1 CHAPTER 1 INTRODUCTION TO WIRELESS SENSOR NETWORK 1.1 INTRODUCTION Recent advances in wireless communications and electronics have enabled the development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate untethered in short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicating components, leverage the idea of sensor networks (Akyildiz et al 2002). Sensor networks represent a significant improvement over traditional sensors. The past few years have witnessed increased interest in the potential use of Wireless Sensor Network (WSN) in a wide range of applications and it has become a hot research area. Sensor nodes in WSN are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue (Pottie & Kaiser 2000). To prolong the networks lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. Optimizing energy consumption is the main concern for designing and planning the operation of the WSN. Clustering technique is one of the methods utilized to extend lifetime of the network by applying data aggregation and balancing energy consumption among sensor nodes of the network. In clustered networks, Sensor nodes in each cluster transmit their data to the respective Cluster Head (CH) and the CH aggregates data and
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CHAPTER 1
INTRODUCTION TO WIRELESS SENSOR NETWORK
1.1 INTRODUCTION
Recent advances in wireless communications and electronics have
enabled the development of low-cost, low-power, multifunctional sensor
nodes that are small in size and communicate untethered in short distances.
These tiny sensor nodes, which consist of sensing, data processing, and
communicating components, leverage the idea of sensor networks
(Akyildiz et al 2002). Sensor networks represent a significant improvement
over traditional sensors. The past few years have witnessed increased interest
in the potential use of Wireless Sensor Network (WSN) in a wide range of
applications and it has become a hot research area.
Sensor nodes in WSN are usually battery-operated devices, and
hence energy saving of sensor nodes is a major design issue (Pottie & Kaiser
2000). To prolong the networks lifetime, minimization of energy consumption
should be implemented at all layers of the network protocol stack starting
from the physical to the application layer including cross-layer optimization.
Optimizing energy consumption is the main concern for designing and
planning the operation of the WSN. Clustering technique is one of the
methods utilized to extend lifetime of the network by applying data
aggregation and balancing energy consumption among sensor nodes of the
network. In clustered networks, Sensor nodes in each cluster transmit their
data to the respective Cluster Head (CH) and the CH aggregates data and
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forwards them to a central base station (Gupta & Younis2003). More energy
is drained from Cluster Heads (CHs) due to message transmission over long
distances (CHs to the base Station) compared to other sensor nodes in the
cluster (Bandyopadhyay & Coyle 2003). It is essential to avoid quick
depletion of cluster heads.
The optimal election and re-election of CHs, and cluster
maintenance are the main issues to be addressed in designing of clustering
algorithms. Hence this thesis proposes methods for selection of cluster head
based on meta-heuristic algorithms like Firefly Algorithms (FA), Artificial
Bee Colony Algorithms (ABC), Particle Swarm Optimization (PSO) and
Shuffled frog leap algorithms (SFLA) for increasing the lifetime of the WSN.
The following sections will discuss the architecture of WSN and related
issues and challenges in it (Michael et al 2000).
1.2 WSN ARCHITECTURE
The basic block diagram of a wireless sensor node is presented in
Figure 1.1. It is mainly made up four basic components (Martinez et al 2004):
Sensing unit
Processing unit
Transceiver unit
Power unit
1.2.1 Sensing Unit
Sensing units are usually composed of two subunits: sensors and
Analog to Digital Converters (ADCs). Sensor is a device which is used to
translate physical phenomena to electrical signals. Sensors can be classified as
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either analog or digital devices. There exists a variety of sensors that measure
environmental parameters such as temperature, light intensity, sound,
magnetic fields, image, etc. The analog signals produced by the sensors based
on the observed phenomenon are converted to digital signals by the ADC and
then fed into the processing unit.
Figure 1.1 Architecture of a Wireless Sensor Node
1.2.2 Processing Unit
The processing unit mainly provides intelligence to the sensor
node. The processing unit consists of a microprocessor, which is responsible
for control of the sensors, execution of communication protocols and signal
processing algorithms on the gathered sensor data.
