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

Unleashing the potential of Real-time Internet of Things

With the recent surge in the interest for the Internet of Things (IoT) and an increased deployment of cyber-physical systems (CPS) in commercial and indus- trial applications, distributed systems have gained a significance influence on modern civilization and are performing increasingly complex tasks. Building such platforms in a reliable manner is challenging, as they include concurrent tasks on the application and the communication layers. As the majority of such devices features a single processor, tasked with both communicating over the network as well as sensing and computing, real-time scheduling conflicts arise as the resource separation of the applications in software is difficult to manage. To achieve such independence, we propose a platform consisting of dedicated application (AP) and communication (CP) processors which are completely de- coupled in terms of resource access, clock speeds and power management using BOLT. Leveraging this hardware separation, we then use the Distributed Real-time Protocol (DRP) to provably provide end-to-end real-time guaran- tees for the communication between distributed applications over a multi-hop wireless network. By establishing a set of contracts at run-time, DRP ensures that all messages reaching their destination meet their hard deadline. To demon- strate this, we implement the BLINK scheduler directly on the AP and adapt the LWB round structure to use DRP as a control layer protocol. We show that our system is capable of supporting several hundred simultaneous streams and can respond to requests in maximally 3 stream periods over up to 10 hops

Demos: Robust Orchestration for Autonomous Networking

Research in wireless sensor networks has resulted in a remarkable breadth of highly capable systems. However, while specialized protocols perform well in the setting they were designed for, they often lack the ability to quickly adapt once operating conditions change drastically. Of particular importance is resilience to node and link failures, as clusters of nodes that lost their leader or split apart need to re-organize and find each other again. With Demos, we present a low-power wireless protocol that ensures robust network orchestration despite such failures. Demos rapidly finds consensus on leadership with its cluster coordination mechanism even if the set of nodes fluctuates by introducing new election quorums. In addition, a novel cluster discovery scheme enables autonomous clusters to merge on the fly and maximize network coverage. Experiments with controlled mobility on a multi-hop network of 24 nodes demonstrate that Demos maintains a reliable data exchange despite severe disruptions and adapts to changes within seconds. We further find that Demos' ability to continuously coordinate and discover achieves highly robust orchestration of fully autonomous clusters

BUTLER: Increasing the Availability of Low-Power Wireless Communication Protocols

Over the past years, various low-power wireless protocols based on synchronous transmissions (ST) have been developed to meet the high dependability requirements of emerging cyber-physical applications. For example, Wireless Paxos provides consensus, a key mechanism for building fault-tolerant systems through replication. However, Wireless Paxos and other ST-based protocols are themselves not fault-tolerant: They suffer from a single point of failure that fundamentally impairs the availability of the communication service in the presence of node crashes and network partitions.We present BUTLER, a mechanism that allows removing the single point of failure in many ST-based protocols. BUTLER synchronizes all nodes in the network so that the communication process can be jointly started by multiple randomly chosen nodes rather than a single dedicated node. We analyze and formally prove the correctness of BUTLER and implement it on the state-of-the-art nRF52840 platform. Experiments on the FlockLab testbed demonstrate that BUTLER reliably synchronizes the network to within 3 s despite large initial offsets, unpredictable node failures, and network partitions. BUTLER's temporal overhead ranges well below 1 %. Because of this efficiency and effectiveness, our results further indicate that BUTLER can dramatically improve the availability of an existing ST-based protocol without any noticeable impact on the overall communication reliability and efficiency

Measuring what Really Matters: Optimizing Neural Networks for TinyML

With the surge of inexpensive computational and memory resources, neural networks (NNs) have experienced an unprecedented growth in architectural and computational complexity. Introducing NNs to resource-constrained devices enables cost-efficient deployments, widespread availability, and the preservation of sensitive data. This work addresses the challenges of bringing MachineLearning to microcontroller units (MCUs), where we focus on the ubiquitous ARM Cortex-M architecture. The detailed effects and trade-offs that optimization methods, software frameworks, and MCU hardware architecture have on key performance metrics such as inference latency and energy consumption have not been previously studied in depth for state-of-the-art frameworks such as TensorFlow Lite Micro. We find that empirical investigations which measure the perceptible metrics –performance as experienced by the user– are indispensable, as the impact of specialized instructions and layer types can be subtle. To this end, we propose an implementation-aware design as a cost-effective method for verification and benchmarking. Employing our developed toolchain, we demonstrate how existing NN deployments on resource-constrained devices can be improved by systematically optimizing NNs to their targeted application scenario

SociTrack: Infrastructure-Free Interaction Tracking through Mobile Sensor Networks

Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity—they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions