Green computing for wireless sensor networks: Optimization and Huffman coding approach

Lifetime maximization has witnessed continuous attention from academia as well as industries right from the inception of Wireless Sensor Networks (WSNs). Recently, mobile sink, trajectory based forwarding and energy supply based node selection have been suggested in literature for optimizing residual energy of nodes. In the most of these approaches, energy consumption has been minimized focusing on the optimization of one particular parameter. The consideration of impact of more than one parameters on energy consumption is lacking in literature. In this context, this paper proposes Huffman coding and Ant Colony Optimization based Lifetime Maximization (HA-LM) technique for randomly distributed WSNs. In particular, ACO based multiple paths exploration and Huffman based optimal path selection consider the impact of two network parameters on energy consumption. The parameters include path length in terms of hop count and residual energy in terms of load of nodes of the path and the least energy node. The construction of multiple paths from source to the sink is mathematically derived based on the concept of two types of ants; namely, Advancing Ant (A-ANT) and Regressive Ant (R-ANT) in ACO. The optimal path is identified from the available multiple paths using Huffman coding. Analytical and simulation results of HA-LM are comparatively evaluated with the state-of-the-art techniques considering four performance metrics; namely, average residual energy, energy consumption, number of alive sensors and standard deviation of energy. The comparative performance evaluation attests the superiority of the proposed technique to the state-of-the-art techniques.