Modeling and Control of Mobile Surveillance Networks Using Mutational Hybrid Systems

Pervasive surveillance can be defined as continuous monitoring and tracking of multiple targets in a large monitored region so that they do not leave the field of view (FOV) of the sensors observing them. Despite the limited sensing capability and range of the individual sensors, the surveillance network can track targets over a large region based on transferring the target tracking task. The challenge for such large scale networked systems is to design an efficient and scalable modeling and analysis tool and devise stable control algorithms for accomplishing the surveillance task. Mutational analysis and shape based control have been proposed to overcome the limitations of current feature (point) based visual servoing techniques, however, they fail to capture the discrete switching nature of the surveillance task of tracking the target using multiple sensors. This paper presents a mutational hybrid model for such pervasive surveillance networks which retains the advantages of using mutational equations while also being able to model the discrete switching between various sensors. We also present an example pervasive surveillance scenario modeled using the proposed method and experimental results verifying the proposed approach