Sensor node faults are a serious threat to wireless sensor networks. They can cause node crashes or lead to the transmission of corrupted data. Especially the latter endangers the quality of subsequent data analyses. Most related fault detection approaches consider the sensor nodes as black boxes. They neglect vital information available on the node level. Consequently, most of these approaches can not distinguish between (i) irregular but correctly sensed data events and (ii) data corruption caused by soft faults. In contrast, our contribution integrates node-level diagnostics with the characteristics of the sensor data. We utilize this node-level diagnostic information to present our fault detection approach. Based on simulations and practical experiments, we show the correctness and efficiency of our approach. The results show that our approach offers a high fault detection rate and can differentiate between events and faults. Furthermore, it consumes a justifiably small overhead of resources and energy.

Over the last years, a significant amount of research papers based their practical evaluation of wireless sensor network-related topics on deployments consisting of Arduino-based sensor nodes. Arduino, on the other hand, offers prototyping boards not tailored for in-the-field deployments. Also, the boards are not specifically designed for ultra low power operation required for long-lasting wireless sensor nodes.We consequently raise the question, whether Arduino is indeed a suitable platform for wireless sensor nodes. For this reason, we evaluated recent Arduino boards by (i) comparing their specifications with common sensor nodes and (ii) analyzing their power consumption in active as well as different sleep modes. Additionally, we evaluated the software components provided by Arduino (i.e., bootloader and libraries).In conclusion, Arduino offers an easy-to-use and cheap basis for early development or analysis, but it falls short in terms of high power consumption as well as issues caused by the bootloader and other integrity issues in the libraries. Therefore, Arduino may be suitable to support the sensor node development but has noticeable drawbacks for actual deployments.

Faults in wireless sensor nodes tend to be the norm rather than the exception. Our paper addresses the following problems: How can we make sure the sensed data has not been distorted by faults? And when we experience anomalies in the sensed data, how can we distinguish between rare but correctly measured events and incorrect data due to faults? The focus of this paper is specifically on soft faults, because (i) they deteriorate the data quality and (ii) are hard to distinguish from irregular but correct events. Many papers on how to detect soft faults have been proposed, but they mostly rely on assumptions on the network's deployment or the nature of the collected data. Therefore, they are insufficient as they can miss faults or misinterpret correct data. In this paper, our key idea is to augment the existing fault-detection approaches with node-level information as additional input. Our contribution is to show the existence of such node-level information that can improve (i) the detection of soft faults and (ii) the distinction between faults and events. This node-level information - called fault indicators - can then be included in existing fault detection schemes. Based on practical experiments, we show that using such fault indicators indeed improves the detection rate and reduces the risk of missing fault-induced variations of sensor data.

Wireless sensor networks play an important role in today's world: When measuring physical conditions, the quality of the sensor readings ultimately impacts the quality of various data analytical services. To maintain data correctness and quality, run-time measures such as anomaly detection techniques are gaining significance. In particular, the detection of threatening node anomalies caused by sensor node faults has become a crucial task.