Applying RIS-Based Communication for Collaborative Computing in a Swarm of Drones

Abstract

A swarm of autonomous and heterogeneous drones has many benefits in various scenarios e.g. search and rescue, disaster management, agriculture, delivery and logistics, mapping and surveying, environmental monitoring, etc. However, the presence of obstacles in the environment poses challenges to communication between drones, including network coverage, received signal power, latency, and power consumption. To improve the drones’ communication in real-time scenarios, reconfigurable intelligent surfaces (RIS) can be used. RIS is a promising technology for empowering millimeter-waves and sub-millimeter waves communication. It also can provide improved communication links with significantly higher received signal strength in non-line-of-sight situations, which should be taken into account by drones to decide when and with which other drone(s) to perform computation offloading. To this end, we provide two federated learning-based computation offloading strategies through direct and indirect communications. These approaches are based on an advanced rating technique including some key computation and communication parameters. The core of the algorithm also involves two separate deep learning models that are helpful to efficiently transfer and update the decreased model weights, drones’ properties, angle of arrival, and angle of departure. Simulation results show that the efficiency of the proposed approaches are superior to a reference strategy in terms of energy consumption by −32%, latency −18%, throughput +50%, and cost of communication and computation by −35%.

Keywords

• Swarm of drones
• reconfigurable intelligent surfaces
• computation offloading
• federated learning