A blockchain-based trust management system to secure task offloading in mobile edge computing.

Abstract

With the growth of the Internet of Things (IoT), the majority of apps run on mobile devices are increasingly reliant on artificial intelligence (AI) algorithms, such as approaches for automatic learning, image/video processing, augmented reality, online gaming, and the longawaited arrival of metaverse applications. These applications need a large amount of resources in order to perform extremely complicated calculations, mobile terminals face significant limits in terms of computation and storage capacity, in addition to a limited battery life. As a solution, computation / data offloading has been presented as a possible resource allocation strategy that comprises transferring intensive jobs and massive volumes of data to be performed by the mobile edge computing (MEC). However, the lack of confidence between mobile terminals and MEC servers is seen as a serious security and confidentiality problem to the computation/data offloading approach’s deployment. Indeed, before engaging in any interaction, each entity of the system must verify and confirm the reliability and the security of the other entity. Blockchain has recently been identified promising solution for increasing the security of MEC systems. Furthermore, the blockchain can provide high levels of security and trust for MEC by depending on a vast community’s distributed verification of the authenticity of MEC system communicators via highly trustworthy consensus protocols. Motivated by the previous discussion, we investigate in this work a trust management system to ensure more secure and reliable interactions in the MEC domain for task offloading. Our system is built on blockchain technology and consists primarily of two parts: a bidirectional trust management mechanism based on a reputation metric and an incentive approach that uses game theory to ensure more efficient block mining. We used a Stackelberg-type game to model the incentive problem in verification with asymmetric information in which the players are: a leader who acts first, followed by several followers. Our Stackelberg game is being played between edge devices. The leader begins by announcing its strategy, after which the followers compete and respond with their best strategy that maximizes the leader’s approach. After receiving all of the followers’ replies, the leader optimizes its final strategy. In the last part we tried to show the relationship between the utility of the players (leader/follower) and the other parameters, and we obtained logical results.