Measuring, Understanding, and Estimating the Influence of the Environment on low-power Wireless Networks

After a decade and a half of research in academia and industry, wireless sensor networks (WSNs) are seen as a key infrastructure able to monitor the environment in which they are immersed, thanks to their miniaturization, autonomy, and flexibility. Still, outdoor deployments of WSNs (e.g., in forests) are notoriously difficult to get right, partly due to the fact that their low-power wireless communication is greatly affected by the characteristics of the target environment (e.g., temperature, humidity, foliage). In the absence of quantitative evidence about the target application environments, the asset that drives a successful and reliable outdoor deployment is the experience gained from previous deployments, lab-like testbeds, or simulators that however often do not resemble the real-world environments. The general goal of this dissertation is to support the principled design and deployment of WSNs by improving the understanding of how the natural outdoor environment affects the network stack, and providing tools and modeling techniques to address this impact. This constitutes the premise for WSNs to be a credible tool for domain experts (e.g., biologists) operating in this field. Our own practical need to design and deploy a reliable WSN system for wildlife monitoring in the mountains near Trento, Italy, pushed our goals towards a deployment and application oriented perspective, whose ultimate objectives are: supporting the WSN deployment; informing the selection or design of protocols, to ensure they are well-suited to the target environment; deriving models to push the envelope of what can be predicted or simulated beforehand. To achieve these goals we must start from the first step—assessing quantitatively the characteristics of the low-power wireless links in-field, i.e., in the environment where the WSN must be deployed. To this end, we contribute with Trident and Harpoon, tools for in-field connectivity and routing performance assessment that support principled, repeatable, automated, and flexible collection of measurements in the target environment without the need for a tethered infrastructure and without requiring coding from the end user. Then, using these tools we collect a large set of data traces from six campaigns across different years, environments and seasons, whose analysis quantified the impact of the environmental factors on the network stack, focusing primarily on the physical and routing layers. Finally, we exploit the data traces to create models for both estimating the link quality at run-time and reproducing realistic network conditions in simulators. We argue that the tools we expressly designed for gathering in-field empirical traces, the understanding and quantitative characterization of data traces from real environments, and the modeling, together significantly advance the state of the art by rendering the process of designing and deploying a WSN more repeatable and predictable.