The Internet of Things (IoT) provides a virtual view, via the Internet Protocol, to a huge variety of real life objects, ranging from a car, to a teacup, to a building, to trees in a forest. Its appeal is the ubiquitous generalized access to the status and location of any “thing” we may be interested in. Wireless sensor networks (WSN) are well suited for long-term environmental data acquisition for IoT representation. This paper presents the functional design and implementation of a complete WSN platform that can be used for a range of long-term environmental monitoring IoT applications. The application requirements for low cost, high number of sensors, fast deployment, long lifetime, low maintenance, and high quality of service are considered in the specification and design of the platform and of all its components. Low-effort platform reuse is also considered starting from the specifications and at all design levels for a wide array of related monitoring applications.

Long-term wildfire monitoring using distributed in situ temperature sensors is an accurate, yet demanding environmental monitoring application, which requires long-life, low-maintenance, low-cost sensors and a simple, fast, error-proof deployment procedure. We present in this paper the most important design considerations and optimizations of all elements of a low-cost WSN platform prototype for long-term, low-maintenance pervasive wildfire monitoring, its preparation for a nearly three-month field test, the analysis of the causes of failure during the test and the lessons learned for platform improvement. The main components of the total cost of the platform (nodes, deployment and maintenance) are carefully analyzed and optimized for this application. The gateways are designed to operate with resources that are generally used for sensor nodes, while the requirements and cost of the sensor nodes are significantly lower. We define and test in simulation and in the field experiment a simple, but effective communication protocol for this application. It helps to lower the cost of the nodes and field deployment procedure, while extending the theoretical lifetime of the sensor nodes to over 16 years on a single 1 Ah lithium battery.

To increase railway use efficiency, the European Railway Traffic Management System (ERTMS) Level 3 requires all trains to constantly and reliably self-monitor and report their integrity and track position without infrastructure support. Timely train separation detection is challenging, especially for long freight trains without electrical power on cars. Data fusion of multiple monitoring techniques is currently investigated, including distributed integrity sensing of all train couplings. We propose a Wireless Sensor Network (WSN) topology, communication protocol, application, and sensor nodes prototypes designed for low power timely train integrity reporting in unreliable conditions, like intermittent node operation and network association (e.g., in low environmental energy harvesting conditions) and unreliable radio links. Each train coupling is redundantly monitored by four sensors, which can help to satisfy the Train Collision Avoidance System (TCAS) and European Committee for Electrotechnical Standardization (CENELEC) SIL 4 requirements and contribute to the reliability of the asynchronous network with low rejoin overhead. A control center on the locomotive controls the WSN and receives the reports, helping the integration in railway or Internet of Things (IoT) applications. Software simulations of the embedded application code virtually unchanged show that the energy-optimized configurations check a 50-car train integrity (about 1 km long) in 3.6 s average with 0.1 s standard deviation and that more than 95 % of the reports are delivered successfully with up to one-third of communications or up to 15 % of the nodes failed. We also report qualitative test results for a 20-node network in different experimental conditions.

Accurate tagless indoor person localization is important for several applications, such as assisted living and health monitoring. Machine learning (ML) classifiers can effectively mitigate sensor data variability and noise due to deployment-specific environmental conditions. In this paper, we use experimental data from a capacitive sensor-based indoor human localization system in a 3 m × 3 m room to comparatively analyze the performance of Weka collection ML classifiers. We compare the localization performance of the algorithms, its variation with the training set size, and the algorithm resource requirements for both training and inferring. The results show a large variance between algorithms, with the best accuracy, precision, and recall exceeding 93% and 0.05 m average localization error.

Accurate indoor person localization is essential for several services, such as assisted living. We introduce a tagless indoor person localization system based on capacitive sensing and localization algorithms that can determine the location with less than 0.2 m average error in a 3 m × 3 m room and has recall and precision better than 70%. We also discuss the effects of various noise types on the measurements and ways to reduce them using filters suitable for on-sensor implementation to lower communication energy consumption. We also compare the performance of several standard localization algorithms in terms of localization error, recall, precision, and accuracy of detection of the movement trajectory.