We consider a node in a multi-hop wireless network that is responsible for transmitting messages in a timely manner while being prudent about energy consumption. The node is powered by batteries that are charged by renewal energy sources such as wind or solar. To strike a balance between latency and availability, we consider a multi-timescale model. At a faster timescale, the node makes decisions based on local information such as queue lengths of packets in input buffers and available energy levels. The decisions include scheduling packets on the output buffers that would be transmitted at the next opportunity. At the slower timescale we model the energy levels in the battery using a stochastic fluid-flow model to determine the availability. Ultimately we present a unified framework that iteratively sets model parameters to satisfy latency and availability targets. The methods are based on Markov decision processes, Markov chains and semi-Markov process analysis.
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