Information Autonomy: Self-Adaptive Information Management for Edge-Assisted Autonomous UAV Systems

Making Unmanned Aerial Vehicles (UAV) fully autonomous faces many challenges, some of which are connected to the inherent limitations of their on-board resources, such as energy supply, sensing capabilities, wireless characteristics, and computational power. The sensing, communication, and computation Internet of Things (IoT) infrastructure surrounding the UAVs can mitigate such limitations. However, external traffic dynamics, signal propagation, and other poignant characteristics of the IoT infrastructure make it an extremely dynamic and incoherent environment, especially in urban scenarios, thus challenging the use of IoT resources for mission-critical UAV applications. Herein, the concept of information autonomy is introduced to extend autonomy to encompass how information-related tasks are handled in this challenging scenario. In this paper, we motivate the need for 'Information Autonomy' based on our observations from real-world experiments and present a self-adaptive framework for edge-assisted UAV applications. Through our preliminary evaluation, we show that our 'Information Autonomy' framework is capable of handling uncertainties autonomously during run-time.