A Manhattan Distance approach for energy optimization in wireless sensor network

Performance of wireless sensor network in several fields has become a key parameter in the research area. Nowadays it is dependent on several factors and energy consumption is one of the important parameter. Various issues regarding the performance of the wireless sensor network (WSN) have been referred, out of which energy efficiency is one of the research issue. In order to monitor the environment more efficiently and for reducing the energy consumption it is a usual practice that the nodes are clustered into groups and clustering has become a valued process to effectively enhance the lifetime of WSNs. Nowadays different methods are used to select Cluster Heads (CHs) and in every round the CHs changes periodically in a cluster on the basis of maximum residual energy so the energy consumed by each node in a cluster become uniform and the network lifetime has been enhanced. The member sensor nodes of a cluster sends their data to their fixed CH called static clustering or on the basis of distance choice for non-CH to the nearest CH called dynamic clustering. While transmitting the data, the nodes may encounter some obstacles which resist them to transmit their data to the destination. In this paper, a distance-based optimization approach called Manhattan Distance (MD) is used to overcome this type of problem. The simulation results performed on MATLAB results that our proposed approach is more efficient than the other existing approaches.