Recent technological innovations allow compact radios to transmit over long distances with minimal energy consumption and could drastically affect the way Internet of Things (IoT) technologies communicate in the near future. By extending the communication range of links, it is indeed possible to reduce the network diameter to a point that each node can communicate with almost every other node in the network directly. This drastically simplifies communication, removing the need of routing, and significantly reduces the overhead of data collection. Long-range low-power wireless technology, however, is still at its infancy, and it is yet unclear (i) whether it is sufficiently reliable to complement existing short-range and cellular technologies and (ii) which radio settings can sustain a high delivery rate while maximizing energy-efficiency. To shed light on this matter, this paper presents an extensive experimental study of the reliability of LoRa , one of the most promising long-range low-power wireless technologies to date. We focus our evaluation on the impact of physical layer settings on the effective data rate and energy efficiency of communications. Our results show that it is often not worth tuning parameters, thereby reducing the data rate in order to maximize the probability of successful reception, especially on links at the edge of their communication range. Furthermore, we study the impact of environmental factors on the performance of LoRa, and show that higher temperatures significantly decrease the received signal strength and may drastically affect packet reception.
Outworn water distribution infrastructures require real-time monitoring and management of water pressure and flow, together with accurate leak detection and localization techniques. Smart water networks based on wireless sensors offer a huge potential in this domain, but their deployment and maintenance is often costly and labor-intensive. In this paper, we present Adige: an efficient smart water network architecture based on long-range wireless technology that improves the scalability and robustness of water distribution systems. We developed a sensor node prototype using a LoRa radio transceiver and used it to carry out a set of experiments showing the benefits of Adige’s approach. Our evaluation shows that, in contrast to previous approaches, the use of long-range wireless technology allows to significantly reduce energy consumption while covering large areas indoors, outdoors, and underground.
Authors: Marco Cattani and Carlo Alberto Boano and David Steelbauer and Stefan Kaltenbacher and Markus Günther and Kay Römer and Daniela Fuchs-Hanusch and Martin Horn
Research groups: Institute for Technical Informatics, Institute of Urban Water Management and Landscape Water Engineering, Technical University of Graz
Conference: International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater)
Date: April 2017
For the Internet of Things to be people-centered, things need to identify when people and their things are nearby. In this article, we present the design, implementation, and deployment of a positioning system based on mobile and fixed inexpensive proximity sensors that we use to track when individuals are close to an instrumented object or placed at certain points of interest. To overcome loss of data between mobile and fixed sensors due to crowd density, traditional approaches are extended with mobile-to-mobile proximity information. We tested our system in a museum crowded with thousands of visitors, showing that measurement accuracy increases in the presence of more individuals wearing a proximity sensor. Furthermore, we show that den-
sity information can be leveraged to study the behavior of the visitors, for example, to track the popularity of points of interest, and the flow and distribution of visitors across floors.
Research groups: Large-scale Distributed Systems group, VU University Amsterdam and Embedded Software group, Delft University of Technology
Journal: IEEE Communications Magazine (CommMag)
Date: February 2017
This paper reports our experience with crowd monitoring technologies in the challenging real-world conditions of a modern, open-space museum. We seized the opportunity to use the NEMO science center as a testbed, and studied the effectiveness of neighborhood discovery and density estimation algorithms in a network formed by visitors wearing bracelets emitting RF beacons. The diverse set of conditions (flash crowds in open spaces vs. single person booths) revealed three interesting findings: (i) state-of-the-art density estimation fails in 80% of the cases, (ii) RSS-based classifiers fail too, because their underlying assumptions do not hold in many scenarios, and (iii) neighborhood discovery can obtain exact information in an energy-efficient way, provided that static and mobile nodes are differentiated to filter out “passers by” clobbering the true popularity of an exhibit. The overall lesson from the experiment is that today’s algorithms are quite far from the ideal of monitoring popularity in a privacy-preserving and energy-efficient way with minimal infrastructure across the set of heterogeneous conditions encountered in practice.
Acm Digital Library | Paper
Authors: Marco Cattani and Ioannis Protonotarios and Claudio Martella and and Joost Van Velzen and Marco A. Zuniga and Koen G. Langendoen
Research groups: Embedded Software group, Delft University of Technology and Large-scale Distributed Systems group, VU University Amsterdam
Conference: International Conference on Embedded Wireless Systems and Networks (EWSN)
Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.
Authors: Claudio Martella, Armando Miraglia, Jeana Frost, Marco Cattani, Maarten van Steen
Research group: Large-scale Distributed Systems group, VU University Amsterdam and Department of Communication Sciences at VU University Amsterdam and Embedded Software group, Delft University of Technology
Journal: Pervasive and Mobile Computing
When deploying a testbed infrastructure for Wireless Sensor Networks (WSNs), one of the most challenging features is to provide repeatable mobility. Wheeled robots, usually employed for such tasks, strive to adapt to the wide range of environments where WSNs are deployed, from chaotic office spaces to neatly raked potato fields. For this reason, wheeled robots often require an expensive customization step in order to adapt, for example, their localization and navigation systems to the specific environment. To avoid this issue, we present Gondola, a parametric robot infrastructure based on pulling wires, rather than wheels. Gondola avoids the most common problems of wheeled robots and easily adapts to many WSNs’ scenarios. Different from existing solutions, Gondola can easily move in 3-dimensional space, with no need of a complex localization system and with an accuracy that is comparable to o-the-shelf traditional robots.
Authors: Marco Cattani and Ioannis Protonotarios
Research groups: Embedded Software group, Delft University of Technology
Conference: 14th ACM Conf. on Embedded Networked Sensor Systems (SenSys)
Sensor networks can nowadays deliver 99.9% of their data with duty cycles below 1%. This remarkable performance is, however, dependent on some important underlying assumptions: low traffic rates, medium size densities and static nodes.
In this thesis, we investigate the performance of these same resource-constrained devices, but under scenarios that present extreme conditions: high traffic rates, high densities and mobility: the so-called Extreme Wireless Sensor Networks (EWSNs).