The Case for a Rigorous Approach to Automating Software Operations and Management of Large-scale Sensor Networks
Software Operations and Management (O&M) i.e., installing, configuring, and updating thousands of software components within a conventional Data Center is a well-understood issue. Existing frameworks such as the Rocks toolkit [8] have revolutionized the way system administrators deploy and manage large-scale compute clusters, storage servers, and visualization facilities. However, existing tools like Rocks are designed for a friendly Data Center environment where stable power along with high-performance compute, storage, and networking is the norm. In contrast, sensor networks are embedded deeply within the harsh physical environment where node failures, node mobility and idiosyncrasies of wireless networks are the norm. In addition, device heterogeneity and resource-constrained nature (e.g., power, memory, CPU capability) of the sensor cyberinfrastructure (CI) are realities that must be addressed and reconciled. Although sensor CI must be more adaptable and more-rapidly reconfigurable than the data center equivalents, few if any of the existing software O&M tools and techniques have been adapted to the significantly more challenging environment of sensor networks. A more automated approach to software O&M would provide significant benefits to system builders, operators, and sensor network researchers. We argue that by starting with software O&M techniques developed for data centers, and then adapting and extending them to the world of resource-constrained sensor networks, we will be able to provide robust and scientifically reproducible mechanisms for defining the software footprint of individual sensors and networks of sensors. This paper describes the current golden-image based software O&M practice in Android world. We then propose an approach that adapts the Rocks toolkit to allow one to rapidly and reliably build complete Android environments (firmware flashes) at the individual sensor level and extend to a large networks of diverse sensors.
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