Managing Information Updating with Edge Computing: A Distributed and Learning Approach

The rapid proliferation of some real-time applications (e.g., video surveillance) has driven enormous interest in maximizing information freshness, quantified by the age of information (AoI). For some computation-intensive updates such as images or videos, the real-time update processing requires intensive resources, which edge servers can provide in mobile edge computing (MEC). In this paper, we investigate information updating scheduling with multiple users in MEC. Due to the centralized algorithms' limitations in distributed systems where users are self-interested, we investigate an efficient distributed scheduling algorithm. We model the information updating scheduling as an uncooperative game and propose a distributed algorithm to compute the unique Nash equilibrium. Considering the unavailability of some global network information, we propose a learning algorithm where each user learns how to make decisions based on observable information in a distributed manner. Extensive evaluation results show the efficiency of the proposed algorithms.