1.2.3 Transceiver Unit
The radio enables wireless communication with neighboring nodes
and the outside world. It consists of a short range radio which usually has
single symmetric channel. There are several factors that affect the power
Computing unit Memory
Communication unit
Sensing unit
Micro controller
Battery
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consumption characteristics of a radio, which includes the type of modulation
scheme used, data rate, transmit power and the operational duty cycle. Similar
to microcontrollers, transceivers can operate in transmit, receive, idle and
sleep modes. An important observation in the case of most radios is that,
operating in idle mode results in significantly high power consumption,
almost equal to the power consumed in the receive mode. Thus, it is important
to completely shut down the radio rather than set it in the idle mode when it is
not transmitting or receiving due to the high power consumed. Another
influencing factor is that, as the radio's operating mode changes, the transient
activity in the radio electronics causes a significant amount of power
dissipation. The sleep mode is a very important energy saving feature in WSN
(Culler et al 2004).
1.2.4 Battery
The battery supplies power to the complete sensor node. It plays a
vital role in determining sensor node lifetime (Gautam et al 2009). The
amount of power drawn from a battery should be carefully monitored. Sensor
nodes are generally small, light and cheap, the size of the battery is limited
(Freris et al 2010).
1.3 APPLICATION OF WIRELESS SENSOR NETWORK
WSN may consist of many different types of sensors such as
seismic, low sampling rate magnetic, thermal, visual, infrared, acoustic and
radar. They are able to monitor a wide variety of ambient conditions that
include temperature, humidity, vehicular movement, lightning condition,
pressure, soil makeup, noise levels, the presence or absence of certain kinds
of objects, mechanical stress levels on attached objects, and the current
characteristics such as speed, direction and size of an object. Some of the
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major WSN applications are explained in the following sections. (Jennifer
Yick et al 2008).
1.3.1 Monitoring
Monitoring applications include indoor/outdoor environmental
monitoring, health and wellness monitoring, power monitoring, inventory
location monitoring, factory and process automation, and seismic and
structural monitoring.
1.3.2 Tracking
Tracking applications include tracking objects, animals, humans,
and vehicles and categorize the applications into military, environment,
health, home and other commercial areas.
1.3.3 Military Applications
The rapid deployment, self-organization and fault tolerance
characteristics of sensor networks make them a very promising sensing
technique for military command, control, communications, computing,
intelligence, surveillance, reconnaissance and targeting systems. Military
sensor networks could be used to detect and gain as much information as
possible about enemy movements, explosions, and other phenomena of
interest, such as battlefield surveillance, nuclear, biological and chemical
attack detection and reconnaissance.
1.3.4 Environmental Applications
WSN have been deployed for environmental monitoring, which
involves tracking the movements of small animals and monitoring
environmental conditions that affect crops and livestock. In these
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applications, WSN collect readings over time across a space large enough to
exhibit significant internal variation. Other applications of WSN are chemical
and biological detection, precision agriculture, biological, forest fire
detection, volcanic monitoring, meteorological or geophysical research, flood
detection and pollution study (Welsh & Lorincz 2007).
1.3.5 Healthcare Applications
WSN based technologies such as Ambient Assisted Living and
Body Sensor Networks provide dozens of solutions to healthcare's biggest
challenges such as an aging population and rising healthcare costs. Body
sensor networks can be used to monitor physiological data of patients. They
can provide interfaces for disabled, integrated patient monitoring. It can
monitor and detect elderly people's behavior, e.g., when a patient has fallen.
These small sensor nodes allow patients a greater freedom of movement and
allow doctors to identify pre-defined symptoms earlier on. The small installed
sensor can also enable tracking and monitoring of doctors and patients inside
a hospital. Each patient has small and lightweight sensor nodes attached to
them, which may be detecting the heart rate and blood pressure. Doctors may
also carry a sensor node, which allows other doctors to locate them within the
hospital.
1.3.6 Home Applications
With the advance of technology, the tiny sensor nodes can be
embedded into furniture and appliances, such as vacuum cleaners, microwave
ovens and refrigerators. They are able to communicate with each other and the
room server to learn about the services they offer, e.g., printing, scanning and
faxing. These room servers and sensor nodes can be integrated with existing
embedded devices to become self-organizing, self-regulated and adaptive
systems to form a smart environment.
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1.3.7 Traffic Control
Traffic conditions can be easily monitored and controlled at peak
times by WSN. Temporary situations such as road works and accidents can be
monitored. Further, the integration of monitoring and management operations,
such as signpost control, is facilitated by a common WSN infrastructure.
