Data Disseminated Energy-Efficient Clustering Algorithm For Avoid Load and Bandwidth Consumption In WSN Integrated IoT Platform

Abstract

Abstract In the current scenario, wireless sensor networks (WSNs) are embedded in the “Internet of Things (IoT) ” platform where sensor nodes automatically link and use the Internet to communicate and execute their activities. WSNs are well suited for the collection of long-term IoT representation environmental data. The WSNs includes wireless communication capabilities, computation process, and nodes with sensing capabilities. Data dissemination methods, power management, and many routing procedures have been mainly designed for WSNs integrated IoT platform. Also, we consider load and bandwidth consumption as an essential issue in our design. Hence, this paper introduces a data disseminated energy-efficient clustering algorithm using multiple parameter decision-making for selecting an optimal clustering algorithm. For the cluster head selection process, we consider different kinds of parameters such as Initial Energy, Average Energy of the Network, Energy Consumption Rate, and Residual Energy. By considering these factors, nodes are continually monitored, and the cluster header is selected according to the maximum energy value. The respective cluster members are chosen in the cluster coverage area using the swarming techniques. In other words, we used swarm techniques as a cluster head selection process to avoid load and bandwidth consumption. The excellence of the system is evaluated using simulation results which show that this introduced method is more effective in terms of preventing bandwidth and load consumption. In this context, we use network simulator 2 (NS2) to simulate different kinds of metrics such as a packet delivery ratio, network lifetime, and energy consumption.