
[8] S. Tarannum, D. Prakash, S. George, B. V. Tara, S. Ushe,
L. Nalini, K. R. Venugopal and L. M. Patnaik, “Consoli-
date and Advance: An Efficient QoS Management in He-
terogeneous Wireless Sensor Networks,” IEEE ICSCN
2008, Chennai, January 2008, pp. 93-98.
[9] B. Akan, Y. Sankarasubramaniam and I. F. Akyildiz,
“ESRT: Event-to-Sink Reliable Transport in Wireless
Sensor Networks,” Proceedings of the 4th ACM Interna-
tional Symposium on Mobile Ad Hoc Networking and
Computing, Annapolis, Maryland, USA, 2003, pp. 177-
[10] M. Kuorilehto, M. Hännikäinen and T. D. Hämäläinen,
“A Survey of Application Distribution in Wireless Sensor
Networks,” EURASIP Journal on Wireless Communica-
tions and Networking, Vol. 2005, No. 5, 2005, pp. 774-
[11] S. Meyer and A. Rakotonirainy, “A Survey of Research
on Context-Aware Homes,” Workshop on Wearable, In-
visible, Context-Aware, Ambient, Pervasive and Ubiqui-
tous Computing, Adelaide, 2003, pp. 159-168.
[12] B. Warneke, M. Last, B. Liebowitz and K. S. J. Pister,
“Smart Dust: Communicating with a Cubic-Millimeter
Computer,” Computer Magazine, Vol. 34, No. 1, 2002,
pp. 44-51.
[13] V. Hsu, M. Kahn and K. Pister, “Wireless Communication
for Smart Dust,” Electronic Research Laboratory Tech-
nical Memorandum, February 1998.
[14] A. G. Ruzzellli, R. Tynan, M. J. O’Grady and G. M. P.
O’Hare, “Advances in Wireless Sensor Networks,” Ency-
clopaedia of Mobile Computing and Commerce (EMCC),
Vol. 1, 2006, pp. 1-12.
[15] D. C. Steere, A. Baptista, D. McNamee, C. Pu and J.
Walpole, “Research Challenges in Environmental Obser-
vation and Forecasting Systems,” Proceedings of the 6th
Annual International Conference on Mobile Computing
and Networking, Boston, Massachusetts, United States,
2000, pp. 292-299.
[16] E. Biagioni and K. Bridges, “The Application of Remote
Sensor Technology to Assist the Recovery of Rare and
Endangered Species,” In Special Issue on Distributed
Sensor Networks for the International Journal of High
Performance Computing Applications, Vol. 16, No. 3,
2002, pp. 315-324.
[17] A. Cerpa, J. Elson, D. Estrin, L. Girod, M. Hamilton and J.
Zhao, “Habitat Monitoring: Application Driver for Wire-
less Communications Technology,” Proceedings of the
2001 ACM SIGCOMM Workshop on Data Communica-
tions in Latin America and the Caribbean, San Jose,
Costa Rica, April 2001, pp. 20-41.
[18] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler and J.
Anderson, “Wireless Sensor Networks for Habitat Moni-
toring,” In ACM International Workshop on Wireless
Sensor Networks and Applications (WSNA’02), Atlanta,
September 2002, pp. 88-97.
[19] H. Wang, J. Elson, L. Girod, D. Estrin and K. Yao, “Target
Classification and Localization in Habitat Monitoring,”
Proceedings of the IEEE ICASSP’03, Hong Kong, April
2003, pp. 597-600.
Copyright © 2010 SciRes. WSN
Copyright © 2010 SciRes. WSN
[20] L. Schwiebert, S. K. S. Gupta and J. Weinmann, “Research
Challenges in Wireless Networks of Biomedical Sensors,”
Mobile Computing and Networking, Rome, Italy, 2001,
pp. 151-165.
[21] M. Eltoweissy, M. Younis, K. Akkaya and A. Wadaa,
“On Handling QoS Traffic in Wireless Sensor Networks,”
Proceedings of the 37th Annual Hawaii International
Conference on System Science, Hawaii, 2004, pp. 5-8.
[22] M. Ilyas and I. Mahgoub, “Handbook of Sensor Networks:
Compact Wireless and Wired Sensing Systems,” CRC
Press, Boca Raton, 2005.
[23] S. Tarannum, S. Aravinda, L. Nalini, K. R. Venugopal
and L. M. Patnaik, “Routing Protocol for Lifetime Maxi-
mization of Wireless Sensor Networks,” International
Journal on Information Processing, Vol. 1, No. 2, 2007,
pp. 58-67.
[24] T. Nieberg, S. Dulman, P. Havinga, L. Hoesel and J. Wu,
“Collaborative Algorithms for Communication in Wire-
less Sensor Networks,” University of Twente, Nederlands,
[25] J. N. A. Karaki and A. E. Kamal, “Routing Techniques in
Wireless Sensor Networks: A Survey,” IEEE on Wireless
Communications, Vol. 11, No. 6, 2004, pp. 6-28.
[26] M. Yebari, T. Addali, A. Z. Sadouq and M. Essaaidi,
“Energy Conservation Challenges in Wireless Sensor
Networks: A State-of-The-Art Study,” International Jour-
nal on Information and Communication Technologies,
Vol. 1, No. 2, 2008, pp. 29-35.
[27] Y. Yao and J. Gehrke, “The Cougar Approach to In-
Network Query Processing in Sensor Networks,” SIG-
MOD Record, September 2002.
[28] S. Tarannum, S. Srividya, D. S. Asha and K. R. Venugopal,
“Dynamic Hierarchical Communication Paradigm for
Wireless Sensor Networks: A Centralized, Energy Effi-
cient Approach,” Wireless Sensor Networks, Vol. 1, No. 4,
2009, pp. 340-349.
[29] S. Tarannum, V. Anitha, A. Priya, K. R. Venugopal and
L. M. Patnaik, “Self-Healing AntChain for Increasing
Lifespan in Wireless Sensor Networks,” International
Engineering and Technology (IETECH) Journal of Com-
munication Techniques, Vol. 2, No. 4, 2008, pp. 239-246.
[30] A. Vargas, “OMNET ++ Discrete Event Simulator Sys-
tem,” Version 2.3 Edition, 2003.