Citation: El Houssaini, D.; Khriji, S.; Viehweger, C.; Keutel, T.; Kanoun, O. Location-Aware IoT-Enabled Wireless Sensor Networks for Landslide Early Warning. Electronics 2022, 11, 3971. https://doi.org/10.3390/ electronics11233971 Academic Editor: Dimitris Kanellopoulos Received: 18 November 2022 Accepted: 27 November 2022 Published: 30 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Location-Aware IoT-Enabled Wireless Sensor Networks for Landslide Early Warning Dhouha El Houssaini * , Sabrine Khriji , Christian Viehweger, Thomas Keutel and Olfa Kanoun * Chair of Measurement and Sensor Technology, Chemnitz University of Technology, 09111 Chemnitz, Germany * Correspondence: dhouha.el-houssaini@etit.tu-chemnitz.de (D.E.H.); olfa.kanoun@etit.tu-chemnitz.de (O.K.) Abstract: Wireless Sensor Networks (WSNs) represent an interesting technology for designing early warning systems for landslides as they can ensure real-time and continuous monitoring. Through accurate localization techniques, changes in the position of installed nodes can be detected even during the early stage of field instability. This is through an accurate detection of nodes position changes independently from systematic deviations resulting from outdoor environmental conditions. In this study, we propose an accurate measurement system for distance measurement between wireless sensor nodes based on an ultra-wideband (UWB) localization method. In particular, distance measurements at different real weather conditions were performed to identify the impact of weather changes on distance measurement deviations. A prototype for a landslide warning system has been developed realizing a localization accuracy of 98%. Keywords: WSN; localization; ultra-wideband; two-way ranging; environment changes; early warning; landslide; SVM; internet of things 1. Introduction Various fields have been promoting Wireless Sensor Networks (WSNs) in recent years, including industrial, environmental, and natural hazard monitoring [1,2]. In particular, WSNs have been actively introduced for risk management and natural hazard monitoring and prevention, where more attention is required to develop innovative and autonomous solutions to counteract life-threatening natural disasters [3–5]. In this regard, continuous field monitoring systems for early warning detection is highly needed to enable hazard assessments and risk management. Along with the use of WSNs, integrating the Internet of Things (IoT) concept ensures the remote monitoring of large areas in real-time [6–8]. Several Early Warning Systems (EWSs) are being developed to maintain control of vul- nerable regions and facilitate immediate intervention in natural disasters. In particular, EWSs integrate WSNs to collect sensor data information, i.e., soil moisture, water level, soil inclination, and temperature, which are indicators of environmental changes. Fur- thermore, WSNs enable flexible and remote data collection, and analysis from distributed sensors [3,9,10]. In landslide applications, critical requirements, such as real-time and con- tinuous measurements, should be considered for designing and implementing the wireless network. A landslide early warning system (LEWS) must ensure flexible and remote access to the monitored area. Furthermore, LEWS provides accurate and precise information allowing the real detection of the landslide. Moreover, the LEWS needs to distinguish between normal weather change (e.g., temperature change between day and night) and landslide triggers (e.g., inclination of soil, severe change in water drop level). Therefore, the LEWS should be weather dependent for the identification of weather influence on collected data. 2. Related Works In [11], the authors developed a landslide monitoring system based on WSNs. Their sensor nodes integrated a pore water pressure sensor and strain gauges to measure the Electronics 2022, 11, 3971. https://doi.org/10.3390/electronics11233971 https://www.mdpi.com/journal/electronics