{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T11:54:41Z","timestamp":1777377281974,"version":"3.51.4"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100032397","name":"Atlantic Technological University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100032397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100813","type":"journal-article","created":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T17:06:56Z","timestamp":1776013616000},"page":"100813","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Accelerating graph-based deep learning for self-calibration in large-scale uncontrolled wireless sensor networks for environmental monitoring"],"prefix":"10.1016","volume":"30","author":[{"given":"Asif","family":"Yar","sequence":"first","affiliation":[]},{"given":"Shagufta","family":"Henna","sequence":"additional","affiliation":[]},{"given":"Marion","family":"McAfee","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"23","key":"10.1016\/j.array.2026.100813_b1","doi-asserted-by":"crossref","first-page":"7767","DOI":"10.1109\/JSEN.2017.2722819","article-title":"Environmental monitoring for smart cities","volume":"17","author":"Bacco","year":"2017","journal-title":"IEEE Sens J"},{"issue":"2","key":"10.1016\/j.array.2026.100813_b2","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.5131","article-title":"Environmental monitoring system for intelligent stations","volume":"33","author":"Li","year":"2021","journal-title":"Concurr Comput: Pr Exp"},{"key":"10.1016\/j.array.2026.100813_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2021.112510","article-title":"Sensors and systems for air quality assessment monitoring and management: A review","volume":"289","author":"Singh","year":"2021","journal-title":"J Env Manag"},{"issue":"8","key":"10.1016\/j.array.2026.100813_b4","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.3390\/electronics12081842","article-title":"Air pollution monitoring via wireless sensor networks: The investigation and correction of the aging behavior of electrochemical gaseous pollutant sensors","volume":"12","author":"Christakis","year":"2023","journal-title":"Electronics"},{"key":"10.1016\/j.array.2026.100813_b5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2020\/8897126","article-title":"Smart river monitoring using wireless sensor networks","volume":"2020","author":"Adu-Manu","year":"2020","journal-title":"Wirel Commun Mob Comput"},{"issue":"12","key":"10.1016\/j.array.2026.100813_b6","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1109\/LCOMM.2016.2612652","article-title":"Wireless sensor network for wildlife tracking and behavior classification of animals in Do\u00f1ana","volume":"20","author":"Dominguez-Morales","year":"2016","journal-title":"IEEE Commun Lett"},{"key":"10.1016\/j.array.2026.100813_b7","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.procs.2020.07.061","article-title":"Wireless sensor network for forest fire detection","volume":"175","author":"Varela","year":"2020","journal-title":"Procedia Comput Sci"},{"key":"10.1016\/j.array.2026.100813_b8","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.adhoc.2019.01.008","article-title":"Self-calibration methods for uncontrolled environments in sensor networks: A reference survey","volume":"88","author":"Barcelo-Ordinas","year":"2019","journal-title":"Ad Hoc Netw"},{"key":"10.1016\/j.array.2026.100813_b9","series-title":"2022 IEEE sensors","first-page":"1","article-title":"How to maintain accuracy of open cavity polymer based relative humidity sensors","author":"She","year":"2022"},{"key":"10.1016\/j.array.2026.100813_b10","series-title":"2020 IEEE 6th world forum on internet of things","first-page":"1","article-title":"Application of machine learning techniques for the calibration of low-cost IoT sensors in environmental monitoring networks","author":"Okafor","year":"2020"},{"key":"10.1016\/j.array.2026.100813_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.jenvman.2022.116910","article-title":"Two step calibration method for ozone low-cost sensor: Field experiences with the UrbanSense DCUs","volume":"328","author":"S\u00e1","year":"2023","journal-title":"J Env Manag"},{"issue":"5","key":"10.1016\/j.array.2026.100813_b12","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.3390\/s22051824","article-title":"Toward integrated large-scale environmental monitoring using WSN\/UAV\/Crowdsensing: A review of applications, signal processing, and future perspectives","volume":"22","author":"Fascista","year":"2022","journal-title":"Sensors-Basel"},{"issue":"2","key":"10.1016\/j.array.2026.100813_b13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446005","article-title":"Low-cost