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

Edge computing has become a major trend in networking research. The rapid growth in the number of data analytics based mobile applications has resulted in an exponential rise in processing demand. One way to cope with this increased processing demand is to use edge networks (EN), which is a wireless ad-hoc network of mobile cloudlets, vehicular cloudlets, dedicated edge devices, and cloud platforms. Typically these devices have different owners and service providers. In this work, we propose a distributed incentive mechanism for an EN, which does not require a trusted third party (TTP). We consider a multi hop EN where a user offloads tasks to neighboring nodes, which may further offload them to their neighbours or the cloud. Our scheme is computationally efficient and helps each node decide on the incentives and workload distribution. We conducted simulations to study the performance of our scheme in different scenarios, including those of untruthful behavior by some nodes. Results show the benefit of using multi hop offloading and that our scheme discourages the untruthful behavior of nodes by assigning them lesser workload. We also propose a partially distributed incentive mechanism and compared its performance to our fully distributed scheme.