Energy Saving in Wireless Sensor Network using Nodes Sleep/Active Strategy based on Bernoulli’s Probability Distribution

Abstract

Wireless sensor networks (WSNs) are used widely due to its monitoring and reporting capabilities from different environments includes military, environmental monitoring,medical systems and industrial applications. The limited energy source is considered as the main constraint in the WSN. Because the energy exhaustion of sensor nodes of WSN affects the overall operation of the WSN.So, an energy harvesting system is introduced to extend the operational lifetime of WSN after the nodes deployed in the field environment. In this work, Exponentially Weighted Moving-Average (EWMA) based energy harvesting is used in WSN to charge the battery of the nodes. Hereclusterbased routing is accomplished in WSN to reduce the energy consumption among the sensor nodes of the WSN. The network is divided into several clusters by using the Recurrent Self Organizing Map (RSOM).Moreover, the RSOM based sleep awake routing is enabled to preserve the energy consumption of the sensor nodes. The sleep awake scheduling of WSN is used to minimize the energy consumption of the WSN by switching the modes of node that is either active or sleep. Also focuses on energy saving in nodes in WSN using sleep/active strategy based on Bernoulli’s probability distribution. Hence, the RSOM-EWMA methodology is validated with the Markov chain model. The performance of the RSOM-EWMA methodology is analyzed in terms of number of alive nodes, number of dead nodes,energy consumption, throughput and total packet send. The performance of the RSOM-EWMA methodology is compared with RSOM without energy harvesting, RSOM only with sleep awake scheduling and NEEC.