A reconfiguration framework for self-organizing distributed state estimators

A sensor network operating under changing operational conditions will have to adapt to its environment, topology and system performance. In order to obtain this flexible behavior, a reconfiguration framework is proposed for distributed signal processing solutions. The considered example in this article is distributed Kalman filtering, whereas the reconfiguration framework is based on a first order logic reasoner to find a feasible configuration in a dynamic execution context. In a simulated scenario of a greenhouse temperature field estimation, the proposed system can minimize the state estimation error, while satisfying the systems constraints such as battery life, communication bandwidth or reliability and timeliness of response.