Apollon Project: A Massive Online Open Lab for Citizen Science Driven Environmental Monitoring

Longo, Antonella; Bochicchio, Mario A.; Zappatore, Marco

doi:10.1109/educon45650.2020.9125151

Cited by 3 publications

(3 citation statements)

References 37 publications

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“…Employ methods such as targeted recruitment, communitybased outreach, and culturally sensitive approaches to enhance representativeness and inclusivity (Coulson et al, 2021). Conduct user engagement workshops and utilize ICT systems, which can make science more accessible and understandable for all participants (Longo et al, 2020;Poslad et al, 2022). Regular training for citizen scientists is key to ensuring data quality.…”

Section: Challenge 1: Balancing Scientific Rigor and Public Engagementmentioning

confidence: 99%

“…The 'outcomes' and 'fitness-for-purpose' elements in the framework help identify applications of community air monitoring projects in terms of 5 tiers. The tiers, based on IPCC's summary for policymakers in its Fifth Assessment Report (AR5), provide a useful categorization of community air monitoring experiments using low-cost sensors based on our review of literature (Intergovernmental Panel on Climate Change (IPCC), 2014 ) (Longo et al, 2020). We use thistiered approach to categorize sensor deployment experiments from our review according to their fitness-for-purpose derived from study findings.…”

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confidence: 99%

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Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

2023

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Air pollution, especially PM2.5 (aerosols < 2.5μm diameter), contributes to 8-10 million early deaths annually. The WHO's 2021 guidelines on PM2.5 exposure show 97.3% of people globally live beyond safe exposure levels, reducing global average life expectancy by 2.2 years. This dissertation investigates the efficacy of IoT-based low-cost sensors in delivering credible, localized pollution data and fostering public environmental advocacy. Three essays structure the exploration:Essay 1 reviews the evolution and performance of low-cost PM sensors (from 2012-2022) and highlights the efficacy of appropriate calibration models to improve performance.

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Section: Challenge 1: Balancing Scientific Rigor and Public Engagementmentioning

confidence: 99%

mentioning

confidence: 99%

Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

2023

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“…Indeed, thanks to the democratization of smartphones that embed increasingly rich sensing capabilities, we are able to sense a large portion of the physical environment and further relate the observed phenomena with human behavior. Various applications illustrate the benefit of mobile crowdsensing toward better informing and enhancing, e.g., environmental monitoring and awareness [2], public health monitoring and policy [3] or traffic management [4]. Still, mobile crowdsensing comes with tremendous challenges for it to be widely adopted and to effectively feed today's AI-powered systems.…”

Section: Introductionmentioning

confidence: 99%

Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

Brahem

Scerri

Anciaux

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)

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Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies "anonymizing" the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the -completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with − 1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure -completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

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Consent-driven Data Reuse in Multi-tasking Crowdsensing Systems: A Privacy-by-Design Solution

Brahem

Scerri

Anciaux

et al. 2022

Pervasive and Mobile Computing

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Apollon Project: A Massive Online Open Lab for Citizen Science Driven Environmental Monitoring

Cited by 3 publications

References 37 publications

Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

Evaluating Performance of Socio-Technical Systems for Improved Reliability: Evidence from Sensor and Human Networks

Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

Consent-driven Data Reuse in Multi-tasking Crowdsensing Systems: A Privacy-by-Design Solution

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