SWARM UNMANNED AERIAL VEHICLES (UAVS)-BASED FOG COMPUTING PLATFORM SUPPORTING INTERNET OF THINGS APPLICATIONS

Abstract

Swarm robots, particularly drone swarms, are commonly used in search and rescue, military, and detection missions. However, due to their limited computing resources, it can be difficult to handle computation-intensive tasks locally. To address this, cloud-based computation offloading is often used, but it may cause latency issues for time-sensitive tasks like object recognition and path planning. Additionally, in environments with no wireless infrastructure, such as disaster areas or battlefields, cloud computing may not be feasible. To solve this problem, this paper proposes integrating Fog Computing with Swarm of Drones architecture. The paper also formulates the problem as a task allocation problem that minimizes energy consumption while accounting for latency and reliability constraints. To improve swarm autonomy, an integrated framework is proposed, and a testbed development is introduced to support this architecture. The paper reviews existing literature on UAV swarm and proposes a new architecture to enhance swarm autonomy.