Anomaly Detection in Urban Air Quality Sensor Networks

D. Lestari; M. Gunawan

doi:10.5555/fji-demo-006

Akses Terbuka DOI: 10.5555/fji-demo-006 Cara menggunakan DOI?

Anomaly Detection in Urban Air Quality Sensor Networks

D. Lestari^*¹, M. Gunawan²

^*Penulis korespondensi: D. Lestari

¹FJI Press
²Institute of Technology Nusantara

Artikel Review · Halaman 1–22 · In: Machine Learning for Environmental Monitoring · Terbit 2026-06-15 · 165 dilihat · 71 diunduh

Riwayat artikel: Diterima 2026-04-10 · Disetujui 2026-05-22 · Tersedia online 2026-06-15

Abstrak

This chapter surveys anomaly detection methods for low-cost urban air quality sensors, covering statistical, machine-learning and hybrid approaches. We compare methods on a public dataset of 120 sensors and give practical guidance on choosing a method under sensor drift and missing data.

Kata kunci: air quality; anomaly detection; sensor networks; survey

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D. Lestari, M. Gunawan (2026). Anomaly Detection in Urban Air Quality Sensor Networks. Machine Learning for Environmental Monitoring,, 1–22. https://doi.org/10.5555/fji-demo-006

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Artikel akses terbuka ini didistribusikan di bawah lisensi CC BY-NC 4.0. CC BY-NC 4.0

DOI: 10.5555/fji-demo-006
Tipe artikel: Artikel Review
Halaman: 1–22
Terbit: 2026-06-15
Metrik: 165 dilihat · 71 diunduh