Document Type : Original Research Paper
A Wireless Sensor Network (WSN) is consisted of thousands of sensor nodes to monitor environmental conditions. Energy plays a major role in these networks because nodes are worked with batteries and are sometimes placed in an inconsistent environment where their battery cannot be charged or replaced. In this paper, a new dynamic clustering method is presented for WSNs with moving targets. In this method, target tracking is carried out using the speed and direction of target movement as well as selection of the optimal cluster-head (CH) using the artificial bee colony (ABC) meta-heuristic algorithm with fuzzy TOPSIS. In the proposed CH selection method, three criteria are considered; i.e. the remaining energy of the node, the cost of selecting each node as the CH, and the risk of hardware failure of each node. Since each of these criteria should be assigned with a suitable weight, ABC optimization algorithm is used to find the best weights for ranking decisions. After specifying the objective function in the ABC algorithm and weighting the criteria, fuzzy TOPSIS algorithm is used and each of the nodes is fuzzified in each of the criteria that are normalized and weighted. The results show that in addition to maintaining the accuracy of tracking the moving target, the proposed method achieves a 5.4% improvement in network energy consumption in comparison with a state-of-the-art method called EEAC.