In-Situ Soil Moisture Sensing: Efficient Random Sensor Placement and Field Estimation Using Compressive Sensing

Wu, Xiaopei; Wu, Yue; Liu, Mingyan; Zheng, Lihua

doi:10.1109/wicom.2011.6040274

Cited by 16 publications

(28 citation statements)

References 13 publications

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“…Spatial prediction is based on sensor placement and they share the same system model. Since the advantages of Gaussian-based sensor placement over random deployment have completely proved in previous works [5,6] and it is costly in terms of budget and time (more than 3 months) to reinstall the deployed wind sensors, we focus on evaluating the potential improvement of spatial prediction accuracy by comparing our approach (MIX) with UniGau and Interpolation based on the real wind measurements on the optimal sensor placement. The prediction error is measured by the average Root-Mean-Squared Error (RMSE) between the estimated values of unobserved locationsX V\A and their actual values X V\A .…”

Section: B Experiments Setupmentioning

confidence: 99%

“…Among them, GP based approaches [8][9][10] have been used in many applications monitoring spatial phenomena like temperature [5] and soil moisture [6]. However, they cannot be directly applied to wind distribution measurement due to the temporal and spatial variations of wind.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of optimal sensor placement has been studied in many applications that monitor spatial phenomenon, like temperature sensing [5] and field soil moisture estimation [6]. Techniques like spatial statistics [7] and subset selection [8] have been proposed in previous works.…”

Section: Introductionmentioning

confidence: 99%

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Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Xing

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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Abstract-We collaborate with environmental scientists to study the hydrodynamics and 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 complex urban landform created by surrounding buildings. In this work, we study an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir with a limited number of wind sensors. Unlike existing sensor placement solutions that assume Gaussian process of target phenomena, this study measures the wind which inherently exhibits strong nonGaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons which follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind in the presence of surrounding buildings. 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 surface in real time. 10 wind sensors are finally deployed around or on the water surface of an urban reservoir. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction approach provides accurate wind measurement which outperforms the state-of-the-art Gaussian model based or interpolation based approaches.

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Section: B Experiments Setupmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Xing

et al. 2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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“…The collision problem becomes even more serious in the context of wireless sensor networks (WSNs). Being energy constrained, most WSNs employ low duty cycles [1]- [3]. For the purpose of saving energy, sensor nodes try to keep their radio off as much as possible, which squeezes the available time for packet transmissions among the nodes and potentially leads to collisions.…”

Section: Introductionmentioning

confidence: 99%

Walking down the STAIRS: Efficient collision resolution for wireless sensor networks

Wang

et al. 2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-Collision resolution is a crucial issue in wireless sensor networks. The existing approaches of collision resolution have drawbacks with respect to energy efficiency and processing latency. In this paper, we propose ST AIRS, a time and energy efficient collision resolution mechanism for wireless sensor networks. STAIRS incorporates the constructive interference technique in its design and explicitly forms superimposed colliding signals. Through extensive observations and theoretical analysis, we show that the RSSI of the superimposed signals exhibit stairslike phenomenon with different number of contenders. That principle offers an attractive feature to efficiently distinguish multiple contenders and in turn makes collision-free schedules for channel access. In the design and implementation of STAIRS, we address practical challenges such as contenders alignment, online detection of RSSI change points, and fast channel assignment. The experiments on real testbed show that STARIS realizes fast and effective collision resolution, which significantly improves the network performance in terms of both latency and throughput.

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“…Recent advances in Wireless Sensor Networks (WSNs) have fostered a large collection of applications [1] [2]. In those networks, a collection of battery powered sensor nodes are self-organized to form a network, interact with the physical world and perform certain tasks, e.g., data collection.…”

Section: Introductionmentioning

confidence: 99%

Sleep in the Dins: Insomnia therapy for duty-cycled sensor networks

Wang

Cao

Mao

2014

IEEE INFOCOM 2014 - IEEE Conference on Computer Communications

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Abstract-Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2%∼40.1% compared with state-of-the-art protocols. Results based on data from a 1200-node operational network further show the effectiveness and scalability of the design.

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In-Situ Soil Moisture Sensing: Efficient Random Sensor Placement and Field Estimation Using Compressive Sensing

Cited by 16 publications

References 13 publications

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Optimal sensor placement and measurement of wind for water quality studies in urban reservoirs

Walking down the STAIRS: Efficient collision resolution for wireless sensor networks

Sleep in the Dins: Insomnia therapy for duty-cycled sensor networks

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