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Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization

Timely communication in wireless multihop sensor networks requires high throughput and low delay, which can be achieved by exploiting multiple channels and time slots. Efficient scheduling becomes indispensable if multiple channels and time slots are utilized. Optimum scheduling of multiple channels and time slots in multihop networks is an NP-complete problem. We apply metaheuristic approaches to solve the scheduling problem because of the fact that not only the global solution but near-optimal solutions can satisfy a given end-to-end delay bound. We adopt simulated annealing (SA) and particle swarm optimization (PSO) to schedule the resources. Different measures and stopping conditions are explored to validate the feasibility of scheduling via SA and PSO, and to compare the performance of the two metaheuristics in satisfying the desired end-to-end delay. Although the purpose of this article is to compare SA and PSO, the simulation results demonstrate that PSO-based scheduling outperforms SA-based scheduling in terms of end-to-end delay.