Energy Efficient Scheduling of Fine-Granularity Tasks in a Sensor Cloud

Abstract

Wireless Sensor Networks (WSNs) are frequently used in number of applications like unattended environmental monitoring. WSNs have low battery power hence schemes have been proposed to reduce the energy consumption during sensor task processing. Consider a Sensor Cloud where owners of heterogeneous WSNs come together to offer sensing as a service to the users of multiple applications. In a Sensor Cloud environment, it is important to manage different types of tasks requests from multiple applications efficiently. In our work, we have proposed a scheduling scheme suitable for the multiple applications in a Sensor Cloud. The scheduling scheme proposed is based on TDMA which considers the fine granularity of tasks. In our performance evaluation, we show that the proposed scheme saves energy of sensors and provides better throughput and response time in comparison to a most recent work.