Adaptation of On-line Scheduling Strategies for Sensor Network Platforms

Current sensor network platforms perform multiple processes including sensor sampling, communication, and various computational tasks. When deployed in unpredictable environments, complex schedules of those processes may arise. Typical sensor network qualities like periodic sampling of sensors, avoidance of process starvation and automatic energy management are required to be maintained in such situations. We propose a scheduling framework for senor nodes consisting of a scheduler, a dispatcher and a controller for an on-line adaptation of the process execution. The key components are a controller and an enhanced dispatcher implementing strategies like jitter correction and starvation avoidance. Further, the framework is aware of the energy consumption of sensors. We show that our controlled scheduling framework performs significantly better than a non-controlled single scheduler in unpredictable environments. Our proposed measures are efficient to implement. Results are shown by extensive simulations and a first implementation on the particle computer platform

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