Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordDue to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more than one UAVs for the effective scheduling under multi-task requests. Based on this scenario, we clarify the issue on how to maintain the energy efficiency of UAVs and guarantee the reputation gain during the scheduling deployment. In this paper, we first formulate the energy consumption and reputation into the decision model of UAVs scheduling. A game-theoretic scheme is then developed for the optimal decision searching. With the developed model, a range of important parameters of UAV scheduling are thoroughly investigated. Our numerical results show that the proposed scheduling strategy is able to increase the reputation and decrease the energy consumption of UAVs simultaneously. In addition, in the game process, the profit of an operator can be maximized and the network economy research can be explored.Engineering and Physical Sciences Research Council (EPSRC

Artificial intelligence and game theory controlled autonomous UAV swarms

Autonomous unmanned aerial vehicles (UAVs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, UAV synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous UAV to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (EM) information. Each UAV using our flight control algorithms positions itself such that the swarm main-tains mobile ad-hoc network (MANET) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile EM transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected MANET, promote a uniform network spread-ing, while avoiding overcrowding with other swarm members. We also prove that they maintain MANET connectivity and, at the same time, they can lead a swarm of autonomous UAVs to follow or avoid an EM transmitter. Simulation experiments in OPNET modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile EM source and maintain MANET connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile EM transmitters