ADTO: A Trust Active Detecting-based Task Offloading Scheme in Edge Computing for Internet of Things

Abstract

In edge computing, Internet of Things devices with weak computing power offload tasks to nearby edge servers for execution, so the task completion time can be reduced and delay-sensitive tasks can be facilitated. However, if the task is offloaded to malicious edge servers, then the system will suffer losses. Therefore, it is significant to identify the trusted edge servers and offload tasks to trusted edge servers, which can improve the performance of edge computing. However, it is still challenging. In this article, a trust Active Detecting-based Task Offloading (ADTO) scheme is proposed to maximize revenue in edge computing. The main innovation points of our work are as follows: (a) The ADTO scheme innovatively proposes a method to actively get trust by trust detection. This method offloads microtasks to edge servers whose trust needs to be identified, and then quickly identifies the trust of edge servers according to the completion of tasks by edge servers. Based on the identification of the trust, tasks can be offloaded to trusted edge servers, to improve the success rate of tasks. (b) Although the trust of edge servers can be identified by our detection, it needs to pay a price. Therefore, to maximize system revenue, searching the most suitable number of trusted edge servers for various conditions is transformed into an optimization problem. Finally, theoretical and experimental analysis shows the effectiveness of the proposed strategy, which can effectively identify the trusted and untrusted edge servers. The task offloading strategy based on trust detection proposed in this article greatly improves the success rate of tasks, compared with the strategy without trust detection, the task success rate is increased by 40.27%, and there is a significant increase in revenue, which fully demonstrates the effectiveness of the strategy.