Maximum data extraction in energy constrained Wireless Sensor network using genetic algorithm

Wireless sensor networks (WSN) find wide applications in defense, environmental monitoring like temperature, pressure, vibration, moisture, industrial monitoring, medical monitoring, habitat surveillance etc. The application of WSN consists of small sensor nodes that are low-cost, low-power and multi-functional. The most important task of WSN is to send the collected data to the sink node and minimize the energy consumed during data transport. Usually the sensors used in the networks are energy limited and many energy constraints like uncertainties, unreliable wireless link, node failure, dynamic traffic load are present. So for any application it is necessary to consider the node constraints. The performance of wireless sensor network is affected mainly by the uncertainty present in the environment. There are various sources of uncertainty that may affect the sensor network´s operation in the real time applications. The uncertainty may be due to distance between the nodes, communication channel etc. Apart from minimizing energy consumption, another problem is maximizing the data extracted to the sink node. This paper proposes an optimization of distance uncertainty for maximizing the data extraction problem. Genetic algorithm is used to optimize this problem. The actual position of the node may differ from what sensor network planned to detect or the rate of energy consumption may deviate from the expected value. In this optimization model, the aim to route data to maximize the information that reaches the sink.