A Sequential Game Approach for Computation-Offloading in an UAV Network

Mobile Edge Computing in Unmanned Aerial Vehicle Networks

“…Specifically, in [3] and [4], a theoretical methodology based on a sequential game was exploited to minimize the execution cost of the UAV-enabled MEC network. Three different types of players (drone, base station, and edge server) were considered in [3] while drone-only players with three different strategies were studied in [4]. The existence of the Nash Equilibrium was demonstrated in [3][4] and a good tradeoff between the energy consumption and the delay was achieved by using the proposed strategies.…”

“…Three different types of players (drone, base station, and edge server) were considered in [3] while drone-only players with three different strategies were studied in [4]. The existence of the Nash Equilibrium was demonstrated in [3][4] and a good tradeoff between the energy consumption and the delay was achieved by using the proposed strategies. Note that UAVs in [3] and [4] 6…”

“…The existence of the Nash Equilibrium was demonstrated in [3][4] and a good tradeoff between the energy consumption and the delay was achieved by using the proposed strategies. Note that UAVs in [3] and [4] 6…”

“…The role of the UAV Operation modes Contributions [3] User with computation task Partial offloading Cost minimization [4] User with computation task Partial offloading Cost minimization [5] User with computation task Partial offloading Completion time minimization [6] MEC server executing computation Partial offloading Architecture establishment [7] MEC server executing computation Partial offloading Energy consumption minimization [8] MEC server executing computation Partial offloading Energy consumption minimization [9] MEC server executing computation Partial offloading and binary computation Computation bits maximization [10] Relay offloading computation task Partial offloading Offloading bits maximization are assumed to hover in the sky and their trajectories are not optimized. Different from the works in [3] and [4], the authors in [5] proposed a resource allocation strategy that jointly optimizes the trajectory of the UAV and the offloading time in order to minimize the mission completion time of the UAV.…”

“…conducted in designing resource allocation schemes[3]-[10], those schemes were mainly proposed for networks with either one UAV or one user. Since the operation time and battery of the UAV are limited and normally a large number of users need to be served in the geographical coverage area, it is important yet challenging to design efficient resource allocation schemes for UAV-enabled MEC networks with multiple users and multiple UAVs.…”