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Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the environment. The algorithm has three objectives, namely (a) achieving energy neutral operation, i.e., energy consumption should not be more than the energy provided by the environment, (b) maximizing the system performance based on an application utility model subject to the above energy-neutrality constraint, and (c) adapting to the dynamics of the energy source at run-time. We present a model that enables harvesting sensor nodes to predict future energy opportunities based on historical data. We also derive an upper bound on the maximum achievable performance assuming perfect knowledge about the future behavior of the energy source. Our methods are evaluated using data gathered from a prototype solar energy harvesting platform and we show that our algorithm can utilize up to 58% more environmental energy compared to the case when harvesting-aware power management is not used.

Energy harvesting offers a promising alternative to solve the sustainability limitations arising from battery size constraints in sensor networks. Several considerations in using an environmental energy source are fundamentally different from using batteries. Rather than a limit on the total energy, harvesting transducers impose a limit on the instantaneous power available. Further, environmental energy availability is often highly variable and a deterministic metric such as residual battery capacity is not available to characterize the energy source. The different nodes in a sensor network may also have different energy harvesting opportunities. Since the same end-user performance may be achieved using different workload allocations at multiple nodes, it is important to adapt the workload allocation to the spatio-temporal energy availability profile in order to enable energy-neutral operation of the network. This paper describes power management techniques for such energy harvesting sensor networks. Platform design considerations as well as power scaling techniques at the node-level and network-level are described.

This paper presents techniques for optimizing the energy efficiency of multi-resource, multi-tasking embedded systems. Low power design of individual system resources, such as embedded processors, has been extensively studied in the past. However, system-level techniques, such as those presented in this paper, which exploit the synergy between various system resources, achieve levels of energy efficiency that cannot be obtained by considering individual resources independently. We demonstrate that, in multi-resource embedded systems that concurrently execute multiple applications, there exists a tradeoff between resource management efficiency and resource allocation fairness. By solving the multi-resource energy optimization problem in the context of an embedded sensor system, we show that our techniques enable the system designer to traverse this efficiency-fairness tradeoff space.

As embedded systems are being networked, often wirelessly, an increasingly larger share of their total energy budget is due to the communication. This necessitates the development of power management techniques that address communication subsystems, such as radios, as opposed to computation subsystems, such as embedded processors, to which most of the research effort thus far has been devoted. In this paper, we present E/sup 2/WFQ, an energy efficient version of the weighted fair queuing (WFQ) algorithm for packet scheduling in communication systems. We employ a recently proposed radio power management technique, dynamic modulation scaling (DMS), as a control knob to enable energy-latency tradeoffs during wireless packet scheduling. The use of E/sup 2/WFQ results in an energy aware packet scheduler, which exploits the statistics of the input arrival pattern as well as the variability in packet lengths. Simulation results show that large savings in energy consumption can be obtained through the use of our scheduling scheme, compared to conventional WFQ, with only a small, bounded increase in worst case packet latency.