Adaptive multi-layer clustering strategies based on capacity weight for Internet of Things

Due to the requirements brought by diversification of IoT applications, differentiation of nodes' capability, dynamic communication environment and demands, the real-time information of nodes (actual energy consumption, living nodes' density, pairwise nodes' communication radius) should be considered comprehensively for the clustering strategies of wireless sensor networks to achieve efficient, stable, and flexible performance with limited energy and different quality of service (QoS). This article proposes an improved dynamic multi-layer clustering strategy for various IoT applications with heterogeneous nodes' energy, unpredictable or fast-changing distribution of alive nodes, and dynamic scenarios. In addition, an adaptive adjustment strategy based on capability weight for multi-layer clustering network is proposed to reduce the impact of unreasonable head selection cycle of clustering. By analyzing the node energy, the change of node locations and historical data transmission of cluster head, different capability weights are assigned to each node to adaptively re-cluster the clusters with heavy load and poor performance, further make the network topology better match current situation and specified QoS requirements. Experimental results have demonstrated that proposed strategy can achieve less energy consumption, longer network lifetime, and better load balancing, especially for the cases with heterogeneous initial energy, nonuniform distribution, and higher density of nodes.