Incentivizing Control of Resource Cycling Problem for Surveillance

This paper presents an incentivizing control of resource cycling problems for surveillance that enables autonomous frontline patrollers to achieve a socially optimal real-time allocation under moral hazard conditions. We model the resource cycling problem as a discrete-time Markov jump linear system that captures transient transition probabilities while recasting the surveillance routing problem in the steady state. To reflect the individual decision-making of the patrollers to optimize the surveillance performance for urban security, we propose that the solution to the resource cycling problem is reduced to the continuous-time theoretically-optimal incentivizing control solution of the long-term time-average revenue case, which is inspired by contract theory, using the forward-difference-type Euler approximation. The proposed incentivizing control is proven to be a strategy-proof mechanism, and its effectiveness is verified through numerical examples.