outdoor air quality monitoring and sensor calibration: A survey and critical analysis","volume":"17","author":"Concas","year":"2021","journal-title":"ACM Trans Sens Netw"},{"issue":"3","key":"10.1016\/j.array.2026.100813_b14","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1109\/COMST.2019.2904897","article-title":"Deep learning in mobile and wireless networking: A survey","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Commun Surv Tutor"},{"issue":"11","key":"10.1016\/j.array.2026.100813_b15","doi-asserted-by":"crossref","first-page":"12379","DOI":"10.1109\/JSEN.2020.3035846","article-title":"Machine learning for advanced wireless sensor networks: A review","volume":"21","author":"Kim","year":"2020","journal-title":"IEEE Sensors J"},{"key":"10.1016\/j.array.2026.100813_b16","series-title":"2023 IEEE international conference on big data (bigData)","first-page":"3779","article-title":"Wireless sensor networks calibration using attention-based gated recurrent units for air pollution monitoring","author":"Henna","year":"2023"},{"key":"10.1016\/j.array.2026.100813_b17","series-title":"2024 35th irish signals and systems conference","first-page":"01","article-title":"Accelerating deep learning for self-calibration in large-scale uncontrolled wireless sensor networks for environmental monitoring","author":"Yar","year":"2024"},{"issue":"3","key":"10.1016\/j.array.2026.100813_b18","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.comnet.2004.03.007","article-title":"Spatio-temporal correlation: theory and applications for wireless sensor networks","volume":"45","author":"Vuran","year":"2004","journal-title":"Comput Netw"},{"key":"10.1016\/j.array.2026.100813_b19","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.array.2026.100813_b20","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.array.2026.100813_b21","series-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"974","article-title":"Graph convolutional neural networks for web-scale recommender systems","author":"Ying","year":"2018"},{"key":"10.1016\/j.array.2026.100813_b22","series-title":"International conference on machine learning","first-page":"1106","article-title":"Learning steady-states of iterative algorithms over graphs","author":"Dai","year":"2018"},{"key":"10.1016\/j.array.2026.100813_b23","series-title":"International conference on machine learning","article-title":"Stochastic training of graph convolutional networks with variance reduction","author":"Chen","year":"2017"},{"key":"10.1016\/j.array.2026.100813_b24","series-title":"Calibrate and debias layer-wise sampling for graph convolutional networks","author":"Chen","year":"2022"},{"issue":"2","key":"10.1016\/j.array.2026.100813_b25","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1109\/JAS.2021.1004311","article-title":"Sampling methods for efficient training of graph convolutional networks: A survey","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE\/CAA J Autom Sin"},{"issue":"9","key":"10.1016\/j.array.2026.100813_b26","doi-asserted-by":"crossref","first-page":"6142","DOI":"10.1109\/TII.2021.3133289","article-title":"A graph neural network based deep learning predictor for spatio-temporal group solar irradiance forecasting","volume":"18","author":"Jiao","year":"2021","journal-title":"IEEE Trans Ind Inf"},{"issue":"3","key":"10.1016\/j.array.2026.100813_b27","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s41060-022-00349-6","article-title":"Graph neural networks for multivariate time series regression with application to seismic data","volume":"16","author":"Bloemheuvel","year":"2023","journal-title":"Int J Data Sci Anal"},{"key":"10.1016\/j.array.2026.100813_b28","series-title":"2021 international joint conference on neural networks","first-page":"1","article-title":"Spatial-temporal dynamic graph convolution neural network for air quality prediction","author":"Ouyang","year":"2021"},{"key":"10.1016\/j.array.2026.100813_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2023.162336","article-title":"A nested machine learning approach to short-term PM2. 