On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

Boubrima, Ahmed; Bechkit, Walid; Rivano, Hervé

doi:10.1109/tnet.2019.2923737

Cited by 33 publications

(10 citation statements)

References 32 publications

(51 reference statements)

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“…In [70][71][72][73], different multilayer architectures are presented that enable a distributed monitoring of the air environmental parameters using only a limited number of deployed sensors while still preserving the low-cost and flexible characteristics of a WSN. The optimal deployment of WSN nodes for finer spatio-temporal air monitoring is addressed in [74][75][76]. The optimization problem is formulated by explicitly taking into account the dynamic diffusion of the air pollutants, represented by means of atmospheric dispersion models, including also some realistic connectivity issues among the nodes in the WSN.…”

Section: Wsn For Air Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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Fighting Earth’s degradation and safeguarding the environment are subjects of topical interest and sources of hot debate in today’s society. According to the United Nations, there is a compelling need to take immediate actions worldwide and to implement large-scale monitoring policies aimed at counteracting the unprecedented levels of air, land, and water pollution. This requires going beyond the legacy technologies currently employed by government authorities and adopting more advanced systems that guarantee a continuous and pervasive monitoring of the environment in all its different aspects. In this paper, we take the research on integrated and large-scale environmental monitoring a step further by providing a comprehensive review that covers transversally all the main applications of wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs), and crowdsensing monitoring technologies. By outlining the available solutions and current limitations, we identify in the cooperation among terrestrial (WSN/crowdsensing) and aerial (UAVs) sensing, coupled with the adoption of advanced signal processing techniques, the major pillars at the basis of future integrated (air, land, and water) and large-scale environmental monitoring systems. This review not only consolidates the progresses achieved in the field of environmental monitoring, but also sheds new lights on potential future research directions and synergies among different research areas.

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Section: Wsn For Air Monitoringmentioning

confidence: 99%

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Fascista

2022

Sensors

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“…This number can evolve over time depending on successive deployments, hardware failures, or depleted batteries. This approach may target monitoring applications of a physical quantity varying over time, such as pollution [3], where the application needs K measurements over a given area periodically, every ∆T , whatever the number of nodes in the network.…”

Section: Assumptionsmentioning

confidence: 99%

A New Distributed and Probabilistic Approach for Traffic Control in LPWANs

Lasri

Maissa

Echabbi

et al. 2021

Advanced Information Networking and Applications

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Low-Power Wide Area Networks (LPWANs) are wireless networks with very low power consumption and wide area coverage. They are capable of supporting the traffic of nearly a thousand nodes with a duty cycle of less than 1%. However, the gradual densification of nodes increases the number of collisions and makes it more difficult to manage the upstream traffic. To mitigate this problem, we propose a new distributed and probabilistic traffic control algorithm, DiPTC, which allows nodes to adapt their traffic according to the needs of the application (e.g., receiving K measurements over a time period) while being agnostic to the number of nodes and to the network topology. A control message is broadcast by the gateway to all nodes each period when the objective is not reached, so that nodes can re-adapt their traffic. We evaluate the proposed solution in simulation and we compare it with the LoRaWAN protocol. The results show that our algorithm is able to reach the objective while keeping a low number of collisions, with a longer network lifetime. Compared to LoRaWAN, our solution shows a three times increase in the success rate and a decrease by a factor of 10 in the collision rate. 1

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“…Based on the variances of simulation errors and correlation coefficients w pq , we generate the variancecovariance matrix. In this work, we consider the correlation coefficient as a function of the distance [9] given as: w pq = e −δḋ pq , were δ is the attenuation coefficient of the correlation function and d pq is the euclidean distance between points p and q. Assuming that the simulation error follows a multivariate normal distribution, we generate a large number of ground truth data sets based on the simulated values and the computed variance-covariance matrix.…”

Section: Ground Truth and Measurements Generationmentioning

confidence: 99%

“…In addition, the conversion from particle counts to PM mass is based on theoretical models [7]. Furthermore, the high density of these sensors pushes us to reconsider the deployment approaches [8,9] as well as the collected data analysis for air quality mapping.…”

Section: Introductionmentioning

confidence: 99%

On the Regression and Assimilation for Air Quality Mapping Using Dense Low-Cost WSN

Fekih

Mokhtari

Bechkit

et al. 2020

Advances in Intelligent Systems and Computing

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The use of low-cost Wireless Sensor Networks (WSNs) for air quality monitoring has recently attracted a great deal of interest. Indeed, the cost-effectiveness of emerging sensors and their small size allow for dense deployments and hence improve the spatial granularity. However, these sensors offer a low accuracy and their measurement errors may be significant due to the underlying sensing technologies. The main aim of this work is to reconsider and compare some regression approaches to assimilation ones while taking into account the intrinsic characteristics of dense deployment of low cost WSN for air quality monitoring (high density, numerical model errors and sensing errors). For that, we propose a general framework that allows the comparison of different strategies based on numerical simulations and an adequate estimation of the simulation error covariances as well as the sensing errors covariances. While considering the case of Lyon city and a widely used numerical model, we characterize the simulation errors, conduct extensive simulations and compare several regression and assimilation approaches. The results show that from a given sensing error threshold, regression methods present an optimal sensor density from which the mapping quality decreases. Results also show that the Random Forest method is often the best regression approach but still less efficient than the BLUE assimilation approach when using adequate correction parameters.

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On the Deployment of Wireless Sensor Networks for Air Quality Mapping: Optimization Models and Algorithms

Cited by 33 publications

References 32 publications

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

A New Distributed and Probabilistic Approach for Traffic Control in LPWANs

On the Regression and Assimilation for Air Quality Mapping Using Dense Low-Cost WSN

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