Abstract
The rapid growth of emerging sensory data processing and delivery application poses a significant challenge to researchers due to the WSN's resource limitations. To minimise the WSN's job processing time, highly resourceful cloud computing technology has become a potential solution. To minimise the WSNs battery usage and job execution time, the development of a proper job scheduling scheme has emerged as a potential research challenge for cloud empowered WSN's by taking into account different job and resource characteristics. In this paper, we propose a time and energy-efficient hybrid job scheduling scheme for WSNs job execution that not only assigns sensor-cloud resources but also network timeslot resources. We present an analytical model and compare our proposed scheme's performance with traditional contention and reservation-based scheme. Our experimental results demonstrate that the proposed scheme supersedes the state of the art up to approximately 28.05% in the metric of schedule length.
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