Self-sustainability for energy scavenging networks is a crucial step in modern sensor network developments. Most existing analyses are however tailored to a single transmitting node or are difficult to map into practical protocol designs. Here, we offer a comprehensive framework for self-sufficient sensor networks powered by renewable energy sources. To this extent, we decompose the problem in two nested optimization steps: in the inner one we characterize the optimal operating point of the network subject to a required energy consumption figure, while in the  outer step , we provide optimal energy management policies to make the system self-sufficient , given the the statistical description of the energy source . Our framework   permits to gauge the impact of key sensor network parameters, such as the battery size , the harvester size (e.g., solar panel), the information transmission rate and the nodes' duty cycle. In addition, the closed form solution of the inner optimization problem lends itself to the implementation, as an online algorithm, on computationally constrained embedded devices. Finally, we provide results describing the consequences of most of the relevant design choices in terms of network behavior, maximum achievable throughput and dynamics associated with the optimal energy management policies .
Self-sustainability is a crucial step for modern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies making the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources . Our framework sheds new light into the design of pragmatic schemes for the control of energy harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity , the harvester size , the information transmission rate and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state of the art solutions and describing the impact of relevant design choices in terms of achievable network throughput and battery level dynamics .
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In addition, the closed form solution of the inner optimization problem lends itself to the implementation, as an online algorithm, on computationally constrained embedded devices. Finally, we provide results describing the consequences of most of the
We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state of the art solutions and describing the impact of
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