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:10.1145/2700265

Cited by 37 publications

(17 citation statements)

References 59 publications

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“…Field estimation problem: For some applications, the cost of deploying sensors at all is very high, but there is still a need for fine-grained field measurements. Applications include monitoring of soil moisture [35] or air [24] or water [12] quality. Here it is assumed to be impossible to deploy sufficient sensors to cover the field at all times.…”

Section: Wsn As a Data Collection Toolmentioning

confidence: 99%

“…They compare the performance of a log-linear regression model and a deep learning framework that learns data dependencies [24]. Du et al generate optimal long-term sensor placements based on wind models and knowledge of annual monsoon seasons [12]. Our approach differs because deploying temporary, fixed sensors in building applications is not too expensive.…”

Section: Wsn As a Data Collection Toolmentioning

confidence: 99%

“…Although they save energy on the nodes, they do not reduce the number of sensors required. Though there are pre-deployment strategies for selecting a limited number of sensors [12,21,24,28,35], they require applications such as outdoor environmental monitoring where a statistical model of the measured phenomena and deployment environment are known a priori. In this work, we focus on a pre-deployment strategy for monitoring in buildings where the statistics of the field is not well known.…”

Section: Research Gap Analysismentioning

confidence: 99%

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BuildSense

Cardell-Oliver

Sarkar²

2019

ACM Trans. Sen. Netw.

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Buildings can achieve energy-efficiency by using solar passive design, energy-efficient structures and materials, or by optimizing their operational energy use. In each of these areas, efficiency can be improved if the physical properties of the building along with its dynamic behavior can be captured using low-cost embedded sensor devices. This opens up a new challenge of installing and maintaining the sensor devices for different types of buildings. In this article, we propose BuildSense, a sensing framework for fine-grained, long-term monitoring of buildings using a mix of physical and virtual sensors. It not only reduces the deployment and management cost of sensors but can also guarantee accurate and fault-tolerant data coverage for long-term use. We evaluate BuildSense using sensor measurements from two rammed-earth houses that were custom-designed for a challenging hot-arid climate so that almost no artificial heating or cooling is required. We demonstrate that BuildSense can significantly reduce the cost of permanent physical sensors whilst still achieving fit-for-purpose accuracy, fault-tolerance, and stability. Overall, we were able to reduce the cost of a building sensor network by 60% to 80% by replacing physical sensors with virtual ones while still maintaining accuracy of ≤1.0°C and fault-tolerance of 2 or more predictors per virtual sensor.

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Section: Wsn As a Data Collection Toolmentioning

confidence: 99%

Section: Wsn As a Data Collection Toolmentioning

confidence: 99%

Section: Research Gap Analysismentioning

confidence: 99%

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BuildSense

Cardell-Oliver

Sarkar²

2019

ACM Trans. Sen. Netw.

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“…Mortazavi et al [19] propose a node placement algorithm for two-tiered WSNs that maximises the area covered by a specified number of relay nodes and sensor nodes. Recently, Du et al [20] develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of sensors under the assumption of strong non-Gaussian yearly distribution.…”

Section: Quality-of-sensing In Wsnmentioning

confidence: 99%

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

Zeng

et al. 2016

IEEE Trans. Ind. Inf.

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In recent years, water pollution or contamination incidents happened frequently, causing serious disasters and negative social impact. To reduce the water contamination risk, water quality monitoring sensors should be deployed in water distribution system to enable real-time pollution detection. It is desirable to deploy sensors everywhere so that any contamination event can be detected and reported in a timely manner. Unfortunately, this is a luxury and unrealistic vision because of high deployment cost. It is significant to lower the deployment cost provided that the quality-of-sensing, e.g., coverage and contamination detection time, can be guaranteed for effective depollution action. In this paper, we consider a water quality monitoring sensor network consisting of two kinds of sensors with different prices. The expensive one is of cellular communication capability and therefore is able to send sensing information to control center directly, while, the cheaper one is of only sensor-to-sensor communication capability. We investigate a cost-efficient sensor deployment problem on how to deploy these two kinds of sensors in a given water distribution system to minimize the deployment cost, without violating the quality-of-sensing requirement. We first formulate the problem into a Mixed Integer Quadratically Constrained Programming (MIQCP) problem, which is then linearized into an equivalent Mixed Integer Linear Programming (MILP). We further propose a polynomial two-stage heuristic algorithm and evaluate its efficiency via extensive simulation-based studies.

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“…The recent advances in low-power wireless communications and computing technologies have enabled the largescale implementation of Internet of Things (IoT) systems [1], where massive sensors, micro-controllers and transceivers are embedded to the facilities of buildings, vehicles, wearable items and wild areas [2], [3], [4]. The IoT aims at making the Internet even more immersive and pervasive, providing interactive cyber-physical access and control services [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Deploying Edge Computing Nodes for Large-Scale IoT: A Diversity Aware Approach

Zhao

Min

Gao

et al. 2018

IEEE Internet Things J.

115

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The recent advances in microelectronics and communications have led to the development of large-scale IoT networks, where tremendous sensory data is generated and needs to be processed. To support real-time processing for large-scale IoT, deploying edge servers with storage and computational capability is a promising approach. In this paper, we carefully analyze the impacting factors and key challenges for edge node deployment. We then propose a novel three-phase deployment approach which considers both traffic diversity and the wireless diversity of IoT. The proposed work aims at providing real-time processing service for the IoT network and reducing the required number of edge nodes. We conducted extensive simulation experiments, the results show that compared to the existing works that overlooked the two kinds of diversities, the proposed work greatly reduces the number of edge nodes and improves the throughput between IoT and edge nodes.

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

Cited by 37 publications

References 59 publications

BuildSense

BuildSense

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

Deploying Edge Computing Nodes for Large-Scale IoT: A Diversity Aware Approach

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