Bernhard Buchli

GPS-Equipped wireless sensor network node for high-accuracy positioning applications

This work presents the design, implementation, and end-to-end system concept and integration of a wireless data acquisition system for high-accuracy positioning applications. A wireless network of GPS-equipped sensor nodes, built from low-cost off-the-shelf components, autonomously acquires L1 GPS data for Differential GPS (DGPS) processing of raw satellite information. The differential processing on the backend infrastructure achieves relative position and motion of individual nodes within the network with sub-centimeter accuracy. Leveraging on global GPS time synchronization, network-wide synchronized measurement scheduling, and duty-cycling coupled with power optimized operation and robustness against harsh environmental conditions make the introduced sensor node well suited for monitoring or surveying applications in remote areas. Unattended operation, high spatial and temporal coverage and low cost distinguish this approach from traditional, very costly and time consuming approaches. The prototype data acquisition system based on a low-power mote equipped with a commercially available GPS module has been successfully implemented and validated in a testbed setting.

X-SENSE: Sensing in extreme environments

The field of Wireless Sensor Networks (WSNs) is now in a stage where serious applications of societal and economical importance are in reach. For example, it is well known that the global climate change dramatically influences the visual appearance of mountain areas like the European Alps. Very destructive geological processes may be triggered or intensified, impacting the stability of slopes, possibly inducing landslides. Unfortunately, the interactions between these complex processes is poorly understood. Therefore, one needs to develop wireless sensing technology as a new scientific instrument for environmental sensing under extreme conditions. Large variations in temperature, humidity, mechanical forces, snow coverage, and unattended operation play a crucial role in long-term deployments. We argue that, in order to significantly advance the application domain, it is inevitable that sensor networks be created as a quality scientific instrument with known and predictable properties, and not as a research toy delivering average observations at best. In this paper, key techniques for achieving highly reliable, yet resource efficent wireless sensor networks are discussed on the basis of productive wireless sensor networks measuring permafrost processes in the Swiss Alps.

Dynamic power management for long-term energy neutral operation of solar energy harvesting systems

In this work we consider a real-world environmental monitoring scenario that requires uninterrupted system operation over time periods on the order of multiple years. To achieve this goal, we propose a novel approach to dynamically adjust the systems performance level such that energy neutral operation, and thus long-term uninterrupted operation can be achieved. We first consider the annual dynamics of the energy source to design an appropriate power subsystem (i.e., solar panel size and energy store capacity), and then dynamically compute the long-term sustainable performance level at runtime. We show through trace-driven simulations using eleven years of real-world data that our approach outperforms existing predictive, e.g., EWMA, WCMA, and reactive, e.g., ENO-MAX, approaches in terms of average performance level by up to 177%, while reducing duty-cycle variance by up to three orders of magnitude. We further demonstrate the benefits of the dynamic power management scheme using a wireless sensor system deployed for environmental monitoring in a remote, high-alpine environment as a case study. A performance evaluation over two years reveals that the dynamic power management scheme achieves a two-fold improvement in system utility when compared to only applying appropriate capacity planning.

Zippy: On-Demand Network Flooding

In this paper, we tackle the challenge of rapidly disseminating rare events through a multi-hop network, while achieving unprecedented energy-efficiency. Contrary to state-of-the-art approaches, we circumvent the undesirable trade-offs associated with low-power duty-cycled protocols and backscatter technologies, and demonstrate a paradigm shift in low-power protocol design. We present Zippy, an on-demand flooding technique that provides robust asynchronous network wake-up, fine-grained per-hop synchronization and efficient data dissemination by leveraging low-complexity transmitter and receiver hardware. We are the first to demonstrate the on-demand flooding of rare events through a multi-hop network with end-to-end latencies of tens of milliseconds, while dissipating less than 10 microwatts during periods of inactivity. We present a prototype implementation of our proposed approach using a wireless sensor platform constructed from commercially available components. We extensively evaluate Zippys performance in a laboratory setting and in an indoor testbed.

Temporal Characteristics of Different Cryosphere-Related Slope Movements in High Mountains

Knowledge of processes and factors affecting slope instability is essential for detecting and monitoring potentially hazardous slopes. The overall aim of this study is to detect and characterize different slope movements in alpine periglacial environments, with the ultimate goal to understand the broad range of phenomena and processes encountered. In this article, our measurement-setup and our strategy for analyzing the spatio-temporal (seasonal and intra-annual) velocity fluctuations of various slope movements is explained and initial results are presented.

Towards Enabling Uninterrupted Long-Term Operation of Solar Energy Harvesting Embedded Systems

In this work we describe a systematic approach to power subsystem capacity planning for solar energy harvesting embedded systems, such that uninterrupted, long-term (i.e., multiple years) operation at a predefined performance level may be achieved. We propose a power subsystem capacity planning algorithm based on a modified astronomical model to approximate the harvestable energy and compute the required battery capacity for a given load and harvesting setup. The energy availability model takes as input the deployment sites latitude, the panel orientation and inclination angles, and an indication of expected meteorological and environmental conditions.We validate the models ability to predict the harvestable energy with power measurements of a solar panel. Through simulation with 10 years of solar traces from three different geographical locations and four harvesting setups, we demonstrate that our approach achieves 100% availability at up to 53% smaller batteries when compared to the state-of-the-art.

Optimal Power Management with Guaranteed Minimum Energy Utilization for Solar Energy Harvesting Systems

In this work we present the first formal study on optimizing the energy utilization of energy harvesting embedded systems while giving bounds on the minimum energy usage. Furthermore, to deal with the uncertainty inherent to solar energy harvesting we propose to use (i) a finite horizon scheme, and (ii) anon-uniformly scaled energy estimation based on an astronomical model. Under certain realistic assumptions, the finite horizon scheme can provide guarantees on minimum energy utilization, and therefore minimum utility. We show that a single non-uniform scaling function is applicable to solar energy traces from diverse locations. We further propose and evaluate a piece-wise linear approximation for efficient implementation as a small look-up table for resource constrained embedded systems. With extensive experimental evaluation for eight publicly available datasets and two datasets collected with our own deployments, we quantitatively establish that the proposed solution is highly effective at providing a guaranteed minimum utilization, and significantly out-performs four previously proposed solutions.