Fuzzy-based clustering solution for hot spot problem in wireless sensor networks

Clustering is an effective approach for organizing network nodes into hierarchical topology, aggregating sending data to the base station and prolonging the network lifetime. However, it may cause sudden death of nodes in some network regions, i.e., hot spots, due to heavy traffic load leading to disruption in network services. This problem is traditional for data collection scenarios in which Cluster Heads (CHs) are responsible of gathering and relaying the sensed data. To balance the workload over the nodes, the CH role must be rotated among all nodes and the cluster size should be determined such that uniformly distribute the energy consumption in network. In this paper, we propose a clustering algorithm that selects the nodes with highest remaining energy in each region as candidate CHs in order to pick the best nodes among them as final CHs. To consider the hot spot issue it employs fuzzy logic in order to adjust the cluster radius of CH nodes based on some local information (distance to base station and local density). Simulation results show that the proposed approach achieves an improvement in terms of network lifetime through mitigating the hot spot problem.