Adaptive Learning Based Scheduling in Multichannel Protocol for Energy-Efficient Data-Gathering Wireless Sensor Networks

Multichannel communication protocols have been developed to alleviate the effects of interference and consequently improve the network performance in wireless sensor networks requiring high bandwidth. In this paper, we propose a contention-free multichannel protocol to maximize network throughput while ensuring energy-efficient operation. Arguing that routing decisions influence to a large extent the network throughput, we formulate route selection and transmission scheduling as a joint problem and propose a Reinforcement Learning based scheduling algorithm to solve it in a distributed manner. The results of extensive simulation experiments show that the proposed solution not only provides a collision-free transmission schedule but also minimizes energy waste, which makes it appropriate for energy-constrained wireless sensor networks.