15-01-2013, 12:01 PM
Cluster based wireless sensor network routing using artificial bee colony algorithm
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Abstract
Due to recent advances in wireless communication
technologies, there has been a rapid growth in
wireless sensor networks research during the past few
decades. Many novel architectures, protocols, algorithms,
and applications have been proposed and implemented.
The efficiency of these networks is highly dependent on
routing protocols directly affecting the network life-time.
Clustering is one of the most popular techniques preferred
in routing operations. In this paper, a novel energy efficient
clustering mechanism, based on artificial bee colony
algorithm, is presented to prolong the network life-time.
Artificial bee colony algorithm, simulating the intelligent
foraging behavior of honey bee swarms, has been successfully
used in clustering techniques. The performance of
the proposed approach is compared with protocols based on
LEACH and particle swarm optimization, which are studied
in several routing applications.
Introduction
Wireless sensor networks (WSNs) consisting of a number
of distributed sensor nodes, have received much attention
in recent years [1–5]. They enable a wealth of distributive
application areas such as military, environmental monitoring,
healthcare services, chemical detection and seismic
measurements [6]. In any of these applications, nodes are
in sense of potential events which are occurring in their
sensing range.
They try to cooperate with each-other to transmit the
sensed data to an external base station. Due to their distributed
nature, performing a collaborative organization for
a robust communication is required.
The sensor nodes are easily configurable to many specific
applications [7]. However, they are powered only by
irreplaceable batteries with limited energy. Nevertheless,
their processors have limited processing power and the
communication channels used by the sensor nodes are
usually in low bandwidths. By considering these constraints
of sensor nodes, innovative techniques are highly
required to enable reliable communications. Not only
trustable communication is needed, but also the network
life-time should be long as much as possible in the applications
of WSNs
Artificial bee colony algorithm
Artificial bee colony algorithm, a swarm-based artificial
intelligence algorithm, is inspired by intelligent foraging
behavior of honey bees [24, 25]. In the ABC algorithm,
there are three bee groups in artificial bee colony:
onlookers, scouts, and employed bees where each bee
represents a position in the search space. When the network
consists n cluster-head sensors, the bees fly in the
search space with n dimensions. The ABC employs a
population of bees to find the cluster-heads. A bee
waiting on the dance area to determine to choose a food
source is an onlooker and a bee goes to the food source
visited by it previously is an employed bee. A bee who
carries out random search is called a scout. The position
of a food source represents a possible solution to the
optimization problem and the nectar amount of a food
source corresponds to the quality (fitness) of the associated
solution.
WSN routing protocols using artificial bee colony
algorithm
In the paper, the scenarios of WSNs routing protocols using
ABC algorithm are developed for the networks having no
global positioning system. The main purpose of the operations
of these protocols is to increase the network life-time
by maximizing the number of transferred data packages
with clustering. The clustering mechanism of the proposed
protocols is based on the clustering technique of LEACH
protocol where cluster heads perform data aggregation
processes of their clusters. Cluster heads use TDMA MAC
in intra-cluster communication and CDMA MAC communication
with the base station. The main operational
difference between the proposed protocols and LEACH is
the selection process of cluster heads (CH); clustering head
selection is performed by ABC algorithm in proposed
protocols while LEACH uses a random selection method.
The proposed network clustering protocol is based on a
centralized control algorithm that is implemented at the
base station. The base station is a node with unlimited
energy supply.
Proposed fitness function for ABC
ABC is used to determine the cluster heads where each
solution represents an array having k items in which every
item consists a sensor node. A sample of solution array is
shown in Table 2.
The ABC employs a population of bees to find the
cluster-heads where the bees fly in the search space with
k dimensions. Each employed bee corresponds to clusterheads
of sensor nodes.
The fitness of cluster heads selection is stated as a fitness
value, which is in inverse proportion to the amount of
energy consumption for a tour. If we mention that a certain
transfer time is required for a data package, energy consumption
is calculated by multiplying transmitting power
(Ps) and the time (t). Equations (10) and (11) gives the
minimum required energy for a cluster, derived from
Eq. (9). In the equations, m is the number of nodes, i is the
node index, di is the distance between ith node and clusterhead,
b is the distance between cluster-head and the base,
and E is the transfer energy of the cluster. Considering
multiple clusters, the calculation of minimum energy
consumption emphasizing the effect of distances will be as
in Eq. (12) expressing sum of the energy consumptions of
clusters. In the equation, j is the cluster index, dij is the
distance between ith node and jth cluster-head, and bj is the
distance between jth cluster-head and the base.
Improvement on the fitness function
Cluster-heads are responsible for fusing the data gathered
in the clusters and forwarding it to the base. Since more
computation and transferring messages to a distant base are
required at the cluster-head nodes, more energy is consumed
in these nodes than the others. Batteries of the nodes
often selected as cluster-heads can no longer be sufficient
to supply the required energy after breaking a critical level.
Therefore, the problem of rapid energy depletions in
cluster-head batteries is a matter of concern. Fitness calculation
is improved by taking into consideration of battery
levels of the sensor nodes. It is noticed that when a node
serves as a cluster-head frequently, its energy level
decreases sharply. In order to increase the network lifetime,
it is more appropriate to operate these nodes (having
weak batteries) as sensing devices instead of operating as
cluster-heads.
Simulation results
The performances of proposed protocols (CWA, ICWA
and ICWAQ) are tested with various parameter settings
using a parallel discrete model providing periodical data
transferring, developed in Matlab. In the simulations, the
network consists of 100 nodes randomly placed in a fixed
area of 500 m 9 500 m and a single base station located at
(250, 575 m) near middle edge of the field. It is assumed
that every node has a capability of communicating with
other nodes in the field as well as the base station. Free
space radio model with isotropic antennas is used as discussed
in [27] with parameters of radio electronics Eelec,
power amplifier of transmitter Eamp as taken in [11], packet
size K and communication frequency f for receiving and
transmitting units, where the values of these parameters are
taken as 50nJ/bit, 100pJ/bit/m2, 512Kbit, and 250 Kbit/s,
respectively.
Conclusion
The main goal of the monitoring applications of WSNs is
to gather information from the field periodically. Increasing
the total number of gathered signals during the network
life-time is essential to get maximum benefit from the
WSNs. In this paper, a novel energy saving routing method
providing longer network life time is achieved by gathering
greater amount of signals from the field. The proposed
protocol ICWAQ uses efficient and fast searching features
of the ABC algorithm to optimize clustering of the nodes in
the selection process of cluster-heads defining routing
gateways. The clustering success of the ABC algorithm is
compared with the protocols based on LEACH and PSO.