A general computational framework for distributed sensing and fault-tolerant sensor integration

Proposes an abstract framework to address the problem of fault-tolerant integration of information provided by multiple sensors. This paper presents a formal description of spatially distributed sensor networks, where i) clusters of sensors monitor (possible overlapping) regions of the environment; ii) sensors return measured values of a multidimensional parameter of interest; and iii) uncertainties associated with a sensor output are represented by a connected subset in the parameter space. A method to obtain interval estimates of components of the actual parameter vector is developed, wherein information from faulty sensors are filtered out. The problem addressed involves combining interval estimates of sensor outputs into a best intersection estimate of outputs. The sensor fault model used assumes most faults cluster in the neighborhood of the correct values. The procedure of this paper is superior to earlier work. To test the theoretical analysis of the framework proposed, we have developed a modular parameter-driven simulator SIMDSN for the fault-tolerant integration of abstract sensor interval estimates. The simulator uses the well-known Monte-Carlo technique to generate random correct and tamely faulty intervals