5 prediction in metropolitan areas using PM2.5 data from different sensor networks","volume":"873","author":"Li","year":"2023","journal-title":"Sci Total Environ"},{"key":"10.1016\/j.array.2026.100813_b30","article-title":"HighAir: A hierarchical graph neural network-based air quality forecasting method","author":"Xu","year":"2021","journal-title":"Prepr ArXiv"},{"key":"10.1016\/j.array.2026.100813_b31","series-title":"Proceedings of the 26th ACM sIGSPATIAL international conference on advances in geographic information systems","first-page":"359","article-title":"Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning","author":"Lin","year":"2018"},{"key":"10.1016\/j.array.2026.100813_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.333","article-title":"A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory","volume":"664","author":"Qi","year":"2019","journal-title":"Sci Total Environ"},{"issue":"3","key":"10.1016\/j.array.2026.100813_b33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631713","article-title":"Group-aware graph neural network for nationwide city air quality forecasting","volume":"18","author":"Chen","year":"2023","journal-title":"ACM Trans Knowl Discov Data"},{"key":"10.1016\/j.array.2026.100813_b34","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","volume":"vol. 26","author":"Mikolov","year":"2013"},{"key":"10.1016\/j.array.2026.100813_b35","series-title":"Proceedings of the 26th annual international conference on machine learning","first-page":"41","article-title":"Curriculum learning","author":"Bengio","year":"2009"},{"key":"10.1016\/j.array.2026.100813_b36","series-title":"International conference on learning representations","article-title":"FastGCN: Fast learning with graph convolutional networks via importance sampling","author":"Chen","year":"2018"},{"key":"10.1016\/j.array.2026.100813_b37","series-title":"International conference on database systems for advanced applications","first-page":"364","article-title":"History driven sampling for scalable graph neural networks","author":"Li","year":"2024"},{"key":"10.1016\/j.array.2026.100813_b38","series-title":"Advances in neural information processing systems","article-title":"Adaptive sampling towards fast graph representation learning","volume":"31","author":"Huang","year":"2018"},{"key":"10.1016\/j.array.2026.100813_b39","series-title":"International conference on learning representations","article-title":"GraphSAINT: Graph sampling based inductive learning method","author":"Zeng","year":"2020"},{"key":"10.1016\/j.array.2026.100813_b40","article-title":"Layer-dependent importance sampling for training deep and large graph convolutional networks","volume":"vol. 32","author":"Zou","year":"2019"},{"key":"10.1016\/j.array.2026.100813_b41","series-title":"Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"1393","article-title":"Minimal variance sampling with provable guarantees for fast training of graph neural networks","author":"Cong","year":"2020"},{"key":"10.1016\/j.array.2026.100813_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2021.107127","article-title":"H2020 project CAPTOR dataset: Raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network","volume":"36","author":"Barcelo-Ordinas","year":"2021","journal-title":"Data Brief"},{"key":"10.1016\/j.array.2026.100813_b43","series-title":"Proceedings of the 2022 SIAM international conference on data mining","first-page":"199","article-title":"Towards similarity-aware time-series classification","author":"Zha","year":"2022"},{"key":"10.1016\/j.array.2026.100813_b44","series-title":"International conference on learning representations","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"issue":"449","key":"10.1016\/j.array.2026.100813_b45","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/01621459.2000.10473909","article-title":"Safe and effective importance sampling","volume":"95","author":"Owen","year":"2000","journal-title":"J Amer Statist Assoc"},{"key":"10.1016\/j.array.2026.100813_b46","series-title":"Artificial neural networks in pattern recognition: 8th IAPR TC3 workshop, ANNPR 2018, siena, Italy, September 19\u201321, 2018, proceedings 8","first-page":"201","article-title":"Inductive\u2013transductive learning with graph neural networks","author":"Rossi","year":"2018"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001360?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001360?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:07:12Z","timestamp":1777367232000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626001360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":46,"alternative-id":["S2590005626001360"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100813","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Accelerating graph-based deep learning for self-calibration in large-scale uncontrolled wireless sensor networks for environmental monitoring","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100813","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100813"}}