Nature inspired algorithm approach for the development of an energy aware model for sensor network

Abstract

The unique and strong characteristics of Wireless Sensor Network (WSN) have paved a way to many real time applications. Nevertheless, the WSN has their own set of challenges likewise data redundancy, resource constraints, security, packet errors and variable-link capacity etc. Among all, management of energy resource is of high importance as the efficient energy mechanism increases the lifespan of the network. Thereby providing good Quality of Service (QoS) demanded by the application. In WSN even though the energy is required for data acquisition (sensing), processing and communication, more energy are consumed during communication where transmission and retransmission of packets are quite often. In WSN data is transmitted from source to destination where at the destination site the data are analyzed using appropriate data mining techniques to convert data into useful information, and knowledge is extracted from that information to aid the user in efficient decision making. The transmission of data can be either through a single hop or via multiple hops. In single hop, a node is just a router where as in multi hop the node acts as both data originator and router. Thus, consuming more amount of energy and in a multi hop if any of the nodes fails it leads to many large retransmissions thus making a system highly susceptible for energy consumption. Many researchers have dedicated and devoted their time, energy and resources in order to come up with better solutions to answer this problem. This chapter is one such effort to provide a better solution to reduce the energy consumption of sensors. Here, the beauty of DBSCAN clustering technique has been fully exploited in order to develop a spatiotemporal relational model of sensor nodes, followed by the selection of representative subset using measure trend strategy and finally meeting the criteria for identifying the best optimal path for transmission of data using few nature inspired algorithms like Ant Colony Optimization (ACO), Bees Colony Optimization (BCO), and Simulated Annealing (SA).