Distributed Multi-hop Task Offloading in Massive Heterogeneous IoT Systems

Edge computing satisfies sudden demands of computation-intensive applications of Internet of Things (IoT) devices. Multi-hop task offloading has been a promising technology to provide edge services to areas with poor server coverage via multi-hop task forwarding. However, the existing multi-hop offloading approaches have primarily assumed that complete information is required, which does not always hold in heterogeneous IoT systems due to the privacy. To overcome this limitation, we propose a novel two-stage method with incomplete information (TMII) to minimize overall task execution cost with an incomplete information setting. Specifically, a hierarchical minority game (HMG) is proposed to estimate the offloading costs by the hierarchical estimation model and the historical data in Stage I. By comparing the estimated offloading cost with the local cost, each IoT device individually decides where to execute the tasks. In Stage II, a tree-based routing mechanism schedules the transmission efficient paths for the offloading nodes by building distributed tree structures. The augmented paths balance the transmission loads to further reduce the offloading delay. Furthermore, the extensive simulation experiments demonstrate TMII outperforms the state-of-art approaches in terms of overall cost reduction with less communication overhead.