Fully and Partially Distributed Incentive Mechanism for a Mobile Edge Computing Network


Computing at the network's edge has emerged as a significant focus of recent NetWorking research. The exponential increase in processing demand that has been brought about as a result of the meteoric rise in the number of mobile applications that are based on data analytics. Edge networks (EN), which are a wireless ad-hoc network of mobile cloudlets, vehicular cloudlets, dedicated edge devices, and cloud platforms, are one solution for meeting the increased demand for processing that has arisen as a result of the recent technological advancements. In most cases, the owners of these devices and the companies that provide their services are separate. Within the scope of this study, we propose a decentralized incentive mechanism for an EN that does not involve the involvement of a trusted third party (TTP). We take into consideration a multi hop EN in which a user offloads tasks to neighboring nodes, which may then further offload them to their neighboring nodes or to the cloud. Our system is computationally efficient and aids in the decision-making process of each node regarding the incentives and distribution of workload. We used simulations to study the performance of our scheme in a variety of scenarios, including those in which some nodes behaved dishonestly. The findings demonstrate that utilizing multi hop offloading is beneficial, and that our scheme deters dishonest behavior of nodes by assigning them a lesser workload than other nodes in the network. In addition to this fully distributed incentive mechanism, we also propose a partially distributed incentive mechanism and evaluate its performance in comparison to our own fully distributed scheme.

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