1.4 CHALLENGES IN SENSOR NETWORKS
The features and challenges of WSN deployment can be summed
up as follows:
Wireless ad hoc nature: A fixed communication infrastructure
does not exist. The shared wireless medium puts forward
additional restrictions on the communication between the nodes
and poses new problems like asymmetric and unreliable links.
But, it provides with the broadcast advantage i.e. a packet
transmitted by a node to the other can be received by all
neighbours of the transmitting node.
Mobility and topology changes: WSN might involve dynamic
scenarios. New nodes might join the network and the existing
nodes might either move through the network or even out of it.
Nodes might cease to function properly and the surviving nodes
may go in or out of transmission radius of other nodes. WSN
applications have to be robust against node failure and dynamic
topology.
Energy limitations: Nodes in majority of the WSN have limited
energy. The basic scenario includes a topology of sensor nodes
and a restricted number of more power efficient base stations.
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Maintenance or recharging of the batteries on sensor nodes is
not possible after deployment. Communication tasks consume
maximum power available to sensor nodes, and in order to
ensure sustained long-term sensing process, communication
tasks should be exercised carefully (Heinzelman et al 2000).
Physical distribution: Each node in a WSN is a self-sufficient
computational unit that communicates with its neighbour nodes
through messages. Data is scattered throughout the nodes in the
network and can be collected at the base station only with high
communication expenses. As a result, algorithms that require
global information from the complete network become very
costly. Thus, restrained distributed algorithms are highly desired
(Krishnamachari et al 2002).
Design and Deployment: WSN are used in enormously diverse
applications ranging from monitoring a biological system
through tissue implanted sensors to monitoring forest fire
through air-dropped sensors. In some applications, the sensor
nodes need to be placed accurately at predetermined locations,
whereas in some others, such positioning is needless or
unreasonable. Sensor network design aspires at determining the
type, quantity and location of sensor nodes to be positioned in
an environment so as to get an absolute knowledge of its
functioning situations (Murugunathan et al 2005).
Localization: Node localization intends at creating location
awareness in all the deployed sensor nodes. Location
information is used to identify and record events or to route
packets by means of geometric aware routing. Moreover, the
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location itself is often the data that needs to be sensed.
Localization methods that make use of time of arrival of signals
from various base stations are usually used in WSN.
Data Aggregation and Sensor Fusion: Sensor fusion is the
method of combining the data derived from multiple sources
such that either the resultant information is in some way better
than with the individual sources or the message overhead of
sending individual sensor readings to the base station is
lessened. Due to a large-scale deployment of sensors, a huge
data is generated and hence its efficient collection is a critical
matter.
Energy Aware Routing and Clustering: A conservative approach
in using energy is important in WSN because replacing or
recharging the batteries on the nodes may be unreasonable,
costly or hazardous. In several applications, network life
expectancy of a few months or years is wanted. Routing means
determination of a path for a message from a source node to a
destination node (Vidhyapriya & Vanathi 2007). In proactive
routing methods, routing tables are created and stored regardless
of when the routes are used. In reactive routing methods, routes
are computed as necessary. In densely deployed networks,
routing tables take an enormous amount of memory, and hence,
hybrids of proactive and reactive methods are suitable for such
networks. Another probable solution is to cluster the network
into hierarchies (Chang & Tassiulas 2004).
Security: Wireless links in WSN are vulnerable to
eavesdropping, impersonating, message distorting etc. Poorly
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protected nodes that are included in the hostile environments
can be effortlessly trapped. Administration becomes more
difficult due to dynamic topology.
Quality of Service (QoS) Management: QoS refers to an
assurance by the network to provide a set of measurable service
attributes to the end-to-end users or applications in terms of
fairness, delay, jitter, available bandwidth, and packet loss.
While maximizing network resource exploitation, a network has
to provide the QoS. To achieve this objective, the network is
required to analyze the application requirements and deploy
various network QoS mechanisms (SanatanMohanty 2010).
This thesis majorly focuses energy aware clustering through cluster
head selection to improve the network lifetime by increasing the time period
of First Node Death (FND) and Last Node Death (LND). The following
sections describe cluster model and its attributes, advantages and challenges.
1.5 CLUSTERING MODELS
Several WSN applications require only an aggregate value to be
reported to the observer. In this case, sensors in different regions of the field
can collaborate to aggregate their data and provide more accurate reports
about their local regions. In order to support data aggregation through
efficient network organization, nodes can be partitioned into a number of
small groups called ‘clusters’. Each cluster has a coordinator, referred to as
‘cluster head’, and a number of ‘member nodes’ or non cluster head nodes.
Clustering results in a two level hierarchy in which Cluster Heads (CHs) form
the higher level while member nodes form the lower level. Figure 1.2
illustrates clustering in WSN (Lee et al 2011). Data moves from a lower
clustered layer to a higher one. Data in this case as well, hops from one node
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to another node, but while it hops from one layer to the other, it covers longer
distances and moves the data more rapidly to the base station as compared to
the multi-hop model. The latency in this model is supposedly much lower
than that in the multi-hop model. Clustering makes available inherent
optimization capabilities at the cluster heads, which results in a more efficient
and well structured network topology. This model is certainly more suitable
than the one-hop and the multi-hop model (Ghiasi et al 2004).
Figure 1.2 Example of Clustering Based Model
Base
Station
Cluster Head
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The member nodes report their data to the respective CHs. As
shown in above Figure 1.2, the clusters are created where each CHs aggregate
the data and send them to the central base through other CHs. Because CHs
often transmit data over longer distances, they lose more energy compared to
member nodes. The network may be re-clustered periodically in order to
select energy abundant nodes to serve as CHs, thus distributing the load
uniformly on all the nodes. Besides achieving energy efficiency, clustering
reduces channel contention and packet collisions, resulting in better network
throughput under high load. Clustering has been shown to improve network
lifetime, a primary metric for evaluating the performance of a sensor network.
Although there is no unified definition of ‘network lifetime’, as this concept
depends on the objective of an application, common definitions include the
time until the first node in the network depletes its energy and the time until a
node is disconnected from the base station. In studies where clustering
techniques were primarily proposed for energy efficiency purposes where the
network lifetime was significantly prolonged (Bhaskar et al 2008).
Clustering has advantages and disadvantages. Clusters can decrease
the power consumption of a WSN, thus boosting the lifetime of the network.
Nodes inside a cluster are only required to broadcast to its CH, and this
decreases each node’s connection variety. This also permits the spatial reuse
of communication channels while decreasing collisions. By aggregating data,
the number of messages that flow through the network can be lowered.
Another important feature of clustering is the rotation cluster head roles
among the sensor nodes in order to not drain the battery of a single node (as
the CH consumes the most energy among all nodes in a cluster).
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1.6 CLASSIFICATION OF CLUSTERING ATTRIBUTES IN
WSN
This section describes various clustering attributes such as cluster
characteristics, CH characteristics and clustering process in WSN (Mohamed
& Abbasi 2007).
1.6.1 Cluster Characteristics
Variability of Cluster Count: Based on variability of cluster
count, clustering schemes can be classified into two types: fixed
and variable ones. In the former scheme, the set of cluster head
are predetermined and the number of clusters is fixed. However,
the number of clusters is variable in the latter scheme, in which
CHs are selected, randomly or based on some rules, from the
deployed sensor nodes.
Uniformity of Cluster Sizes: In the light of uniformity of cluster
sizes, clustering routing protocols in WSN can be classified into
two classes: even and uneven ones, respectively with the same
size clusters and different size clusters in the network. In
general, clustering with different sizes clusters is used to
achieve more uniform energy consumption and avoid energy
hole.
Intra-Cluster Routing: According to the methods of inter-cluster
routing, clustering routing manners in WSN also include two
classes: single-hop intra-cluster routing methods and multiple-
hop ones. For the manner of intra-cluster single-hop, all
Member Nodes (MNs) in the cluster transmit data to the
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corresponding CH directly. Instead, data relaying is used when
MNs communicate with the corresponding CH in the cluster.
Inter-Cluster Routing: Based on the manners of inter-cluster
routing, clustering routing protocols in WSN include two
classes: single-hop inter-cluster routing manners and multiple-
hop ones. For the manner of inter-cluster single-hop, all CHs
communicate with the BS directly. In contrast to it, data
relaying is used by CHs in the routing scheme of inter-cluster
multiple-hop.
1.6.2 Cluster Head Characteristics
Existence: Based on whether there exist CHs within a cluster,
clustering schemes can be grouped into cluster head based and
non-cluster head based clustering. In the former schemes, there
exist at least one CH within a cluster, but there aren’t any CHs
within a cluster in the latter schemes, such as some chain based
clustering algorithms.
