Impact of two-level fuzzy cluster head selection model for wireless sensor network: An Energy efficient approach in remote monitoring scenarios

Abstract: The robust application of wireless sensor networks has increased during the past decade due to the potential use of wireless nodes in transmission of information by decreasing latency for surveillance and monitoring. The study proposes an Energy Efficient Dynamic Scenario (EEDS) for cluster head allocation for optimum balance in the energy consumption of the whole network that will prolong the lifetime of the network in an efficient manner. In this paper, a two-level fuzzy logic is proposed in choosing cluster head based on node localization and network traffic. In the upper decision making level called global level of qualification leads to better performance of the inference system based on all the above six fuzzy parameters for establishing an energy efficient network model. We develop an algorithm to calculate energy across the network if the source and destination is known. We evaluate the cost and benefit of the data fusion, in order to adaptively adjust whether fusion shall be performed for minimizing the total energy consumption when energy efficient node scheduling migrates from a particular node to another node. Simulation results show that EEDS gives the best performance with respect to network life time density and residual energy of the node.