Robust and Low Complexity Rate Control for Solar Powered Sensors

Moser,; Thiele,; Brunelli, Davide; Benini, Luca

doi:10.1109/date.2008.4484691

Cited by 24 publications

(20 citation statements)

References 6 publications

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“…Under these conditions, considering the constraints on system components, the performance requirements can be met by constantly adapting the application's energy consumption to match energy harvested from the environment. To this end, approaches for harvested-energy management have been proposed that adapt the system to uncertainty in energy availability by predicting the expected incoming energy [2,3,4,5]. In case of solar energy, this is possible by capitalizing on its 24-hour cycles to predict energy availability.…”

Section: Introductionmentioning

confidence: 99%

“…The predictor was based on the observation that energy generation during a given time slot of day was similar to that generated at the same instant on previous days and it can be estimated using an exponentially weighted moving average of historical data. Moser et al [3] used similar prediction in their proposed adaptive power management framework. Recently, Recas et al [5] proposed an improved solar energy predictor by using past and current day's power measurements in the prediction algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…The effectiveness of harvested-energy management [2,3,4,5] is sensitive to accuracy of prediction algorithm. This has been acknowledged in previous works, nevertheless, there is a lack of clear justification how the prediction accuracy should be best quantified.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and design exploration of solar harvested-energy prediction algorithm

Ali

Al-Hashimi

Recas

et al. 2010

2010 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE 2010)

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Abstract-To respond to variations in solar energy, harvestedenergy prediction is essential to harvested-energy management approaches. The effectiveness of such approaches is dependent on both the achievable accuracy and computation overhead of prediction algorithm implementation. This paper presents detailed evaluation of a recently reported solar energy prediction algorithm to determine empirical bounds on achievable accuracy and implementation overhead using an effective error evaluation technique. We evaluate the algorithm performance over varying prediction horizons and propose guidelines for algorithm parameter selection across different real solar energy profiles to simplify implementation. The prediction algorithm computation overhead is measured on actual hardware to demonstrate prediction accuracy-cost trade-off. Finally, we motivate the basis for dynamic prediction algorithm and show that more than 10% increase in prediction accuracy can be achieved compared to static algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation and design exploration of solar harvested-energy prediction algorithm

Ali

Al-Hashimi

Recas

et al. 2010

2010 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE 2010)

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“…[Moser et al 2008] perform offline linear programming techniques to predict future energy in addition to control methods; the paper describes a simulation of this with no field instantiation. [Lu and Whitehouse 2012] compute offline predictions of sunlight for daylight harvesting.…”

Section: Energy Predictionmentioning

confidence: 99%

In-Network Distributed Solar Current Prediction

Basha

Jurdak

Rus

2014

ACM Trans. Sen. Netw.

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Long-term sensor network deployments demand careful power management. While managing power requires understanding the amount of energy harvestable from the local environment, current solar prediction methods rely only on recent local history, which makes them susceptible to high variability. In this paper, we present a model and algorithms for distributed solar current prediction, based on multiple linear regression to predict future solar current based on local, in-situ climatic and solar measurements. These algorithms leverage spatial information from neighbors and adapt to the changing local conditions not captured by global climatic information. We implement these algorithms on our Fleck platform and run a 7-weeklong experiment validating our work. In analyzing our results from this experiment, we determined that computing our model requires an increased energy expenditure of 4.5mJ over simpler models (on the order of 10 −7 % of the harvested energy) to gain a prediction improvement of 39.7%.

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“…To enhance vision sensor networks, two successful strategies can be adopted: 1) exploiting alternative power sources, which increase the autonomy of the nodes; 2) exploring multi-modal sensor integration, which can save on-board power consumption. Recently, several researchers have proposed alternative power sources and energy harvesting techniques to replenish energy buffers like batteries or super capacitors by extracting and converting power from the surrounding environments [18]- [20]. Energy harvesting technologies are used to collect energy from ambient sources.…”

mentioning

confidence: 99%

Multimodal Video Analysis on Self-Powered Resource-Limited Wireless Smart Camera

Magno

Tombari

Brunelli

et al. 2013

IEEE J. Emerg. Sel. Topics Circuits Syst.

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Abstract-Surveillance is one of the most promising applications for wireless sensor networks, stimulated by a confluence of simultaneous advances in key disciplines: computer vision, image sensors, embedded computing, energy harvesting, and sensor networks. However, computer vision typically requires notable amounts of computing performance, a considerable memory footprint and high power consumption. Thus, wireless smart cameras pose a challenge to current hardware capabilities in terms of low-power consumption and high imaging performance. For this reason, wireless surveillance systems still require considerable amount of research in different areas such as mote architectures, video processing algorithms, power management, energy harvesting and distributed engine. In this paper, we introduce a multimodal wireless smart camera equipped with a pyroelectric infrared sensor and solar energy harvester. The aim of this work is to achieve the following goals: 1) combining local processing, low power hardware design, power management and energy harvesting to develop a low-power, low-cost, power-aware, and self-sustainable wireless video sensor node for video processing on board; 2) develop an energy efficient smart camera with high accuracy abandoned/removed object detection capability. The efficiency of our approach is demonstrated by experimental results in terms of power consumption and video processing accuracy as well as in terms of self-sustainability. Finally, simulation results show how perpetual work can be achieved in an outdoor scenario within a typical video surveillance application dealing with abandoned/removed object detection.Index Terms-Embedded smart camera, energy efficient, infrared sensor, multimodal video surveillance system, wireless sensor network.

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Robust and Low Complexity Rate Control for Solar Powered Sensors

Cited by 24 publications

References 6 publications

Evaluation and design exploration of solar harvested-energy prediction algorithm

Evaluation and design exploration of solar harvested-energy prediction algorithm

In-Network Distributed Solar Current Prediction

Multimodal Video Analysis on Self-Powered Resource-Limited Wireless Smart Camera

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