AoI Optimization for UAV-aided MEC Networks under Channel Access Attacks: A Game Theoretic Viewpoint

Abstract

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAVs) are playing a more and more important role in Mobile Edge Computing Networks (MECN), such as ground sensor communication assistance, user data collection, edge computation offloading and remote control services. In UAV-aided MECN, the timeliness of exchange data is a key factor that influences the real-time data-driven decisions at the server-side. Simultaneously, the Age of information (AoI) is also an indicator that reflects the freshness of data in terms of the destination during the communication process. Hence, AoI minimization is a vital goal in the MECN. The most recent work overlooks the possible security issues in the AoI minimization process, especially the revealed channel access attacks (CAAs), which aim to deteriorate network performance from ground to air channels. To overcome this research gap, in this paper, we improve the AoI-oriented channel access problem under CAA from the perspective of game theory. Firstly, a system model with active probability consideration is established to obtain a MECN-based AoI indicator under CAA. Subsequently, by utilizing Ordinary Potential Game (OPG), we formulate the AoI-based channel access optimization problem. Then, to reach the Nash Equilibrium (NE) of the OPG, a learning algorithm called Distributed Channel Access Strategy Determination (DCASD) is proposed to determine the channel access strategies. Finally, we conduct experiments under different parameters to present the better performance of our algorithm as compared with related work.