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.
"Recently, several new approaches have been developed to predict the solar energy levels at a single point, most of which have focused on solar-powered sensing . Some of these techniques choose a fixed sensor sampling rate based on long-term expected sunlight levels   , while others make near-term predictions, e.g. 3-72 hours in advance  . "
[Show abstract][Hide abstract] 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.
"At operating system layer, Liu et al.  and Moser et al.  propose task scheduling techniques for energy harvesting systems. At the application layer, Noh et al.  use an adaptive technique to turn on and off storage services based on different energy thresholds. Ravinagarajan et al.  adapt task utility of structural health monitoring applications to maximize accuracy of tasks. "
[Show abstract][Hide abstract] ABSTRACT: Renewable energy technology has become a promising solution to reduce energy concerns due to limited battery in wireless sensor networks. While this enables us to prolong the lifetime of a sensor network (perpetually), unstable environmental energy sources bring challenges in the design of sustainable sensor networks. In this paper, we propose an adaptive energy harvesting management framework, QuARES, which exploits an application's tolerance to quality degradation to adjust application quality based on energy harvesting conditions. The proposed framework consists of two stages: an offline stage which uses prediction of harvested energy to allocate energy budget for time slots; and an online stage to tackle the fluctuation in time-varying energy harvesting profile. We implemented the application and our framework in a network simulator, QualNet. In comparison with other approaches (e.g.,), our system offers improved sustainability (low energy consumption, no node deaths) during operation with data quality improvement ranging from 30-70%. QuARES is currently being deployed in a campus-wide pervasive space at UCI called Responsphere.
Green Computing Conference and Workshops (IGCC), 2011 International; 08/2011
"We also assume that each node Vi has a pre-assigned perepoch energy budget Bi. Such energy budgets can be produced by algorithms such as   , based on the predictions of harvested energy for any of the epochs in the horizon. Further, related work such as   require that a sensor node can consume no more than the amount of energy harvested in any epoch. "
[Show abstract][Hide abstract] ABSTRACT: There is currently tremendous interest in deploying energy harvesting wireless sensor networks. Engineering such systems requires striking a careful balance between sensing performance and energy management. Our work addresses this problem through the design and analysis of a harvesting aware utility-based sensing rate allocation algorithm. Based on a network utility formulation, we show that our algorithm is optimal in terms of assigning rates to individual nodes to maximize overall utility, while ensuring energy-neutral operation. To our knowledge, our work is the first optimal solution that maximizes network utility through rate assignments for tree-structured energy harvesting sensor networks. Our algorithm is fast and efficient with running time O(N3), where N is the number of nodes. We evaluate the performance, scalability, and overhead of our algorithm for various utility functions and network sizes, underlining its significant advantages.
Proceedings of the 14th International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2011, Miami, Florida, USA, October 31 - November 4, 2011; 01/2011
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