This paper presents the design, deployment, and evaluation of a real-world sensor network system in an active volcano - Mount St. Helens. In volcano monitoring, the maintenance is extremely hard and system robustness is one of the biggest concerns. However, most system research to date has focused more on performance improvement and less on system robustness. In our system design, to address this challenge, automatic fault detection and recovery mechanisms were designed to autonomously roll the system back to the initial state if exceptions occur. To enable remote management, we designed a configurable sensing and flexible remote command and control mechanism with the support of a reliable dissemination protocol. To maximize data quality, we designed event detection algorithms to identify volcanic events and prioritize the data, and then deliver higher priority data with higher delivery ratio with an adaptive data transmission protocol. Also, a light-weight adaptive linear predictive compression algorithm and localized TDMA MAC protocol were designed to improve network throughput. With these techniques and other improvements on intelligence and robustness based on a previous trial deployment, we air-dropped 13 stations into the crater and around the flanks of Mount St. Helens in July 2009. During the deployment, the nodes autonomously discovered each other even in-the-sky and formed a smart mesh network for data delivery immediately. We conducted rigorous system evaluations and discovered many interesting findings on data quality, radio connectivity, network performance, as well as the influence of environmental factors.

In this paper, we propose a lightweight and transparent management framework for TinyOS sensor networks, called L-SNMS, which minimizes the overhead of management functions, including memory usage overhead, network traffic overhead, and integration overhead. We accomplish this by making L-SNMS virtually transparent to other applications hence requiring minimal integration. The proposed L-SNMS framework has been successfully tested on various sensor node platforms, including TelosB, MICAz and IMote2.