Abstract
Wireless sensor networks (WSNs) require an extremely energy-efficient design. As sensor nodes have limited power sources, the problem of autonomy is crucial. Energy harvesting provides a potential solution to this problem. However, as current energy harvesters produce only a small amount of energy and their storage capacity is limited, efficient power management techniques must also be considered. In this article we address the problem of modeling and simulating energy harvesting WSN nodes with efficient power management policies. We propose furthermore a framework that permits to describe and simulate an energy harvesting sensor node by using a high level modeling approach based on power consumption and energy harvesting. The node architectural parameters as well as the on-line power management techniques will also be specified. Two new power management architectures will be introduced, taking into account energy-neutral and negative-energy conditions. Simulations results show that the throughput of a sensor node can be improved up to 50% when compared to a state of the art power management algorithm for solar harvesting WSN. The simulation framework is then used to find an efficient system sizing for a solar energy harvesting WSN node.
Highlights
Energy supply is still a limiting factor for embedded systems, such as Wireless sensor networks (WSNs)
Since M is used in the negativeenergy strategy, only the data-rate during zero energy intervals (ZEIs) is affected by this parameter
We have presented a high level modeling approach, based on the α and β parameters, used to describe a generic energy harvesting system
Summary
Energy supply is still a limiting factor for embedded systems, such as Wireless sensor networks (WSNs). The gap between the energy that can be stored in current energy storage devices, such as batteries and supercapacitors, and the power consumption of the electronic circuitry limits the system’s lifespan. The lifespan of a WSN deployments depends on the type of events that are monitored. Published studies dealing with battery powered WSN deployments (e.g., [1]) show that lifetimes can vary from a few days to several years. Like structural monitoring [2], can have a lifetime of few days. Environmental sensing applications, like tracking animal movements, forest fire or flood detection [3] (as well as several other applications) require lower data rate and can achieve longer lifetimes for an equivalent energy budget
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