With the rapid development in the area of Internet of Things (IoT), the number of delay-sensitive and power-hungry IoT applications has drastically increased over the past few years. Mobile edge computing (MEC) has become an effective computing model for meeting the IoT applications’ requirements. However, the resource-constrained nature of the IoT devices, different characteristics of IoT tasks such as delay sensitivity, and heterogeneity of the edge cloud servers (ECSs) make the task offloading problem a fundamental challenge in the MEC systems. Motivated by this, in this article, we propose a deadline-aware and energy-efficient computation offloading algorithm, DECO, for scheduling and processing the generated tasks from the IoT devices. The proposed algorithm jointly takes into account the deadline requirement of the tasks and the energy consumption of the IoT devices in the local decision-making process. Also, it considers the priority of the tasks and heterogeneity of ECSs for the task-node mapping process. The extensive simulation results confirm that the proposed algorithm improves the deadline satisfaction ratio of delay-sensitive tasks about 85.4% and reduces their average response time up to 62.9% compared to the state-of-the-art method while the amount of increase in the total energy consumption of IoT devices is less than 13.4%.
