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

Du, Wan; Xing, Zikun; Li, Zhenjiang; He, Bingsheng; Chua, Leok Poh; Miao, Haiyan

doi:10.1109/ipsn.2014.6846750

Cited by 48 publications

(26 citation statements)

References 35 publications

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“…Deploying millions of these stations to measure finer grained environmental noise in every nook and cranny of cities is impossible [9]. Maybe low-cost wireless sensing units have been already proven feasible for noise and other environmental data, such as in [14,15], but the problem is the feasibility in technology does not mean that we can acquire the data from these sensors easily. For example: in Beijing, with more than 20 million of population, and developed advanced transport system, it is not very convenient to learn sound level of environmental noise from public channel.…”

Section: Introductionmentioning

confidence: 99%

Mapping Urban Environmental Noise Using Smartphones

Zuo

Xia

Liu

et al. 2016

Sensors

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Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be created using limited measurement data. To achieve the measurement with certain precision, a set of methods was designed to calibrate the smartphones. Measuring noise with mobile phones is different from the traditional static observations. The users may be moving at any time. Therefore, a method of attaching an additional microphone with a windscreen is proposed to reduce the wind effect. However, covering an entire area is impossible. Therefore, an interpolation method is needed to achieve full coverage of the area. To reduce the influence of spatial heterogeneity and improve the precision of noise mapping, a region-based noise-mapping method is proposed in this paper, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to create a noise map. To validate the effect of the method, a comparison of the interpolation results was made to analyse our method and the ordinary Kriging method. The result shows that our method is more accurate in reflecting the local distribution of noise and has better interpolation precision. We believe that the proposed noise-mapping method is a feasible and low-cost noise-mapping solution.

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Section: Introductionmentioning

confidence: 99%

Mapping Urban Environmental Noise Using Smartphones

Zuo

Xia

Liu

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…Some research has studied optimal sensor configurations in built environments, either in terms of pollutant dispersion to protect against nuclear, biological, and chemical attacks (NBC) [29] or with the aim to reconstruct a close approximation of the flow field [30]. Recent work by Du et al [31] proposed a methodology to identify optimal sensor locations for wind studies in an urban reservoir. In their study, an entropy-based sensor placement has been applied to wind predictions obtained from CFD simulations in order to identify optimum sensor locations.…”

mentioning

confidence: 99%

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Papadopoulou

Raphael

Smith

et al. 2014

Entropy

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Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind OPEN CCESSEntropy 2014, 16 5079 characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.

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“…For example, the sensor placement problem [58] and routing protocol design [59] are studied by Matlab. Sometimes, the collected traces from the real deployments [60,61] are imported into the simulation models to reproduce the transmission behaviors of links and paths in WSNs.…”

Section: Network Simulators With Node Modelsmentioning

confidence: 99%

Modeling and simulation of networked low-power embedded systems: a taxonomy

Mieyeville

Navarro

et al. 2014

J Wireless Com Network

Self Cite

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Simulation has been widely adopted for the evaluation of novel protocols or other designs for networked low-power embedded systems, especially for wireless sensor networks (WSNs). A large number of simulation tools have been developed for WSNs in the past few years. However, different tools may emphasize on different features. For example, general network simulators mainly focus on the high-level performance evaluation with certain assumptions, while some SystemC-based simulators have been developed recently to realize the hardware and software co-design of sensor node at the system level. Therefore, it is necessary to study the different modeling methodologies and to distinguish the various features of the existing WSN simulators. In this paper, we propose a taxonomy that categorizes the existing simulation tools into four groups, i.e., network simulators with node models, network simulators with node emulators, node system simulator with network models, and node emulators with network models. To demonstrate the rationality and usability of the proposed taxonomy, we use it to conduct a survey of the existing simulation tools. This study is intended to be comprehensive to cover all important simulation tools.

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

Cited by 48 publications

References 35 publications

Mapping Urban Environmental Noise Using Smartphones

Mapping Urban Environmental Noise Using Smartphones

Hierarchical Sensor Placement Using Joint Entropy and the Effect of Modeling Error

Modeling and simulation of networked low-power embedded systems: a taxonomy

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