An energy-adaptive clustering method based on Taguchi-based-GWO optimizer for wireless sensor networks with a mobile sink

In wireless sensor networks (WSNs), balancing energy consumption is always a critical problem. Multi-hop data transmission may cause the nodes around the static sink to exhaust energy prematurely. It can be avoided by introducing the mobile sink (MS) in WSNs. The MS only needs to visit some specific nodes, such as cluster heads (CHs) in the clustering network, to collect data. In this paper, we propose an energy-adaptive clustering method based on Taguchi-based-GWO optimizer (EACM-TGWO). Different from the existing clustering protocols, we develop the optimal number of CHs according to the characteristic of energy consumption in MS-based WSNs. Afterwards, the fitness function is established by combining the residual energy and the average distance within clusters to select CHs. Meanwhile, an adaptive weight factor is introduced to dynamically tune the influence degree of the two factors on the clustering results. Furthermore, a novel meta-heuristic algorithm Taguchi-based grey wolf optimizer (TGWO) is used to search for the optimal CHs set. Compared with grey wolf optimizer (GWO) and some of its improved versions, TGWO is more excellent tested in CEC2017. We also make an elaborate comparison of EACM-TGWO with LEACH-C, EACM-GWO, MSECA, and GAOC. The simulation results indicate that EACM-TGWO is more powerful in terms of balancing energy consumption and saving network energy.

Automated summary

In this paper, an energy-adaptive clustering method based on Taguchi-based-GWO optimizer (EACM-TGWO) is proposed for wireless sensor networks with a mobile sink. The method determines the optimal number of cluster heads (CHs) based on the energy consumption characteristics of the network and uses a fitness function to select CHs. The Taguchi-based grey wolf optimizer (TGWO) algorithm is employed to search for the optimal set of CHs. Simulation results demonstrate that EACM-TGWO outperforms other algorithms in terms of balancing energy consumption and saving network energy.