Implementation of Multilevel Cluster Head Selection Algorithm with Enhanced Network Life Time
by Kirandeep Kaur
In recent years, wireless sensor networks because of their small size, minimum energy consumption, rugged and adaptive nature find a wide application in the field of wireless communication. In a WSN, sensors once placed have to adapt according to environment. These sensor nodes have limited energy as these are driven by battery source which is not rechargeable. The network lifetime of the wireless sensor network is limited due to this energy constraint. As the performance of the WSN is measured in terms of its network life time and consumption of energy, so there is need of algorithms which would tend to improve the lifetime of these networks. A solution to this problem is the use of clustering algorithms for WSNs. These clustering algorithms divide the whole network into number of clusters, each of these clusters has a cluster head. In this thesis, a new cluster head selection algorithm is introduced namely, “MULTILEVEL CLUSTER HEAD SELECTION ALGORITHM”. In this algorithm, selection of cluster head is done in three levels, first based on redundant nodes, second on the basis of energy and distance from sink and third on the basis of multilevel heterogeneous nature of sensor nodes and distance. The nodes are organised in the form of hexagonal grid which will ensures maximum network coverage with minimum nodes. This uses the selective data forwarding concept for data transmission from redundant nodes thus reducing the chances of data duplication and results in saving energy and enhancing network lifetime. A three level heterogeneous WSN is used in this algorithm for enhanced network life time. Using this algorithm, network lifetime is improved by factor of 24.03 %, while there is reduction in energy consumption by a factor of 24.921% and in packet collision by a factor of 40% as compared to existing algorithm.