International audienc

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.

Energy-aware scheduling of real time applications over multiprocessor systems is considered in this paper. Early research reports that while various energysaving policies, for instance Dynamic Power Management (DPM) and Dynamic Voltage & Frequency scaling (DVFS) policies, perform well individually for a specific set of operating conditions, they often outperform each other under different workload and/or architecture configuration. Thus, no single policy fits perfectly all operating conditions. Instead of designing new policies for specific operating conditions, this paper proposes a generic power/energy management scheme that takes a set of wellknown existing (DPM and DVFS) policies, each of which performs well for a set of conditions, and adapts at runtime to the best-performing policy for any given workload. Experiments are performed using state-of the-art DPM and DVFS policies and the results show that our proposed scheme adapts well to the changing workload and always achieves overall energy savings comparable to that of best-performing policy at any point in time.

This paper presents a Genetic Algorithm (GA) based approach for Hardware/Software partitioning targeting an architecture composed of a processor and a dynamically reconfigurable datapath (FPGA). From an acyclic task graph and a set of Area-Time implementation trade off points for each task, our GA performs HW/SW partitioning and scheduling such that the global application execution time is minimized. The efficiency of our GA is established through its application to an AC-3 decoder function and its performance is compared with a greedy algorithm.

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In this paper 1 , we have addressed energy-efficient scheduling of real time applications intended to be executed on multiprocessor systems. Our proposed technique, called Deterministic Stretch-to-Fit (DSF) technique, is based on inter-task real time dynamic voltage and frequency scaling (RT-DVFS). It mainly comprises of three components. Firstly, we propose an online algorithm to reclaim energy by adapting to the variations in actual workload of target application tasks. Secondly, we extend our online algorithm with an adaptive and speculative speed adjustment mechanism. This mechanism anticipates early completion of future task instances based on the information of their average workload. Thirdly, we propose a one-task extension technique for multi-task multiprocessor systems. No real time constraints of target application are violated while applying our proposed technique. Simulation results show that our online slack reclamation algorithm alone gives up to 53% gains on energy consumption and our extended speculative speed adjustment mechanism, along with the one-task extension technique, gives additional gains, reaching a theoretical low-bound on the scalable frequency and voltage.

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