Incentive Aware Task Offloading in the Internet-of-Things

Abstract

The evolution of the Internet of Things (IoT) has surged connectivity among IoT devices, amplifying data processing. Efficient resource utilization necessitates task offloading among these devices. Yet, selecting the right node for offloading confronts two primary challenges: 1) varying QoS requirements of tasks, and 2) discrepancies in candidate devices’ capabilities encompassing battery usage, computing power, and mobility. To foster collaboration among such diverse devices and optimize their limited resources, this paper proposes a cooperation-driven incentive model (salary). Bonus allocation hinges on both time deadlines and node performance, ensuring equitable treatment. To address fairness concerns, a Stackelberg non-cooperative game is devised, aimed at maximizing task receivers’ profit while minimizing costs for offloading devices. The results highlight that the cost incurred upon task offloading devices is decreased through proposed algorithms and improved price gain for the task of floaders. The results highlight that the cost incurred upon task offloading devices is decreased through proposed algorithms and improved price gain for the task offloaders. This framework endeavours to encourage device cooperation and enhance task offloading efficiency within the heterogeneous IoT landscape.