AdaptSens: An Adaptive Data Collection and Storage Service for Solar-Powered Sensor Networks

Wang, Lili; Yang, Yong; Noh, Dong Kun; Le, Hieu; Liu, Jie; Abdelzaher, Tarek; Ward, Michael R.

doi:10.1109/rtss.2009.8

Cited by 35 publications

(21 citation statements)

References 13 publications

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“…Thus, a system with elastic workload demands achieves energy-neutral operation by changing the intensity of its workload, and hence its energy usage, at fine time-scales to match the energy it harvests. Prior work on energy harvesting primarily focuses on systems with energy-proportional components that have elastic workload demands [5], [8], [9], [11], [12], [20], [21], [22]. Maintaining energy-neutral operation in a system with inelastic workload demands using components that are not energy-proportional poses new challenges, since the system is unable to precisely change the intensity of its workload and energy usage to match the energy it harvests.…”

Section: Introductionmentioning

confidence: 99%

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Sharma

Gummeson

Irwin

et al. 2010

2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)

141

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Abstract-To sustain perpetual operation, 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 and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions.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 evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts for predictions in both solar-and wind-powered sensor systems increases each system's ability to satisfy its demands compared with existing strategies.

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Section: Introductionmentioning

confidence: 99%

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Sharma

Gummeson

Irwin

et al. 2010

2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON)

141

View full text Add to dashboard Cite

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“…The topology we use for the experiment is described in Figure 5. We used the solar-energy data obtained by our outdoor testbed [17] in Urbana, Illinois, during 10 days from OCT. 24th to Nov. 4th, 2011, as shown in Figure 6. The average energy harvested by one node during this period was 30.7Ah at 12V.…”

Section: Figure 5 Wsn Topology Deployed For the Performance Testmentioning

confidence: 99%

Adaptive Video-Data Quality Control for Solar-Energy-Harvesting Wireless Sensor Networks

Yi¹,

Noh

2014

IJMUE

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“…Noh et al showed how to use solar energy to maximize the amount of throughput by adaptively controlling the reliability [15]. In addition, they designed simple solar energy allocation (SSEA) and accurate solar energy allocation (ASEA) algorithms to optimally utilize the periodically harvested solar energy, while minimizing the variability of energy allocation [16].…”

Section: Related Workmentioning

confidence: 99%

Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity

Zhang

Chen

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms.Index Terms-Rechargeable sensor networks, limited battery capacity, energy allocation, sampling rate control.

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AdaptSens: An Adaptive Data Collection and Storage Service for Solar-Powered Sensor Networks

Cited by 35 publications

References 13 publications

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Adaptive Video-Data Quality Control for Solar-Energy-Harvesting Wireless Sensor Networks

Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity

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