An Energy-Aware Computation Offloading Framework for a Mobile Crowdsensing Cluster Using DMIPS Approach

Abstract

The proliferation of the Internet of Things (IoT) devices for systems development has highlighted requirements for the devices to be able to perform various types of computations and services. They include basic computation applications that perform the simple computation to more complex and cumbersome load computation tasks. Since IoT devices are designed to be powered by battery. It has limitation in energy. This issue become one of the main challenges need to be dealt with in IoT -based application developments. Computation offloading where heavy tasks can be sent to the cloud server is one of promising technique to address this issue. However, sending large computational jobs along with the data to the cloud server not always give better results in term of energy consumptions. This paper proposes an approach to build energy-efficient computation offloading framework for an IoT-based mobile crowdsensing cluster based on the DMIPS approach. The experiments were conducted using real IoT devices and the results show that the smart offloading approach can reduce the energy consumption of the devices in performing high computation tasks compared to the full local or offloading executions.