Probability-Aware Dynamic Topology Control With Sleep Scheduling for Wireless Sensor Networks

The integration of probability-based hierarchical clustering routing with sleep scheduling is essential for topology control in achieving energy optimization within energy-constrained wireless sensor networks (WSNs). However, it encounters numerous challenges, such as the selection of low energy nodes as cluster heads (CHs), non-uniform distribution of CHs, large overlapping areas between nodes, and unreasonable setting of sleep probability. To enhance energy efficiency and reduce redundancy, we propose a probability-aware dynamic topology control with sleep scheduling (PDTC), which includes energy and distance weighted dynamic clustering (EDDC) routing and dynamic probability sleep scheduling (DPSS) scheme. EDDC is employed to construct a hierarchical network topology, whereas DPSS is designed to alleviate network load. EDDC prioritizes the node with sizeable residual energy and close to the sink as the CH to equalize energy expenditure. It combines control parameters that encompass residual energy, distance to the sink, and the optimal number of CHs, thereby eliminating the randomness in the selection of CHs. Furthermore, DPSS adopts a recursive method to derive the general expression for the number of active member nodes within a cluster at the beginning of each time slot. It then models the sleep decision as a Bernoulli process to dynamically set the sleep probability for each cluster. This approach probabilistically activates a small number of active member nodes within the cluster to provide intra-cluster communication. The simulation results indicate that our PDTC outperforms existing protocols in terms of network lifetime, the number of alive nodes, and packets received by the sink.