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 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 reallife 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.

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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.

Abstract-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.