Sensor-Rocks: A novel integrated framework to improve software Operations and Management (O&M) and power management in environmental observing systems

Long-term deployments of sensor-based environmental observing systems
face two critical challenges namely, a manual software
Operations and Management (O&M) approach that does not scale and a
power management approach that does not meet the functional, operational,
financial, and policy requirements. In this proposal we will develop an
integrated approach that will address both these challenges.

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 has revolutionized the way system administrators deploy and manage
large-scale compute clusters, storage servers, and visualization facilities.
However, the existing tools 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. An automated approach to software O&M would provide significant
benefits to system builders, operators, and sensor network researchers.
To that end, we will develop Sensor-Rocks, an innovative platform that utilizes,
adapts, and extends the Rocks toolkit, used in numerous academic data centers
and NSF track 2 systems, for defining the software footprint of individual
sensors and network of sensors. The resulting faster, reliable deployment
of the software infrastructure fundamentally improves the reproducibility,
which is a key for conducting good-quality science and sensor network research.

Long-term science experiments that require high-resolution sensor data need
platforms (e.g. buoys or towers), which are large enough to house large solar
panels and bulky batteries. The current practice of using large form factor
platforms poses significant functional, operational, financial, and policy
challenges. For example, large solar panels and batteries cost more money and
result in less portable platforms which are harder to deploy and maintain.
In addition, policy decisions often regulate the form factor of buoys that can
be deployed in public lakes, oceans, or forests (e.g., to preserve aesthetic value).
Although scientists would like to go with miniaturized buoys, energy-constraints
imposed by small solar panels and batteries limit their deployment duration.
To that end, building on a well defined "sensor appliance" created using
Sensor-Rocks, we will develop novel context-aware power management algorithms
that will maximize the network lifetime and provide scientists unprecedented
capability to conduct long-term experiments using miniaturized platforms.