A methodology for in-network evaluation of integrated logical-statistical models

Synthesizing high-level semantic knowledge from low-level sensor data is an important problem in many sensor network applications. Programming a network to perform such synthesis in situ is especially difficult due to the stringent resource constraints, unreliable wireless communication, and complex distributed algorithms and network protocols required to manipulate the data. Recently, a declarative programming language called Snlog [5] has been developed to address this problem. However, statistical reasoning for modeling noise in the context of sensor networks has not been addressed in Snlog. In this paper, we develop a methodology based on the PRISM [36] framework, which integrates logical and statistical reasoning, for specifying sensor network programs that deal with noisy data and tolerate faults in the network. The relationship between high-level (synthesized) and low-level (observed) data is captured by logical rules, while statistical models are used to specify computations in the presence of noise and faults. We illustrate our methodology with three examples: (i) estimating temperature at various points in a region, (ii) evaluating the trajectory of an object observed by a sensor network, based on the Hidden Markov Model, and (iii) evaluating most reliable communication paths between sensor nodes. We analyze the results of simulations as well as an experimental deployment to evaluate the practical feasibility of our approach.