Research Area:  Wireless Sensor Networks

Abstract:

Wireless Sensor Networks (WSNs) is composed of self-organizing and tiny nodes that can process and transmit the data over wireless medium. The energy conservation and effective energy utilization is a significant problem to be considered in WSN. Many previous cluster based solutions relied on routing protocols, considered the relationship between sensor nodes and cluster head. It might lead to the probability of nodes that are left without being a member of any of the clusters called as residual nodes. These residual nodes might decrease the networks lifetime. The resource-constrained sensor nodes have been included in specific networks for exploring their surroundings and processing through one or multiple gateways to send the gathered data. Gateways in the network could be done in a controlled manner to communicate between sensors of WSN that can be utilized for several applications. For improving the lifetime of WSN, several sinks are deployed optimally which has been considered as one of the efficient energy techniques. This work presents the latest structure which would comprise the mechanism of effective clustering along with Intra Cluster Gateway (IC-GW). IC-GW depends on Particle Swarm Optimization with Genetic Algorithm (PSO-GA) termed as ICGW-PSOGA for distance-communication and optimal SINK placement in WSNs. This Intra Gateway would gather the data from the heads of cluster and would be delivering to the SINK. The PSO-GA relied estimation of location algorithm has been initiated for finding the most excellent arrangement for the Gateway and SINK relied on the structure of the network. This algorithm has been extensively examined on several scenarios with the variation in the simulation duration; numerous sensor nodes and range of communication. The simulation results are promising and the obtained results are compared and validated with the earlier mechanisms.

Keywords:  

Author(s) Name:  Y. M. Raghavendra,U. B. Mahadevaswamy

Publisher name:  Springer

DOI:  10.1007/s11277-021-08247-z

Volume Information:  volume 119, pages 1009–1028 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s11277-021-08247-z