Competitive Energy Allocation for Aerial Computation Offloading: A Colonel Blotto Game

In this paper, we consider a competitive aerial computation offloading environment, where two edge resource operators provide computing services on a time-slot basis to multiple users, via Unmanned Aerial Vehicles (UAVs), each bearing a mounted edge server. The aim of each UAV is to selfishly maximize the difference between its personal and the opponent UAV's utility, by competitively allocating its energy resources to the different users in the system. The problem is formulated as a Generalized Colonel Blotto (GCB) game, where the UAVs allocate their resources across a number of battlefields, i.e., the users, as competing players, seeking to win the battlefield by increasing the difference of their in-between allocated resources and thus, experienced utility. The overall framework is complemented by a Reinforcement Learning (RL)-empowered algorithm to account for the energy efficient scheduling of the UAVs' overall available energy in the different time slots, where the GCB game is realized. The performance evaluation of the proposed framework is achieved via modeling and simulation. The obtained numerical results demonstrate the operation of the proposed GCB game, under different levels of competitiveness between the UAVs, and assess the effectiveness and efficiency of the proposed RL algorithm against different comparative scenarios.