DCLA: A Duty-Cycle Learning Algorithm for IEEE 802.15.4 Beacon-Enabled WSNs

The current specification for IEEE 802.15.4 beacon-enabled networks does not define how active and sleep schedules should be configured in order to achieve the optimal network performance in all traffic conditions. Several algorithms exist in the literature that dynamically vary these schedules based on traffic load estimations. But it is still uncertain how these adaptive schemes perform with regard to each other as their performance has only been compared with the standard beacon mode. In this paper, we compare the current state-of-the-art schemes, and with the objective of overcoming the performance deficiencies shown by previous approaches, we introduce DCLA, an adaptive duty-cycle scheme for IEEE 802.15.4 beacon-enabled Wireless Sensor Networks (WSN) that employs a reinforcement learning technique. Simulation results show that the proposed scheme achieves the best overall network performance for a wide range of traffic conditions and performance parameters when compared with existing IEEE 802.15.4 duty-cycle adaptation schemes.