Difference of Capabilities: Based on uniformity of energy
assignment for sensor nodes, clustering schemes in WSN can be
classified into homogeneous or heterogeneous ones. In
homogeneous schemes, all the sensor nodes are assigned with
equal energy, computation, and communication resources and
CHs are designated according to a random way or other criteria.
However, sensor nodes are assigned with unequal capabilities in
heterogeneous environment, in which the roles of CHs are pre-
assigned to sensor nodes with more capabilities.
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Mobility: According to the mobility attributes of CHs,
clustering approaches in WSN also can be grouped into mobile
and stationary manners. In the former manners, CHs are mobile
and membership dynamically change, thus a cluster would need
to be continuously maintained. Contrary to it, CHs are
stationary and can keep a stable cluster, which is easier to be
managed. Sometimes, a CH can travel for limited distances to
reposition itself for better network performance (Mohamed &
Abbasi 2007).
Role: A CH can simply act as a relay for the traffic generated by
the sensor nodes in its cluster or perform aggregation/fusion of
collected information from sensor nodes in its cluster.
Sometime, a cluster head acts as a sink/BS that takes actions
based on the detected phenomena or targets (Mohamed &
Abbasi2007). It is worth mentioning, sometimes a CH acts in
more than one role.
1.6.3 Clustering Process
Control Manners: Based on control manners of clustering,
clustering routing methods in WSN can be grouped into
centralized, distributed and hybrid ones. In centralized methods,
a sink or CH requires global information of the network or the
cluster to control the network or the cluster. In distributed
approaches, a sensor node is able to become a CH or to join a
formed cluster on its own initiative without global information
of the network or the cluster. Hybrid schemes are composed of
centralized and distributed approaches. In this environment,
distributed approaches are used for coordination between CHs,
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and centralized manners are performed for CHs to build
individual clusters.
Execution Nature: Considering the execution nature of cluster
formation, clustering modes in WSN can be classified into two
classes: probabilistic or iterative ones. In probabilistic
clustering, a probability assigned to all sensor nodes is used to
determine the roles of the sensor nodes. In other words, each
sensor node can independently decide on its own roles.
Nevertheless, every node must wait until a certain number of
iterations is achieved or for certain nodes to decide their roles
before making a decision in iterative clustering manner.
Convergence Time: Considering the convergence time,
clustering methods in WSN can be grouped into variable and
constant convergence time ones. The convergence time depends
on the number of nodes in the network in variable convergence
algorithms, which accommodate well to small-scale networks.
After a fixed number of iterations, constant convergence time
algorithms certainly converge regardless of the scale of the
networks.
Parameters for CH Election: Based on the parameters used for
CH election, clustering approaches can be categorized as
deterministic, adaptive, and random ones. In deterministic
schemes, special inherent attributes of the sensor nodes are
considered, such as the identifier (ID), number of neighbors
they have. In adaptive manners, CHs are elected from the
deployed sensor nodes with higher weights, which includes such
as residual energy, communication cost, and etc. In random
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modes, mainly used in secure clustering algorithms, CHs are
elected randomly without regard to any other metrics like
residual energy, communication cost, etc (Liang et al 2009).
1.6.4 Advantages and Objectives of Clustering
Clustering protocols have a variety of advantages, such as more
scalability, less load, less energy consumption and more robustness. Some of
the advantages and objectives of WSN clustering are as follows:
More Scalability: In s clustering routing scheme, sensor nodes
are divided into a variety of clusters with different assignment
levels. The CHs are responsible for data aggregation,
information dissemination and network management, and the
member nodes for events sensing and information collecting in
their surroundings. Clustering topology can localize the route
set up within the cluster and thus reduce the size of the routing
table stored at the individual sensor nodes (Mohamed & Abbasi
2007). Compared with a flat topology, this kind of network
topology is easier to manage, and more scalable to respond to
events in the environment (Akkaya & Younis 2005).
Data Aggregation/Fusion: Data aggregation/fusion, which is the
process of aggregating the data from multiple nodes to eliminate
redundant transmission and provide fused data to the BS, is an
effectual technique for WSN to save energy (Rajagopalan &
Varshney2006). The most popular data aggregation/fusion
method is clustering data aggregation, in which each CH
aggregates the collected data and transmits the fused data to the
BS. Usually CHs are formed a tree structure to transmit
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aggregated data by multihopping through other CHs which
results in significant energy savings (Ozdemir & Xiao 2009).
