Optimization and intelligent adaptation techniques on energy aware routing strategies in wireless sensor networks

Abstract

Wireless Sensor Networks (WSN) connects the physical and digital world using large number of sensor nodes. Due to the diversified range of advantages, the WSN has been employed in different real-time applications to monitor, detect and recognize different objects. An adaptive mechanism provides intelligent approach in changing environments. The mechanism should have the ability to learn and adapt with new situations. For the algorithm to be efficient, it must be able to generate a variety of solutions and need to explore the whole search space. Meta-heuristic algorithm helps in attaining clustering and it has better control over the network. Hence, an effective routing policy and adaptive clustering mechanism can save the network. The proposed Genetic Algorithm is used for global search among the network to obtain Cluster Head (CH) and the corresponding member nodes. Then, the final solution of Genetic Algorithm is given as initial solution to Tabu Search algorithm; to search locally around the nodes to find best solution to route the data Once sensor nodes are deployed in the field, all the sensor nodes tries to collect the neighbor information like energy, distance from sink node and identification number from the surrounding nodes. The base station broadcast the request information to all the sensor nodes in the network to send the basic information like ID of node, residual energy, location of sensor node and the number of neighbor sensor nodes. The sensor node sends the collected information to the base station. Once base station received the information from the sensor node; it selects best CHs using fitness value to reduce the energy consumption due to the communication to resourcefully increases the lifetime of the network. In this proposed method, the output of the Genetic Algorithm is used as input for Tabu Search algorithm to find the routing path in hierarchical network. newline