A general approach for solving dynamic sensor activation problems for a class of properties

We study the problem of dynamic sensor activation for centralized partially-observed discrete event systems. The sensors can be turned on/off online dynamically according to a sensor activation policy in order to satisfy some observation property. In this paper, we consider a general class of properties, called Information-State-based (or IS-based) properties, which include, but are not limited to, observability, K-diagnosability, predictability, and opacity. We define a new Most Permissive Observer (MPO) that generalizes previous versions of this structure. The MPO that we define embeds all sensor activation policies for an IS-based property. An optimal sensor activation policy can then be synthesized based on the MPO. Our results generalize the previous works on dynamic sensor activation for enforcing the properties of observability, K-diagnosability, and opacity. Moreover, our MPO is applicable to solving dynamic sensor activation problems for a wide class of user-defined properties that can be formulated as IS-based properties. As a special case, we show that the problem of minimal sensor activation for enforcing predictability, which has not been considered in the literature, is solvable by our new approach.