Less Load: Since sensors might generate significant redundant
data, data aggregation or fusion has emerged as an important
tenet and objective in WSN. The main idea of data aggregation
or fusion is to combine data from different sources to eliminate
redundant data transmissions, and provide a rich and multi-
dimensional view of the targets being monitored. Many
clustering routing schemes with data aggregation capabilities
require careful selection for clustering approach. For clustering
topology, all cluster members only send data to CHs, and data
aggregation is performed at the CHs, which help to dramatically
reduce transmission data and save energy. In addition, the routes
are set up within the clusters which thus reduce the size of the
routing table stored at the individual sensor nodes (Akkaya &
Younis 2005).
Less Energy Consumption: In clustering routing scheme, data
aggregation helps to dramatically reduce transmission data and
save energy. Moreover, clustering with intra-cluster and inter-
cluster communications can reduce the number of sensor nodes
performing the task of long distance communications, thus
allowing less energy consumption for the entire network. In
addition, only CHs perform the task of data transmission in
clustering routing scheme, which can save a great deal of energy
consumption.
More Robustness: Clustering routing scheme makes it more
convenient for network topology control and responding to
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network changes comprising node increasing, node mobility and
unpredicted failures, etc. A clustering routing scheme only
needs to cope with these changes within individual clusters, thus
the entire network is more robust and more convenient for
management. In order to share the CH responsibility, CHs are
generally rotated among all the sensor nodes to avoid the single
point of failure in clustering routing algorithms.
Latency Reduction: When a WSN is divided into clusters, only
CHs perform the task of data transmissions out of the cluster.
The mode of data transmissions only out of the cluster helps
avoiding collisions between the nodes. Accordingly latency is
reduced. Furthermore, data transmission is performed hop by
hop usually using the form of flooding in flat routing scheme,
but only CHs perform the task of data transmission in clustering
routing scheme, which can decrease hops from data source to
the BS, accordingly decrease latency.
Load Balancing: Load balancing is an essential consideration
aiming at prolonging the network lifetime in WSN. Even
distribution of sensor nodes among the clusters is usually
considered for cluster construction where CHs perform the task
of data processing and intra-cluster management. In general,
constructing equal-sized clusters is adopted for prolonging the
network lifetime since it prevents the premature energy
exhaustion of CHs. Besides, multi-path routing is a method to
achieve load balancing.
Energy Hole Avoidance: Generally, multi-hop routing is used to
deliver the collected data to a sink or a BS. In those networks,
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the traffic transmitted by each node includes both self-generated
and relayed traffic. Regardless of MAC protocols, the sensor
nodes closer to the BS have to transmit more packets than those
far away from the BS (Li & Mohapatra 2007). As a result, the
nodes closer to the BS to deplete their energy first, leaving a
hole near the BS, partitioning the whole network, and
preventing the outside nodes from sending information to the
BS, while many remaining nodes still have a plenty of energy.
This phenomenon is called energy hole (Tran-Quang & Miyoshi
2010). Mechanisms of energy hole avoidance, i.e., energy
consumption balancing, can be classified into three groups:
node deployment, load balancing, as well as energy mapping
and assigning (Ishmanov et al 2011). Especially, uneven
clustering is one of the methods of load balancing. In this
method, a smaller cluster radius near the sink and a larger
cluster radius away from the sink are defined respectively,
hence the energy consumption of processing data in inter-cluster
is less for cluster with smaller radius, and thus more energy can
be used to relay data from remote nodes (Liu et al 2011). On the
other hand, it is not easy to analyze the optimization of cluster
radius theoretically (Li et al 2005).
Maximizing of the Network Lifetime: Network lifetime is an
inevitable consideration in WSN, because sensor nodes are
constrained in power supply, processing capability and
transmission bandwidth, especially for applications of harsh
environments. Usually it is indispensable to minimize the
energy consumption for intra-cluster communication by CHs
which are richer in resources than Ordinary Nodes (ONs).
Besides, sensor nodes that are close to most of the sensor nodes
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in the clusters should be prone to be CHs. Additionally, the aim
of energy-aware idea is to select those routes that are expected
to prolong the network lifetime in inter-cluster communications,
and the routes composed of nodes with higher energy resources
should be preferred (Chong & Kumar 2003).
