Sink relocation schemes for increasing the network lifetime of wireless sensor networks

Abstract

A Wireless Sensor Network (WSN) consists of a group of organized sensor elements often known as sensor nodes with limited amount of resource such as bandwidth, energy etc. The sensor nodes gather data and forwarded it to the sink node for appropriate further action. Thus, sink nodes are placed in such a way that the failures are avoided and resources are optimized. In this regard, many researchers have focused exclusively on improving the energy efficiency and paid least attention on parameters, which eventually result in network service performance degradation. This problem is addressed throughout the research work. A novel method namely Life Time and Reliability Concerned Optimal Sink Relocation (LTRC-ORC) has been introduced. The research aims in minimizing the energy utilization by the sensor nodes and improving the effective lifetime of the network. The proposed research work introduces Energy and Distance aware Clustering Technique that groups the data ready intermediate sensor/mobile nodes, which are closer to both sink node and the sensor node. Here Weighted K-Means algorithm is used to construct clusters among the nodes. and, Cat Swarm Optimization (CSO) algorithm is used to select the optimal cluster head that ensures the successful transmission of data fulfills the objective of the research namely, higher residual energy along with available bandwidth. This helps in identifying the most suitable position for the sink node in order to ensure better data transmission with optimized resource. The first method, LTRC-ORC doesn t consider the scenario with more number of nodes.The energy consumption would be high in case of transmission of more number of repeated sensed data. This problem isresolved in the proposed research method by introducing the novel research method namely Memory Concerned Hierarchical Clustering Framework (MCHCF). In the proposed research method, hierarchical clustering is introduced to optimize the energy conservation of nodes. Cluster head selection is improved by introducing the Hyb