This paper describes a systematic approach to building micro-solar power subsystems for wireless sensor network nodes. Our approach composes models of the basic pieces -solar panels, regulators, energy storage elements, and application loads -to appropriately select and size the components. We demonstrate our approach in the context of a microclimate monitoring project through the design of the node, micro-solar subsystem, and network, which is deployed in a challenging, deep forest setting. We evaluate our deployment by analyzing the effects of the range of solar profiles experienced across the network.

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We present a building block approach to hardware platform design based on a decade of collective experience in this area, arriving at an architecture in which general-purpose modules that require expertise to design and incorporate commonlyused functionality are integrated with application-specific carriers that satisfy the unique sensing, power supply, and mechanical constraints of an application. Of course, modules are widespread, but our focus is far less on the performance of any individual module and far more on an overall architecture that supports the prototype, pilot, and production stages of design, and preserves the artifacts and learnings accumulated along the way.We present heuristics for partitioning functionality between modules and carriers, and identify guidelines for their interconnection. Our approach advocates exporting a wide electrical interface, eliminating the system bus, and supporting many physical interconnect options for modules and carriers. We evaluate this approach by constructing a family of general-purpose modules and application-specific carriers that achieve a high degree of reuse despite very different application requirements. We show that this approach shortens platform development time-to-result for novice graduate students, making custom platforms broadly accessible.

We present an incremental network programming mechanism which reprograms wireless sensors quickly by transmitting the incremental changes using the Rsync algorithm; we generate the difference of the two program images allowing us to distribute only the key changes. Unlike previous approaches, our design does not assume any prior knowledge of the program code structure and can be applied to any hardware platform. To meet the resource constraints of wireless sensors, we tuned the Rsync algorithm which was originally made for updating binary files among powerful host machines. The sensor node processes the delivery and the decoding of the difference script separately making it easy to extend for multi-hop network programming. We are able to get a speed-up of 9.1 for changing a constant and 2.1 to 2.5 for changing a few lines in the source code

Autonomous long-term monitoring is an essential capability of wireless sensor networks, and solar energy is a viable means of enabling this capability due to its high power density and wide availability. However, micro-solar power system design is challenging because it must address long-term system behavior under highly variable solar energy and consider a large space of design options. Several micro-solar power systems have been designed and implemented, validating particular points in the whole design space.In this dissertation we develop a practical theory of micro-solar power systems that is materialized in a simulation suite that models component and system behavior over a long time-scale and in an external environment that depends on time, location, weather and local conditions. This simulation provides sufficient accuracy to guide specific design choices in a large design space. This design tool is very different from the many "macro-solar" calculators, which model typical behavior of kilowatt systems in the best conditions, rather than detailed behavior of milliwatt systems in the worst conditions. We provide a general architecture of micro-solar power systems, comprising key components and interconnections among the components, and formalize each component in an analytical or empirical model of its behavior.We incorporate these component models and their interconnections in the simulation suite.Our discrete time-event simulation models the daily behavior and the long-term behavior by iteratively evaluating the state of the system in the context of its solar i