1.7 CHALLENGES IN CLUSTERING ALGORITHMS
Clustering schemes play an important role in WSN. This can
effectively improve the network performance (Olutayoboyinbode et al 2010).
There are several key limitations in clustering schemes of WSN. These are
following:
Limited Energy: Wireless sensor nodes are small size battery
operated sensors, so they have limited energy storage. It is not
practicable to recharge or replace their batteries after
exhaustion. The clustering algorithms are more energy efficient
compared to the direct routing algorithms. This can be achieved
by balancing the energy consumption in sensor nodes by
optimizing the cluster formation, periodically re-electing CHs
based on their residual energy, and efficient intra-cluster and
inter-cluster communication.
Network Lifetime: The energy limitation on nodes results in a
limited network lifetime for nodes in a network. Clustering
schemes help to prolong the network lifetime of WSN by
reducing the energy usage in the communication within and
outside clusters.
Limited Abilities: The small physical size and small amount of
stored energy in a sensor node limit many of the abilities of
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nodes in terms of processing, memory, storage, and
communication.
Secure Communication: The ability of a WSN to provide secure
communication is ever more important when considering these
networks for military applications (Akyildiz et al 2002). The
self-organization of a network has a huge dependence on the
application it is required for. An establishment of secure and
energy efficient intra-cluster and inter-cluster communication is
one of the important challenges in designing clustering
algorithms since these tiny nodes when deployed are unattended
to in most cases.
Cluster formation and CH selection: Cluster formation and CHs
selection are two of the important operations in clustering
algorithms. Energy wastage in sensors in WSN due to direct
transmission between sensors and a base station can be avoided
by clustering the WSN. Clustering further enhances scalability
of WSN in real world applications. Selecting optimum cluster
size, election and re-election of CHs, and cluster maintenance
are the main issues to be addressed in designing of clustering
algorithms. The selection criteria to isolate clusters and to
choose the CHs should maximize energy utilization.
Synchronization: When considering a clustering scheme,
synchronisation and scheduling will have a considerable effect
on the overall network performance. Slotted transmission
schemes such as TDMA allow nodes to regularly schedule sleep
intervals to minimize energy used. Such schemes require
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synchronization mechanisms to setup and maintain the
transmission schedule.
Data Aggregation: Data aggregation eradicates duplication of
data. In a large network, there are often multiple nodes sensing
similar information. Data aggregation allows differentiation
between sensed data and useful data. Many clustering schemes
providing data aggregation capabilities must carefully select a
suitable clustering approach (Heinzelman et al 2000).
Repair Mechanisms: Due to the nature of WSN, they are often
prone to node mobility, node death, delay and interference. All
of these situations can result in link failure. When designing
clustering schemes, it is important to look for mechanisms that
ensure link recovery and reliable data communication (Gupta &
Younis 2003).
This proposed work in this thesis, addresses issues and solutions
related to Cluster formation and CH selection, Limited energy constraints and
Network Lifetime improvement using meta-heuristic optimization algorithms.
The following section introduces various meta-heuristic optimization
algorithms for the selection of efficient cluster heads.
1.8 META-HEURISTIC ALGORITHM
Heuristic algorithms typically intend to find a good solution to an
optimization problem by ‘trial-and-error’ in a reasonable amount of
computing time. Here ‘heuristic’ means to ‘find’ or ‘search’ by trials and
errors. There is no guarantee to find the best or optimal solution, though it
might be a better or improved solution than an educated guess. Any
reasonably good solution, often suboptimal or near optimal, would be good
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enough for such problems. Broadly speaking, local search methods are
heuristic methods because their parameter search is focused on the local
variations, and the optimal or best solution can be well outside this local
region. However, a high-quality feasible solution in the local region of
interest is usually accepted as a good solution in many optimization problems
in practice if time is the major constraint.
Meta-heuristic algorithms are higher-level heuristic algorithms.
Here, ‘meta’ means ‘higher-level’ or ‘beyond’, so meta-heuristic means
literally to find the solution using higher-level techniques, though certain
trial-and-error processes are still used. Broadly speaking, meta-heuristics are
considered as higher-level techniques or strategies which intend to combine
lower-level techniques and tactics for exploration and exploitation of the huge
space for parameter search. In recent years, the word ‘meta-heuristics’ refers
to all modern higher-level algorithms(Xin-She Yang 2010), including Particle
