Designing an Energy Efficient Medium Access Control for Wireless Sensor Networks Using Short Listening Time and Clusterization

Abstract

WSN is composed of small, low-power, and resource-constrained SNs that collaborate to monitor physical environments and transmit collected data wirelessly. Due to the limited energy resources of SNs, energy management techniques are essential to prolong the network's lifespan. Although energy saving in WSN may be carried out at several stages of the TCP/IP protocol suite, energy conservation at the MAC layer is discovered to be the most efficient one due to its capacity of directly controlling the radio. To address the energy efficiency issue in WSNs, researchers have proposed various MAC protocols, each focusing on specific aspects and trade-offs. By allowing SNs to occasionally turn off their radios and go to sleep, SMAC helps SNs decrease the main cause of energy waste known as idle listening. And ESMAC adjusts duty-cycle in accordance with changes in the amount of energy left in the nodes; they set an energy threshold and weigh the amount of energy left in each node to determine the duty-cycle. Even though a lot of research has been done, still it is not enough to save nodes energy to extend lifetime of the networks. Therefore, to ensure a long-lived network of WSNs, we are in need of a MAC protocol that is able to improve energy efficiency by maximizing sleep duration, and minimizing idle listening. This study proposes SLT and clusterization for designing energy efficient MAC protocol. In SLT, we determine the sleeping time of a node based on the residual energy of the nodes so that it does not listen for a long time when it has less energy. In clusterization, after nodes form a cluster based on their distance from each other, the amount of energy they have is measured to select cluster head. So, the simulation results indicated that the EES-MAC improves the average throughput by 5.89% and 95.28%, over ESMAC and SMAC. The simulation findings demonstrate that EES-MAC could successfully improve network performance when used in conjunction with SLT and the clusterization adjustment method by reducing network latency, increasing network throughput, and node energy efficiency.