Guillermo Barrenetxea

SensorScope: Out-of-the-Box Environmental Monitoring

Environmental monitoring constitutes an important field of application for wireless sensor networks. Given the severity of potential climate changes, environmental impact on cities, and pollution, it is a domain where sensor networks can have great impact and as such, is getting more and more attention. Current data collection techniques are indeed rather limited and make use of very expensive sensing stations, leading to a lack of appropriate observations. In this paper, we present SensorScope, a collaborative project between environmental and network researchers, that aims at providing an efficient and inexpensive out-of-the-box environmental monitoring system, based on a wireless sensor network. We especially focus on data gathering and present the hardware and network architecture of SensorScope. We also describe a real-world deployment, which took place on a rock glacier in Switzerland, as well as the results we obtained.

The hitchhiker's guide to successful wireless sensor network deployments

The successful deployment of a wireless sensor network is a difficult task, littered with traps and pitfalls. Even a functional network does not guarantee gathering meaningful data. In SensorScope, with its multiple campaigns in various environments (e.g., urban, high-mountain), we have acquired much knowledge in planning, conducting, and managing real-world sensor network deployments. In this paper, we share our experience by stepping through the entire process, from the preparatory hard- and software development to the actual field deployment. Illustrated by numerous real-life examples, excerpted from our own experience, we point out many potential problems along this way and their possible solutions. We also indicate the importance of a close interaction with the end-user community in planning and running the network, and finally exploiting the data.

SensorScope: Application-specific sensor network for environmental monitoring

SensorScope is a turnkey solution for environmental monitoring systems, based on a wireless sensor network and resulting from a collaboration between environmental and network researchers. Given the interest in climate change, environmental monitoring is a domain where sensor networks will have great impact by providing high resolution spatio-temporal data for long periods of time. SensorScope is such a system, which has already been successfully deployed multiple times in various environments (e.g., mountainous, urban). Here, we describe the overall hardware and software architectures and especially focus on the sensor network itself. We also describe one of our most prominent deployments, on top of a rock glacier in Switzerland, which resulted in the description of a micro-climate phenomenon leading to cold air release from a rock-covered glacier in a region of high alpine risks. Another focus of this paper is the description of what happened behind the scenes to turn SensorScope from a laboratory experiment into successful outdoor deployments in harsh environments. Illustrated by various examples, we point out many lessons learned while working on the project. We indicate the importance of simple code, well suited to the application, as well as the value of close interaction with end-users in planning and running the network and finally exploiting the data.

Wireless Sensor Networks for Environmental Monitoring: The SensorScope Experience

While wireless sensor networks have been extensively studied in the past few years, most results are of theoretical nature and were obtained outside of a practical context. This can be problematic for real applications, especially in the area of environmental monitoring where many factors, such as harsh weather conditions, can greatly influence the performance of such a network, while reliable delivery and high-quality measurements are required. SensorScope is an interdisciplinary project, elaborated by environmental and networking researchers, that aims at narrowing the gap between theory and practice. Several successful real-world deployments have already been undertaken in rugged environments. In this paper, we analyze the particular requirements of environmental monitoring and how these requirements have been met in the SensorScope project. We also present an application example of a deployment, undertaken in a harsh mountain environment.

OpenSense: open community driven sensing of environment

This paper outlines a vision for community-driven sensing of our environment. At its core, community sensing is a dynamic new form of mobile geosensor network. We believe that community sensing networks, in order to be widely deployable and sustainable, need to follow utilitarian approaches towards sensing and data management. Current projects exploring community sensing have paid less attention to these underlying fundamental principles. We illustrate this vision through OpenSense -- a large project that aims to explore community sensing driven by air pollution monitoring.

Hydrologic response of an alpine watershed: Application of a meteorological wireless sensor network to understand streamflow generation

A field measurement campaign was conducted from June to October 2009 in a 20 km2 catchment of the Swiss Alps with a wireless network of 12 weather stations and river discharge monitoring. The objective was to investigate the spatial variability of meteorological forcing and to assess its impact on streamflow generation. The analysis of the runoff dynamics highlighted the important contribution of snowmelt from spring to early summer. During the entire experimental period, the streamflow discharge was dominated by base flow contributions with temporal variations due to occasional rainfall-runoff events and a regular contribution from glacier melt. Given the importance of snow and ice melt runoff in this catchment, patterns of near-surface air temperatures were studied in detail. Statistical data analyses revealed that meteorological variables inside the watershed exhibit spatial variability. Air temperatures were influenced by topographic effects such as slope, aspect, and elevation. Rainfall was found to be spatially variable inside the catchment. The impact of this variability on streamflow generation was assessed using a lumped degree-day model. Despite the variability within the watershed, the streamflow discharge could be described using the lumped model. The novelty of this work mainly consists in quantifying spatial variability for a small watershed and showing to which extent this is important. When the focus is on aggregated outputs, such as streamflow discharge, average values of meteorological forcing can be adequately used. On the contrary, when the focus is on distributed fields such as evaporation or soil moisture, their estimate can benefit from distributed measurements.

Stream Temperature Response to Three Riparian Vegetation Scenarios by Use of a Distributed Temperature Validated Model

Elevated in-stream temperature has led to a surge in the occurrence of parasitic intrusion proliferative kidney disease and has resulted in fish kills throughout Switzerlands waterways. Data from distributed temperature sensing (DTS) in-stream measurements for three cloud-free days in August 2007 over a 1260 m stretch of the Boiron de Merges River in southwest Switzerland were used to calibrate and validate a physically based one-dimensional stream temperature model. Stream temperature response to three distinct riparian conditions were then modeled: open, in-stream reeds, and forest cover. Simulation predicted a mean peak stream temperature increase of 0.7 °C if current vegetation was removed, an increase of 0.1 °C if dense reeds covered the entire stream reach, and a decrease of 1.2 °C if a mature riparian forest covered the entire reach. Understanding that full vegetation canopy cover is the optimal riparian management option for limiting stream temperature, in-stream reeds, which require no riparian set-aside and grow very quickly, appear to provide substantial thermal control, potentially useful for land-use management.

Lattice networks: capacity limits, optimal routing, and queueing behavior

Lattice networks are widely used in regular settings like grid computing, distributed control, satellite constellations, and sensor networks. Thus, limits on capacity, optimal routing policies, and performance with finite buffers are key issues and are addressed in this paper. In particular, we study the routing algorithms that achieve the maximum rate per node for infinite and finite buffers in the nodes and different communication models, namely uniform communications, central data gathering and border data gathering. In the case of nodes with infinite buffers, we determine the capacity of the network and we characterize the set of optimal routing algorithms that achieve capacity. In the case of nodes with finite buffers, we approximate the queue network problem and obtain the distribution on the queue size at the nodes. This distribution allows us to study the effect of routing on the queue distribution and derive the algorithms that achieve the maximum rate

Howis the weather: Automatic inference from images

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.

Share risk and energy: Sampling and communication strategies for multi-camera wireless monitoring networks

In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multi-camera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm. Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%-50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower.