Abstract
In wireless sensor networks, the quality of the provided data is influenced by the properties of the sensor nodes. Often deployed in large numbers, they usually consist of low-cost components where failures are the norm, even more so in harsh outdoor environments. Current fault detection techniques, however, consider the sensor data alone and neglect vital information from the nodes’ hard- and software. As a consequence, they can not distinguish between rare data anomalies caused by proper events in the sensed data on one side and fault-induced data distortion on the other side. In this paper, we contribute with a novel, open-source sensor node platform for monitoring applications such as environmental monitoring. For long battery life, it comprises mainly low-power components. In contrast to other sensor nodes, our platform provides self-diagnostic measures to enable active node-level reliability. The entire sensor node platform including the hardware and software components has been implemented and is publicly available and free to use for everyone. Based on an extensive and long-running practical experiment setup, we show that the detectability of node faults is improved and the distinction between rare but proper events and fault-induced data distortion is indeed possible. We also show that these measures have a negligible overhead on the node’s energy efficiency and hardware costs. This improves the overall reliability of wireless sensor networks with both, long battery life and high-quality data.
Highlights
Based on a tripartite experiment setup, we show the effectiveness of the ASN(x) in terms of node-level fault detection and its efficiency related to the energy consumption that is comparable with recent sensor nodes
Hard hard Fault fault hardware fault transceiver microcontroller sensors power / battery fault type data-centric faults outlier calibration fail-stop spike connection / hardware fail-silence stuck-at broken sensor noise short circuits soft fault timing low battery value value out of range arbitrary value clipping fault manifestation fault persistence fault level permanent node err. data sensing solid link / network err. data processing intermittent cluster head / fog node err. data communication transient system-centric faults crash application. While parts of this taxonomy are generally applicable, it is specially tailored to the characteristics of wireless sensor networks (WSNs) especially concerning their hardware components, network structures, and fault types commonly appearing in sensor networks
The approach proposed in [48] extends a dynamic Bayesian network with a sequential dependency model (SDM) separated in time slices where spatial correlations can be exploited in a single time slice and temporal dependencies can be treated by exploiting time slices of different nodes
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Most WSN deployments can be categorized in one of two main applications depending on whether they provide continuous sensing (e.g., environmental or process monitoring) or perform event detection (e.g., forest fire detection or surveillance) While both share some common characteristics (such as the network structure), there are differences in their respective requirements especially concerning the expected lifetimes, the communication patterns, and the amount of data to be transferred. Several WSN applications densely deploy the sensor nodes to cover a wide area and/or have a fine spatial granularity resulting in large numbers of devices (ranging from tens up to thousands) This usually requires the sensor nodes to mainly consist of low-cost components to keep the deployment costs as low as possible
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