A Hybrid MCDM and Meta-heuristic-based Approach for Energy Efficient Routing in Wireless Sensor Networks

Author(s): Man Gun Ri, Ye Hyang Choe, Song Il Ko

The energy-constrained WSNs consist of sensor nodes to be discriminated by multi-criteria which are mutually contradictory each other. Therefore, developing a clustering scheme blending these multi-criteria comprehensively is of particular importance for energy-constrained WSNs. However, most of the latest works have focused on exploiting individual intelligent optimization algorithms and little effort has been made on integrating Multi-Criteria Decision Making (MCDM) approach with the meta-heuristic algorithm. In this paper, we propose a novel clustering scheme using Adaptive Fuzzy C-Means (AFCM) and an improved Ant Lion Optimization (ALO) approach. This scheme first divides the whole network into k clusters using the AFCM algorithm. After that, an improved ALO is applied for each cluster to select the optimal Cluster Head (CH) nodes. At this time, this ALO prescribes a new multi-criteria based fitness function based on the weights of six multi-criteria assigned by Fuzzy Cognitive Network Process (FCNP) and made compensation with Variable Weight Analysis (VWA). Extensive simulation results reveals that the proposed scheme achieves a superior energy consumption balance, thus extending the network lifetime up to 272.9%, 116.6% and 109.2% compared to LEACH, ALOC and K-LionER schemes, respectively.