Nodes that are part of a multihop wireless network, typically deployed in mission critical settings, are expected to perform specific functions. Establishing a notion of reliability of the nodes with respect to each function (referred to as functional reliability or FR) is essential for efficient operations and management of the network. This is typically assessed based on evidence collected by nodes with regards to other nodes in the network. However, such evidence is often affected by factors such as channel induced effects and interference. In multihop contexts, unreliable intermediary relays may also influence evidence. We design a framework for collaborative assessment of the FR of nodes, with respect to different types of functions; our framework accounts for the above factors that influence evidence collection. Each node (say Chloe) in the network derives the FR of other nodes (say Jack) based on two types of evidence: (i) direct evidence, based on her direct transactions with each such node and (ii) indirect evidence, based on feedback received regarding Jack from others. Our framework is generic and is applicable in a variety of contexts. We also design a module that drastically reduces the overhead incurred in the propagation of indirect evidence at the expense of slightly increased uncertainty in the assessed FR values. We implement our framework on an indoor/outdoor wireless testbed. We show that with our framework, each node is able to determine the FR for every other node in the network with high accuracy. Our indirect evidence propagation module decreases the overhead by 37% compared to a simple flooding based evidence propagation, while the accuracy of the FR computations is decreased only by 8%. Finally, we examine the effect of different routing protocols on the accuracy of the assessed values.
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