$ALC^{2}$ : When Active Learning Meets Compressive Crowdsensing for Urban Air Pollution Monitoring

Liu, Tong; Zhu, Yanmin; Yang, Yuanyuan; Ye, Fan

doi:10.1109/jiot.2019.2939552

Cited by 50 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By exploiting analytical models for the variations of the air pollutants concentrations, a cost-effective balance between performance in terms of joint sensing accuracy and communication costs using a vehicular sensor network can also be achieved [218]. In the presence of a sparse number of crowdsensing nodes, compressed sensing techniques can be employed as viable tools to reconstruct accurate air pollution maps using only a small selected set of samples [219,220].…”

Section: Crowdsensing 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

View full text Add to dashboard Cite

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.

show abstract

Section: Crowdsensing 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

View full text Add to dashboard Cite

show abstract

“…Focusing on pedestrians' well-being, human perception involves different spheres, and thus scientific effort includes data collection related to (i) urban air quality, (ii) noise pollution, (iii) and outdoor thermal comfort. An overview of the most recent studies [114,115,116,117,118,119,120,121,122,123,124,125,126,127] involving these types of data collection at the urban scale is presented distinguishing among different involved monitoring systems as summarized in Table 2.…”

Section: Crowdsourced Environmental Datamentioning

confidence: 99%

“…Sensor networks -Real time noise/pollution measures for population alert [115] -Fine-grained city air quality map through automobile built-in sensors [119] -Visualize air pollution propagation [118] -IoT platform for public consultancy of air quality [116] -Prototype of IoT-based technology for noise and air quality pollution real-time monitoring [117] Sensor & Social media -Monitoring and mitigation of urban noise pollution [126] Environmental sensor & Survey -Investigation of dynamic thermal comfort [128] -Extreme learning machine approach to predict thermal comfort in outdoors [129] Wearables -Map PM2.5 distribution through miniaturized, personal devices [121] -Map transient outdoor comfort [123] -Understanding dynamic thermal comfort [124] -Environmental mapping according to pedestrian perspective [130] -Enhancement of crowd-sourcing air quality through low costs participating [120] Wearables & Social media -Evaluation and representation of sound environment [130] -Soundscapes related to people perception [127] -Sound classification and mapping [131] Wearables & physiological data -Physiological response to different microclimates [132]…”

Section: Types Of Data Purposementioning

confidence: 99%

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

Salim

Dong

Ouf

et al. 2020

Building and Environment

View full text Add to dashboard Cite

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.

show abstract

“…With a swarm of cellphones doing a variety of vital tasks, hosts prioritises good data collecting. This endeavour falls under the category of "community operations of perceiving prejudice" (1) and can be either proactive or reactive. similar to how Google Glass works, which shows information to the user like a smartphone but frees up the user's hands to perform other tasks.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Improved Mobile Crowdsensing with Integration of Emerging Technologies

Pandey¹

2021

MSEA

View full text Add to dashboard Cite

Mobile sight is rapidly expanding and changing as mobile technology and associated disciplines of technology continue to progress. This seminar, titled "Mobile crowd-seeing (hosts)," seeks to investigate and offer answers for the effect, inflow, and processing of data via commercial mobile devices used by enormous numbers of people. This opens the door for research into novel monitoring techniques in a variety of industries, including social networks, daily living, healthcare, and smart transportation. The hardware and software of mobile devices are used by hosts, including but not limited to GPS and other related technologies, smart wearables, and smartphones with the Android and iOS operating systems. These gather data from their surroundings, effectively storing it, and either transferring it to a database to be processed there or doing the same actions on the raw data directly. This topic has a wide range of applications for both individuals and society as a whole. This article lists the essential structural elements that sustain the host's skeleton. However, significant technological advancements lessen the chance of problems. The accessibility of hosts makes it much easier for nefarious groups and individuals to gain access to, breach, and seize control of the data stores of the different components (including those of humans). The most current problems with the hosts' findings are investigated, and the advantages and disadvantages of many viewpoints are listed. Once some time has elapsed, the issues with unrestricted exploration and the upcoming challenges for hosts become increasingly important.

show abstract

$ALC^{2}$ : When Active Learning Meets Compressive Crowdsensing for Urban Air Pollution Monitoring

Cited by 50 publications

References 21 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

Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey

A Survey on Improved Mobile Crowdsensing with Integration of Emerging Technologies

Contact Info

Product

Resources

About