Neighbor-Aware Distributed Task Offloading Algorithm in Energy-Harvesting Internet of Things

In the distributed task offloading system, the desired task completion time cannot be achieved when lots of mobile devices offload simultaneously the tasks with high complexity to a specific edge cloud. In this research, we present a neighbor-aware distributed task offloading algorithm (NA-DTOA) where the IoT device takes its energy status and the decisions of neighbor devices into account for the decisions on whether to process the task by itself and on which edge cloud is selected to offload the task. To decrease the task completion time while maintaining the average energy outage probability below the specified threshold, we design a constrained stochastic game model. To achieve the solution of the model, a best response dynamics-based algorithm is devised. The evaluation results reveal that, compared to a probability-based scheme, NA-DTOA reduces the average task completion time by almost 47% while ensuring a substantially low-average energy outage probability of 0.03.

Automated summary

In the research, a neighbor-aware distributed task offloading algorithm (NA-DTOA) is proposed to address the issue of high task completion time and energy outage in a distributed task offloading system. The algorithm considers the energy status and decisions of neighbor devices to optimize task processing and edge cloud selection. A constrained stochastic game model and a best response dynamics-based algorithm are designed to achieve the solution. Evaluation results demonstrate that NA-DTOA significantly reduces the average task completion time by 47% and maintains a low-average energy outage probability of 0.03 compared to a probability-based scheme.