IHSCR: Energy-efficient clustering and routing for wireless sensor networks based on harmony search algorithm - Bing Zeng; Yan Dong; Xinyu Li; Liang Gao (International Journal of Distributed Sensor Networks)
Clustering and routing are two key techniques to improve the energy efficiency of wireless sensor networks. As clustering and routing for improving the energy efficiency of wireless sensor networks are NP-hard problems, increasing meta-heuristic algorithms are introduced for solving them. However, due to their discreteness and strong constraints, most meta-heuristics are unsuitable or inefficient to optimize them. Harmony search algorithm is one of the most suitable meta-heuristics for solving these problems. This article proposes a new energy-efficient clustering and routing algorithm based on harmony search algorithm to improve the energy efficiency of wireless sensor networks. The proposed approach contains two parts: clustering phase and routing phase. First, a new objective function model, which has considered balancing the energy consumption of both gateways and regular nodes as well as considered routing, is established for the clustering phase. Then, a new energy-efficient clustering algorithm is designed based on several improvements made to harmony search algorithm: (1) a discrete encoding scheme of a harmony for clustering is proposed; (2) a roulette wheel selection method is designed to choose a gateway for a regular sensor node to join, which is employed by two steps (i.e. initialization of harmony and improvisation of a new harmony); (3) the dynamically changed harmony memory considering rate is designed for improvisation of a new harmony; (4) a local search scheme is proposed to improve the best harmony within the harmony memory in iterations. In addition, the improved harmony search based energy-efficient routing algorithm that we proposed previously is employed to balance the energy consumption of gateways in the routing phase. The proposed approach is compared with several popular meta-heuristic-based clustering algorithms over extensive wireless sensor networks cases. The experimental results clearly demonstrate the superiority of the proposed approach.