Rate-Aware Fuzzy Clustering and Stable Sensor Association for Load Balancing in WSNs

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale-Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

Automated summary

This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association to improve the resource utilization and energy efficiency of wireless sensor networks (WSNs). The framework includes methods for initial clustering, energy-aware cluster head selection, optimal sensor association, and load balancing. Simulation results show the effectiveness of the proposed algorithms.