J
Julien Mineraud
Researcher at University of Helsinki
Publications - 27
Citations - 732
Julien Mineraud is an academic researcher from University of Helsinki. The author has contributed to research in topics: Routing protocol & The Internet. The author has an hindex of 10, co-authored 27 publications receiving 517 citations. Previous affiliations of Julien Mineraud include Waterford Institute of Technology.
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Journal ArticleDOI
A gap analysis of Internet-of-Things platforms
TL;DR: A gap analysis of the current IoT landscape aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems and concludes with a list of recommendations for extending these IoT platforms in order to fill in the gaps.
Journal ArticleDOI
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Francesco Concas,Julien Mineraud,Eemil Lagerspetz,Samu Varjonen,Xiaoli Liu,Kai Puolamäki,Petteri Nurmi,Sasu Tarkoma +7 more
TL;DR: In this article, the authors survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques and identify open research challenges and present directions for future research.
Journal ArticleDOI
Toward Massive Scale Air Quality Monitoring
Naser Hossein Motlagh,Tuukka Petäjä,Markku Kulmala,Sasu Trachoma,Eemil Lagerspetz,Petteri Nurmi,Xin Li,Samu Varjonen,Julien Mineraud,Matti Siekkinen,Andrew Rebeiro-Hargrave,Tareq Hussein +11 more
TL;DR: A research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution is presented.
Posted Content
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Francesco Concas,Julien Mineraud,Eemil Lagerspetz,Samu Varjonen,Xiaoli Liu,Kai Puolamäki,Petteri Nurmi,Sasu Tarkoma +7 more
TL;DR: The rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques is surveyed and open research challenges are identified and present directions for future research.
Proceedings ArticleDOI
MegaSense: Feasibility of Low-Cost Sensors for Pollution Hot-spot Detection
Eemil Lagerspetz,Sasu Tarkoma,Tareq Hussein,Naser Hossein Motlagh,Martha A. Zaidan,Pak Lun Fung,Julien Mineraud,Samu Varjonen,Matti Siekkinen,Petteri Nurmi,Yutaka Matsumi +10 more
TL;DR: A 44-day measurement campaign is conducted to assess performance of low-cost air quality monitors under different environmental conditions and shows that the accuracy is sufficient for applications relying on variations in air quality index values, such as hot spot detection.