Incentive-based task matching with evolving preferences in distributed fog computing: A trust-aware approach
Lei Yan, Zaigui Zhang, Bin Sun, Yonghai Wang · 2023
Fog computing has been a promising technology for the computation-intensive Apps with the resource pool adjacent to the mobile users. In which, users’ workloads or management can be offloaded to servers with a designed matching scheme. However, the selfish servers are reluctant to share their resources cooperatively. An efficient incentive scheme is desirable for the edge networks, especially for the decentralized fog networks. In addition, existing works mainly assume that the preferences of users and servers are static or fixed, and it is not practical for the fog networks with dynamicity caused by many uncertainties. In this paper, a trust-aware incentive scheme for task matching with evolving preferences in decentralized fog computing is proposed. We first formulate a long-term trust management scheme for the network considering the participants’ behaviors, such as, service qualities and payment behavior, etc. Then, the evolving preferences of both users and fog nodes are derived with their variable trust levels, and a trust-aware task matching algorithm is proposed. Finally, a trust-aware incentive scheme is designed to encourage users and fog nodes to behave positively during the offloading interaction. The simulation results demonstrate that our proposed trust-aware fog computing scheme is more efficient than existing methods regarding energy cost and delay.