Multi-criteria-Based Mobile Hotspot Selection in IoT-Based Highly Dense Network

Abstract

One of the major challenges in Wireless Sensor Networks-based internet of things networks is to conserve energy and extend network lifetime. In order to provide green computing, energy-saving is necessary for both unchangeable limited battery sensors and energy outsourced devices such as smartphones. In this research work, a scenario is considered, in which sensors are deployed in an area where smart devices (mobile elements) are present in abundance. Only Mobile Elements can take the role of cluster head (CH). Mobile element is selected as CH based on the weighted average of multiple attributes. Then the cluster heads send data to the cloud network. Cluster members select their CH in a distributed manner and send an acknowledgment message to the respective CH. CH updates its member list and prepares a time division multiple access (TDMA) schedule. On the basis of this TDMA schedule, CH transmits the data of energy critical nodes first. As mobile elements are motile, therefore, proposed work manages mobility to avoid packet loss and end-to-end delay. Simulation and results prove that proposed energy-efficient multi-criteria-based clustering improves network lifetime, average throughput, and minimizes end-to-end delay and communication cost.