Decentralized Updates Scheduling for Data Freshness in Mobile Edge Computing

Age of information (AoI) has been proposed to quantify data freshness. In some real-time applications such as surveillance systems, real-time analytics on source updates requires intensive computation resources and incurs high energy consumption. By providing computing resources at the network edge, mobile edge computing (MEC) can reduce update processing time and improve data freshness. In this paper, we investigate the age-optimal computation-intensive update scheduling for multiple sources in MEC-enabled IoT networks. Since the centralized algorithms may not apply due to the high computational complexity, we design an efficient decentralized scheduling mechanism for self-organized IoT networks. We provide a game-theoretic analysis and prove the existence of pure strategy Nash equilibrium. An efficient and decentralized algorithm based on the best-response dynamics is proposed to compute the equilibrium. We also provide the approximation ratio of the proposed algorithm. In particular, for the homogeneous source model, we show that the approximation ratio is at most 2.5. Extensive evaluation results show that the proposed decentralized algorithm is computationally efficient and closely approximates the centralized optimum in various settings.