Spectrum Enforcement and Localization Using Autonomous Agents With Cardinality

TLDR: This work shows that by estimating spatial orientation of the agents with single antenna, the accuracy is improved by 96% over crowdsourcing only, and solves the scheduling problem with a 3-approximation ratio in polynomial time.

Abstract: The distributed nature of policy violations in spectrum sharing necessitate the use of mobile autonomous agents (e.g., UAVs, self-driving cars, and crowdsourcing) to implement cost-effective enforcement systems. We define this problem as multiagent planning with cardinality (MPC), where cardinality represents multiple, unique agents visiting each infraction location to collectively improve the accuracy of the enforcement tasks. Designed as a practical and deployable system, our solution leverages crowdsourced information to determine the optimum cardinality and provide a routing schedule for the agents to achieve the desired level of accuracy of detection and localization at minimum possible cost. We show that by estimating spatial orientation of the agents with single antenna, the accuracy is improved by 96% over crowdsourcing only. Using geographical maps as the basis, we solve the scheduling problem with a 3-approximation ratio in polynomial time that exhibits statistically similar performance under variety of urban locale across multiple continents. The longest path traversed by an agent on average is 1.2 km per unit diagonal length of a rectangular geographic area, even when there are twice as many infractions as agents. Deploying UAVs to the estimated region of infraction improves localization accuracy by ≈70% compared to ground vehicles.