Minimum Variance Energy Allocation for a Solar-Powered Sensor System.
ABSTRACT Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply. From an application’s
perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior
frequently. Reconciling the varying supply with the fixed demand requires good tools for predicting supply such that its average
is computed and demand is fixed accordingly. In this paper, we describe a probabilistic observation-based model for harvested
solar energy, which accounts for both long-term tendencies and temporary environmental conditions. Based on this model, we
develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing
the variance in energy allocation. Our algorithm is tested on both outdoor and indoor testbeds, demonstrating the efficacy
of the approach.
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ABSTRACT: Daylight harvesting is the use of natural sunlight to reduce the need for artificial lighting in buildings. The key challenge of daylight harvesting is to provide stable indoor lighting levels even though natural sunlight is not a stable light source. In this paper, we present a new technique called SunCast that improves lighting stability by predicting changes in future sunlight levels. The system has two parts: 1) it learns predictable sunlight patterns due to trees, nearby buildings, or other environmental factors, and 2) it controls the window transparency based on a quadratic optimization over predicted sunlight levels. To evaluate the system, we record daylight levels at 39 different windows for up to 12 weeks at a time, and apply our control algorithm on the data traces. Our results indicate that SunCast can reduce glare by 59% over a baseline approach with only a marginal increase in artificial lighting energy.01/2012;
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ABSTRACT: In this paper, we propose a harvesting-aware power management algorithm that targets at achieving good energy effi-ciency and system performance in energy harvesting real-time sys-tems. The proposed algorithm utilizes static and adaptive sched-uling techniques combined with dynamic voltage and frequency selection to achieve good system performance under timing and en-ergy constraints. In our approach, we simplify the scheduling and optimization problem by separating constraints in timing and en-ergy domains. The proposed algorithm achieves improved system performance by exploiting task slack with dynamic voltage and fre-quency selection and minimizing the waste on harvested energy. Experimental results show that the proposed algorithm improves the system performance in deadline miss rate and the minimum storage capacity requirement for zero deadline miss rate. Com-paring to the existing algorithms, the proposed algorithm achieves better performance in terms of the deadline miss rate and the min-imum storage capacity under various settings of workloads and harvested energy profiles. Index Terms—Dynamic voltage and frequency selection (DVFS), embedded system, energy harvest, power management, real-time, task scheduling.IEEE Transactions on Very Large Scale Integration (VLSI) Systems 01/2012; 1. · 1.22 Impact Factor
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ABSTRACT: Recently, solar energy emerged as a feasible supplement to battery power for wireless sensor networks (WSNs) which are expected to operate for long periods. Since solar energy can be harvested periodically and permanently, solar-powered WSNs can use the energy more efficiently for various network-wide performances than traditional battery-based WSNs of which aim is mostly to minimize the energy consumption for extending the network lifetime. However, using solar power in WSNs requires a different energy management from battery-based WSNs since solar power is a highly varying energy supply. Therefore, firstly we describe a time-slot-based energy allocation scheme to use the solar energy optimally, based on expectation model for harvested solar energy. Then, we propose a flow-control algorithm to maximize the amount of data collected by the network, which cooperates with our energy allocation scheme. Our algorithms run on each node in a distributed manner using only local information of its neighbors, which is a suitable approach for scalable WSNs. We implement indoor and outdoor testbeds of solar-powered WSN and demonstrate the efficiency of our approaches on them. Copyright © 2010 John Wiley & Sons, Ltd.Wireless Communications and Mobile Computing 04/2010; 12(5):379 - 392. · 0.86 Impact Factor