In situ observations of the Swiss periglacial environment using GNSS instruments

Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022),​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management

A decade of detailed observations (2008-2018) in steep bedrock permafrost at the Matterhorn Hörnligrat (Zermatt, CH)

The PermaSense project is an ongoing interdisciplinary effort between geo-science and engineering disciplines and started in 2006 with the goals of realizing observations that previously have not been possible. Specifically, the aims are to obtain measurements in unprecedented quantity and quality based on technological advances. This paper describes a unique >10-year data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500ma:s:l. Through the utilization of state-of-the-art wireless sensor technology it was possible to obtain more data of higher quality, make these data available in near real time and tightly monitor and control the running experiments. This data set (https://doi.org/10.1594/PANGAEA.897640,Weber et al., 2019a) constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over a period of 10 or more years. By documenting and sharing these data in this form we contribute to making our past research reproducible and facilitate future research based on these data, e.g., in the areas of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. Finally, the cross-validation of four different data types clearly indicates the dominance of thawing-related kinematics

Robustness of predictive energy harvesting systems: Analysis and adaptive prediction scaling

Internet of Things (IoT) systems can rely on energy harvesting to extend battery lifetimes or even render batteries obsolete. Such systems employ an energy scheduler to optimise their behaviour and thus performance by adapting the system's operation. Predictive models of harvesting sources, which are inherently non-deterministic and consequently challenging to predict, are often necessary for the scheduler to optimise performance. Because the inaccurate predictions are utilised by the scheduler, the predictive model's accuracy inevitably impacts the scheduler and system performance. This fact has largely been overlooked in the vast amount of available results on energy schedulers and predictors for harvesting-based systems. The authors systematically describe the effect prediction errors have on the scheduler and thus system performance by defining a novel robustness metric. To alleviate the severe impact prediction errors can have on the system performance, the authors propose an adaptive prediction scaling method that learns from the local environment and system behaviour. The authors demonstrate the concept of robustness with datasets from both outdoor and indoor scenarios. In addition, the authors highlight the improvement and overhead of the proposed adaptive prediction scaling method for both scenarios. It improves a non-robust system's performance by up to 13.8 times in a real-world setting.ISSN:1751-8601ISSN:1751-861

Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo

Synchronous Transmissions is a technology that combines energy efficiency and reliability for low-power wireless multi-hop networks. But using this technology to design network stacks is a complex task, in part due to the tight timing requirements on the execution of radio operations. To facilitate the development of protocols based on Synchronous Transmissions, we developed Baloo, a flexible network stack design framework, which we present in this paper. We show that Baloo is flexible enough to implement a wide variety of network layer protocols, while introducing only limited memory and energy overhead. Most importantly Baloo makes Synchronous Transmissions accessible: The software is open source and well documented. We believe that Baloo will be an important enabler for a whole new class of Internet of Things applications leveraging the reliability, efficiency, and flexibility of Synchronous Transmissions

Synchronous Transmissions Made Easy: Design Your Network Stack with Baloo

Synchronous Transmissions is a technology that combines energy efficiency and reliability for low-power wireless multi-hop networks. But using this technology to design network stacks is a complex task, in part due to the tight timing requirements on the execution of radio operations. To facilitate the development of protocols based on Synchronous Transmissions, we developed Baloo, a flexible network stack design framework, which we present in this paper. We show that Baloo is flexible enough to implement a wide variety of network layer protocols, while introducing only limited memory and energy overhead. Most importantly Baloo makes Synchronous Transmissions accessible: The software is open source and well documented. We believe that Baloo will be an important enabler for a whole new class of Internet of Things applications leveraging the reliability, efficiency, and flexibility of Synchronous Transmissions

Competition: Keep it Simple, Let Flooding Shine

This abstract presents our solutions to the EWSN 2019 Dependability Competition. We designed generic yet performant solutions by leveraging the flexibility of Baloo, a design framework tailored to synchronous transmissions. Instead of crafting subtle optimizations to match the Competition requirements, we used some well-known concepts and combined them efficiently to propose simple yet robust and efficient protocols. Keep it simple, let flooding shine

LSR: Energy-Efficient Multi-Modulation Communication for Inhomogeneous Wireless IoT Networks

In many real-world wireless IoT networks, the application dictates the location of the nodes and therefore the link characteristics are inhomogeneous. Furthermore, nodes may in many scenarios only communicate with the Internet-attached gateway via multiple hops. If an energy-efficient short-range modulation scheme is used, nodes that are reachable only via high-path-loss links cannot communicate. Using a more energy-demanding long-range modulation allows connecting more nodes but would be inefficient for nodes that are easily reachable via low-path-loss links. Combining multiple modulations is challenging as low-power radios usually only support the use of a single modulation at a time. In this paper, we present the Long-Short-Range (LSR) protocol which supports low-power multi-hop communication using multiple modulations and is suited for networks with inhomogeneous link characteristics. To reduce the inherent redundancy of long-range modulations, we present a method to determine the connectivity graph of the network during regular data communication without adding significant overhead. In simulations, we show that LSR allows for reducing power consumption significantly for many scenarios when compared to a state-of-the-art multi-hop communication protocol using a single long-range modulation. We demonstrate the applicability of LSR with an implementation on real hardware and a testbed with long-range links.ISSN:2691-1914ISSN:2577-620