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We investigate the theoretical limits of positioning algorithms. In particular, we study scenarios where the nodes do not receive anchors directly (multi-hop) and where no physical distance or angle information whatsoever is available (connectivity-based). Since we envision large-scale sensor networks as an application, we are interested in fast, distributed algorithms. As such, we show that plain hop algorithms are not competitive. Instead, for one-dimensional unit disk graphs we present an optimal algorithm HS. For two or more dimensions, we propose an algorithm GHoST which improves upon the basic hop algorithm in theory and in simulations.

Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.

A wireless sensor network is a network made up of many tiny intercommunicating computers equipped with one or several sensors. Each tiny computer represents a node of the network. The nodes are self‐contained units typically consisting of a power supply with limited capacity, a radio transceiver, a microcontroller, and one or more sensors. This article gives an introduction to wireless sensor networks for structural health monitoring, shows the general architecture of sensor nodes, and overviews current hardware and software platforms. Furthermore, it shows the characteristics and limits of such monitoring systems and gives advice to choose a suitable platform to an application. Because nodes are powered by an autonomous source, energy‐related aspects, energy storage, and scavenging are presented as well.

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

Microelectromechanical system (MEMS) sensors are small, generally low power, highly integrated, and, usually, very cheap. These qualities enable the deployment of structural health monitoring (SHM) systems with a large number of sensors, partly integrated into the structure, at affordable costs. MEMS sensors are often used in wireless sensor networks (WSNs), a monitoring technology that heavily bases its features and performance on low‐power sensors. This article briefly describes a MEMS‐based wireless sensor network that is designed for long‐term structural health monitoring applications. Since WSN nodes are battery powered, in long‐term monitoring applications the power management of the network significantly influences the overall data handling processes. In WSN, data communication is the most energy‐consuming task, hence, transferring all the acquired raw data through the network would dramatically reduce the lifetime of the system. A significant data reduction, which has to be achieved on the nodes, is a challenging task, since it has to be performed with very limited computing power and memory resources, and in competition with other tasks like data communication, self‐organization, and time synchronization. These aspects are illustrated with tests on a cable stay bridge.

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