Achieving Long-Term Surveillance in VigilNet

Tian, He; Vicaire, Pascal; Yan, Tao; Cao, Qing; Zhou, Gang; Gu, Lin; Luo, Lijuan; Stoleru, Radu; Stankovic, John A.; Abdelzaher, Tarek

doi:10.21236/ada446898

Cited by 24 publications

(20 citation statements)

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“…Some diagnostic approaches have also been designed for these applications [Liu et al 2008;Dereszynski and Dietterich 2011]. Many large-scale systems with hundreds of nodes [Dutta et al 2006;He et al 2006;Liang et al 2009; have been developed as well, such as military surveillance, temperature measurement in data centers, and forest monitoring. In this article, we focus on the optimal sensor placement problem for wind distribution measurements in large areas.…”

Section: Related Workmentioning

confidence: 99%

Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

Xing

et al. 2015

ACM Trans. Sen. Netw.

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We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches. ACM Reference Format:Wan Du, Zikun Xing, Mo Li, Bingsheng He, Lloyd Hock Chye Chua, and Haiyan Miao. 2015. Sensor placement and measurement of wind for water quality studies in urban reservoirs.

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Section: Related Workmentioning

confidence: 99%

Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

Xing

et al. 2015

ACM Trans. Sen. Netw.

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“…For example, the GreenOrbs program code consumes approximately 45 Kbyte, approaching the limit of TelosB's total size of program flash. In other real-world systems, for example, VigilNet [16], the program code is also quite large. A large transferred code size would result in a long dissemination time, high transmission overhead, and high energy consumption.…”

Section: Why Incremental Reprogramming?mentioning

confidence: 99%

R2: Incremental Reprogramming Using Relocatable Code in Networked Embedded Systems

Dong

Chen

et al. 2013

IEEE Trans. Comput.

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“…The energy consumption in sleep state is much smaller than in active and listen state, so taking nodes to sleep is an efficient method to save energy. Most of researches focus on taking nodes to sleep while maintaining full coverage, such as [3,4,5]. Some of them focus on solving the problem of set kcoverage [9,10], which divide the network area into several subsets, and each of the subsets can cover the whole monitoring area.…”

Section: Related Workmentioning

confidence: 99%

Duty Cycling in Target Monitoring Wireless Sensor Network Systems

Guo

Hong

2013

International Journal on Smart Sensing and Intelligent Systems

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Abstract-Duty cycling is an important method for energy constrained sensor network systems to prolong lifetime. Current research on duty cycling is mainly based on such assumption that all the sensory coverage should be maintained while some nodes are sleeping. However, for the applications of target monitoring, it is not necessary anymore to keep the whole sensory coverage of the sensor network. It only needs to make sure that such kinds of nodes are active which can perceive the activities of the monitored targets. This paper proposes a novel duty cycling design for target monitoring sensor networks, which includes two algorithms -sleep scheduling algorithm and gradient query algorithm based on sleep periods. In the proposed design, most of sensor nodes are sleeping, while still keep the functions of target monitoring and information query in the sensor networks. The performance of our INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS VOL. 6, NO. 3, JUNE 2013 1054 design has been evaluated through both theoretical analysis and simulations, which prove the functionality of the proposed design on the reduction of energy consumption.

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Achieving Long-Term Surveillance in VigilNet

Cited by 24 publications

References 0 publications

Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

Sensor Placement and Measurement of Wind for Water Quality Studies in Urban Reservoirs

R2: Incremental Reprogramming Using Relocatable Code in Networked Embedded Systems

Duty Cycling in Target Monitoring Wireless Sensor Network Systems

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