DEANS: an energy-efficient coverage-optimised node scheduling strategy for dynamic energy sensing

In IoT wireless sensor networks (WSNs), node scheduling strategies are crucial for coverage quality and energy utilisation efficiency. The traditional ECO-NS strategy performs well in static scenarios, but its coverage control and energy management capabilities are limited in dynamic load environments. To this end, this paper proposes an energy-efficient node scheduling strategy DEANS based on ECO-NS for dynamic energy sensing, which incorporates an exponentially weighted moving average (EWMA) dynamic energy prediction model, an adaptive coverage redundancy thresholding mechanism, and an improved multi-state switching control logic to enhance the network's resilience and energy-efficiency performance. Simulation results show that: the average network coverage quality of DEANS reaches 77.02%, which is 12.24% and 32.95% higher than that of ECO-NS and TCA-NS, respectively; the average energy consumption rate of the nodes is lower, with a relative decrease of 69.08% and 77.69%, which achieves the improvement of energy balance; in terms of network lifetime, DEANS extends the effective coverage maintenance time by 12.20% and 48.39% over ECO-NS and TCA-NS, respectively. The above results verify the superior energy efficiency scheduling capability and network survivability of DEANS under dynamic sensing environment.