An Architecture for Secure Positioning in a UAV Swarm using RSSI-based Distance Estimation

The widespread of small unmanned aerial vehicles (UAVs) offers more opportunities for collaborative UAV swarm to optimise missions. The common application for UAV swarms are surveillance, path planning, airborne, and relay networks. Cooperative applications make use of the UAVs' locations to make decisions. However, the question of vulnerability to security must be considered when the system infers that there are benefits and rights based on the UAV's location. There is a risk that an attacker can cheat the system by claiming a false or inaccurate location to gain access to restricted resources or be engaged in malicious activities without detection. This makes the system be very sensitive and dependent on the trust of the node's geo-temporal information in a swarm. In this paper, we propose an architecture that uses a UAV wireless card to measure the distance between a UAV, called a prover node, and the set of verifier nodes closest to it. The architecture core is based on a multilateration algorithm, which is employed to estimate the prover's position. In a previous study, we applied image processing from a UAV payload stereo camera to measure the distances between the nodes. In this paper, we have extended the study to securing positioning in a UAV swarm by applying the fundamental principles of beaconing-based communication to calculate the distance between the nodes on the basis of the received signal strength indicator (RSSI). The simulation results showed that the correct position determined by RSSI was better than the image processing from stereo cameras. The degree of accuracy achieved from RSSI was greater than 99.6% in distances of up to 165 m, while a similar degree of accuracy was limited up to 100 m of the distance, when a high-definition stereo camera was used".