Published Paper Details:

Abstract: Energy efficiency is one of most important parameter to consider while designing the wireless sensor networks. In this paper, we proposed robust energy management routing scheme based on two methods using artificial neural network (ANN). First is Most Significant Sensor Node prediction and another is Group Head selection. We discuss both of these problems in any WSN and seek possible solutions using neural networks, which will actually use to determine the shortest routing path in any WSN for minimizing the energy consumption. Usually WSNs life-time ends by having a single sensor node which uses all its energy and the other sensors consuming the remaining energy. This sensor (which is the cause of the networks end of lifetime) is most likely located in a very significant sensor node which always is in the routing path of many nodes to the base station. By predicting these significant nodes, it is possible to allocate tasks to the nodes in a more efficient way and thus increase the lifetime of the network. Selecting Group Heads amongst all the nodes is also energy conserving scheme for a WSN is proposed herewith. Sensor nodes are initially powered by batteries with full capacities. Each sensor collects data which are typically associated with other sensors in its neighborhood, and then the associated data is sent to the Base Station through Group Head for evaluating the tasks more efficiently. The following sections describe briefly energy saving scheme for network life time optimization, the experiments performed and results achieved in this work, and the main conclusions drawn from our experimental results.