Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic

Abstract

Clustering is a promising and popular approach to organize sensor nodes<br><br>into a hierarchical structure, reduce transmitting data to the base station by aggregation<br><br>methods, and prolong the network lifetime. However, a heavy traffic load may cause<br><br>the sudden death of nodes due to energy resource depletion in some network regions,<br><br>i.e., hot spots that lead to network service disruption. This problem is very critical,<br><br>especially for data-gathering scenarios in which Cluster Heads (CHs) are responsible<br><br>for collecting and forwarding sensed data to the base station.To avoid hot spot problem,<br><br>the network workload must be uniformly distributed among nodes. This is achieved<br><br>by rotating the CH role among all network nodes and tuning cluster size according to<br><br>CH conditions. In this paper, a clustering algorithm is proposed that selects nodes with<br><br>the highest remaining energy in each region as candidate CHs, among which the best<br><br>nodes shall be picked as the final CHs. In addition, to mitigate the hot spot problem,<br><br>this clustering algorithm employs fuzzy logic to adjust the cluster radius of CH nodes;<br><br>this is based on some local information, including distance to the base station and local<br><br>density. Simulation results demonstrate that, by mitigating the hot spot problem, the proposed approach achieves an improvement in terms of both network lifetime and<br><br>energy conservation.