Linear randomized voting algorithm for fault tolerant sensor fusion and the corresponding reliability model

Sensor failures in process control programs can be tolerated through application of well known modular redundancy schemes. The reliability of a specific modular redundancy scheme depends on the predefined number of sensors that may fail, f, out of the total number of sensors available, n. Some recent sensor fusion algorithms offer the benefit of tolerating a more significant number of sensor failures than modular redundancy techniques at the expense of degrading the precision of sensor readings. In this paper, we present a novel sensor fusion algorithm based on randomized voting, having linear - O(n) expected execution time. The precision (the length) of the resulting interval is dependent on the number of faulty sensors - parameter f. A novel reliability model applicable to general sensor fusion schemes is proposed. Our modeling technique assumes the coexistence of two major types of sensor failures, permanent and transient. The model offers system designers the ability to analyze and define application specific balances between the expected system reliability and the desired interval estimate precision. Under the assumptions of failure independence and exponentially distributed failure occurrences, we use Markov models to compute system reliability. The model is then validated empirically and examples of reliability prediction are provided for networks with fairly large number of sensors (n>100).