Differential games in large-scale sensor-actuator networks

Surveillance systems based on sensor network technology have been shown to successfully detect, classify and track targets of interest over a large area. State information collected via the sensor network also enables these systems to actuate mobile agents so as to achieve surveillance goals such as target capture and asset protection. But satisfying these goals is complicated by the fact that track information in a sensor network is routed to mobile agents through multi-hop communication links and is thus subject to delays and losses. In addition, as the sensor network is scaled in size, high throughput rates for all pursuers cannot be sustained at all times, which necessitates a network communication strategy that adapts to pursuer information requirements. In this paper, we concentrate on the formulation of optimal pursuit control strategies in the presence of network effects, assuming that target track information has been established locally in the sensor network. We adapt ideas from the theory of differential games to networked games-including ones involving non-periodic track updates, message losses and message delays-to derive optimal strategies, bounds on the information requirements, and scaling properties of these bounds. Moreover, we present a specific network communication protocol which has the required scalable information characteristics and conclude with the results of experimental studies.