A Taxonomy of Faults for Wireless Sensor Networks

Over the last decade, Wireless Sensor Networks (WSN) went from being a promising technology to the main enabler of countless Internet of Things applications in all types of areas. In industry, WSNs are now used for monitoring and controlling industrial processes, with the benefits of low installation costs, self-organization, self-configuration, and added functionality. Nevertheless, despite the fact that base WSN technologies are quite stable and subject to standardization, they have kept one of their main characteristics: fault-proneness. As a result, in recent years considerable effort has been made in order to provide mechanisms that increase the availability, reliability and maintainability of this type of networks. In this context, a whole range of techniques such as fault detection, fault identification and fault diagnosis used in other research fields are now being applied to WSNs. Unfortunately, this has not led to a consistent, comprehensive WSN fault taxonomy that can be used to characterize and/or classify faults. Neglecting the importance of WSN fault characterization (e.g., when using supervised algorithms for anomaly detection) may lead to bad classifiers and, consequently, bad fault handling procedures and/or tools. In this paper, we start by reviewing base fault management concepts and techniques that can be applied to WSNs. We then proceed to propose and present a comprehensive WSN fault taxonomy that can be used not only in general purpose WSNs but also in Industrial WSNs. Finally, the proposed taxonomy is validated by applying it to an extensive set of faults described in the literature.