Conference Paper

Minimum Variance Energy Allocation for a Solar-Powered Sensor System.

DOI: 10.1007/978-3-642-02085-8_4 Conference: Distributed Computing in Sensor Systems, 5th IEEE International Conference, DCOSS 2009, Marina del Rey, CA, USA, June 8-10, 2009. Proceedings
Source: DBLP

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.

1 Bookmark
  • [Show abstract] [Hide abstract]
    ABSTRACT: Energy management in Wireless Sensor Networks (WSNs) is of paramount importance for the remotely deployed energy stringent sensor nodes. These nodes are typically powered by attached batteries. Several battery-driven energy conservation schemes are proposed to ensure energy efficient network operation. The constraints associated to the limited battery capacity shifted the research trend towards finding alternate sources by harvesting ambient energy. This survey presents a high level taxonomy of energy management in WSNs. We analyze different battery-driven energy consumption based schemes and energy harvesting based energy provisioning schemes. We also highlight the recent breakthrough of wireless energy transference to a sensor node as an alternative to typical batteries. We recommend taking into account recent energy provisioning advancements in parallel with the traditional energy conservation approaches for a sensor network while designing energy efficient schemes.
    Computers & Electrical Engineering 07/2014; · 0.99 Impact Factor
  • Source
    [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.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic, since it cannot precisely scale its usage to match its supply. Instead, the system must choose how to satisfy its demands based on its current energy reserves and predictions of its future energy supply. In this paper, we show that prediction strategies that use weather forecasts are more accurate than prediction strategies based on the past, and are capable of improving the performance of a variety of systems. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a solar or wind energy harvesting prediction, and quantify its accuracy. We then compare the performance of three types of energy harvesting systems—a lexicographically fair sensor network, an off-the-grid sensor testbed, and a solar-powered smart home—using prediction models based on both forecasts and the past. In each case, forecast-based predictions significantly improve system performance.
    Sustainable Computing: Informatics and Systems. 09/2014;