This paper presents a performance evaluation of real-time scheduling schemes in configurable component-based sensor node platforms from two perspectives: a configurable component-based sensor node platform and a scheduling scheme for a mix of periodic and aperiodic tasks running on the sensor node platform. We consider two different types of configurable component-based sensor node platforms: event-driven and multi-tasking sensor node platforms. The basic components of these two configurable component-based sensor node platforms are tasks. Tasks are classified to have periodic and aperiodic properties. Periodic tasks need to be processed in a timely fashion and delivered to meet their deadlines, while aperiodic tasks need to be quickly processed for fast responses without missing any periodic task deadlines. Consequently, we present two different scheduling schemes for event-based and multi-tasking sensor node platforms, which attempt to meet the timing constraints of periodic tasks and improve the response time of aperiodic tasks. The experimental evaluation showed that the proposed scheduling schemes yielded efficient performance in terms of the minimal deadline miss ratio of periodic tasks and a fast average response time for aperiodic tasks.
This paper primarily presents a technique to integrate energy-aware real-time scheduling into batterypowered sensor node platforms. The proposed scheduling technique attempts to achieve energy savings while meeting timing requirements of real-time tasks as well as improving the response time of non-real-time tasks. The proposed energy-aware real-time scheduling technique is addressed from two perspectives: a dynamic voltage scaling (DVS)-based scheduling approach and a combined real-time and non-real-time scheduling approach. From the perspective of DVS-based scheduling approach, it reduces the processor frequency and still ensures that no tasks miss their timing constraints. From the perspective of a combined real-time and non-real-time scheduling scheme, tasks are allowed to have timing constraints. Consequently, realtime tasks need to be processed in a timely fashion and delivered to meet their timing constraints, and non-real-time tasks need to be quickly processed to deliver a fast response. An experimental evaluation shows that the proposed scheduling technique yields efficient performance in terms of low energy consumption and a fast average response time for non-real-time tasks, whilst meeting the timing constraints of real-time tasks.
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