Smart City Gnosys

Smart city article details

Title	Machine Learning-Enabled Internet Of Things (Iot): Data, Applications, And Industry Perspective
ID_Doc	36075
Authors	Bzai J.; Alam F.; Dhafer A.; Bojović M.; Altowaijri S.M.; Niazi I.K.; Mehmood R.
Year	2022
Published	Electronics (Switzerland), 11, 17
DOI	http://dx.doi.org/10.3390/electronics11172676
Abstract	Machine learning (ML) allows the Internet of Things (IoT) to gain hidden insights from the treasure trove of sensed data and be truly ubiquitous without explicitly looking for knowledge and data patterns. Without ML, IoT cannot withstand the future requirements of businesses, governments, and individual users. The primary goal of IoT is to perceive what is happening in our surroundings and allow automation of decision-making through intelligent methods, which will mimic the decisions made by humans. In this paper, we classify and discuss the literature on ML-enabled IoT from three perspectives: data, application, and industry. We elaborate with dozens of cutting-edge methods and applications through a review of around 300 published sources on how ML and IoT work together to play a crucial role in making our environments smarter. We also discuss emerging IoT trends, including the Internet of Behavior (IoB), pandemic management, connected autonomous vehicles, edge and fog computing, and lightweight deep learning. Further, we classify challenges to IoT in four classes: technological, individual, business, and society. This paper will help exploit IoT opportunities and challenges to make our societies more prosperous and sustainable. © 2022 by the authors.
Author Keywords	data imputation; edge and fog computing; feature selection; Internet of Behavior (IoB); Internet of Things (IoT); lightweight deep learning; machine learning; outliers; smart cities; smart homes

Similar Articles

Id	Similarity	Authors	Title	Published
28691	0.942	Goel N.; Yadav R.K.	Handbook Of Research On Machine Learning-Enabled Iot For Smart Applications Across Industries	Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries (2023)
35888	0.936	Rashmi Bandara M.S.; Halgamuge M.N.; Marques G.	Machine Learning And Internet Of Things For Smart Living: A Comprehensive Review And Analysis	Studies in Fuzziness and Soft Computing, 410 (2021)
35982	0.933	Alsamhi S.H.; Almalki F.A.; Al-Dois H.; Ben Othman S.; Hassan J.; Hawbani A.; Sahal R.; Lee B.; Saleh H.	Machine Learning For Smart Environments In B5G Networks: Connectivity And Qos	Computational Intelligence and Neuroscience, 2021 (2021)
56602	0.926	Mishra S.; Tyagi A.K.	The Role Of Machine Learning Techniques In Internet Of Things-Based Cloud Applications	Internet of Things (2022)
36064	0.924	Alfahaid A.; Alalwany E.; Almars A.M.; Alharbi F.; Atlam E.; Mahgoub I.	Machine Learning-Based Security Solutions For Iot Networks: A Comprehensive Survey	Sensors, 25, 11 (2025)
8624	0.924	Peralta Abadía J.J.; Smarsly K.	An Introduction And Systematic Review On Machine Learning For Smart Environments/Cities: An Iot Approach	Intelligent Systems Reference Library, 121 (2022)
35990	0.922	Chen Z.; Tian P.; Qian C.; Liao W.; Hussaini A.; Yu W.	Machine Learning Frameworks In Iot Systems: A Survey, Case Study, And Future Research Directions	Smart Spaces: a volume in Intelligent Data-Centric Systems (2024)
36025	0.919	Afshan N.; Rout R.K.	Machine Learning Techniques For Iot Data Analytics	Big Data Analytics for Internet of Things (2021)
6522	0.917	Dhanalakshmi M.; Nand Kishor Kumar G.; Himabindu G.; Gosavi V.R.; Sundar Ganesh C.S.; Premanand R.	Advanced Machine Learning Innovations In Embedded Systems And Narrowband Internet Of Things (Nb-Iot) Devices	Integrating Artificial Intelligence Into the Energy Sector (2025)
32899	0.917	Sarker I.H.; Khan A.I.; Abushark Y.B.; Alsolami F.	Internet Of Things (Iot) Security Intelligence: A Comprehensive Overview, Machine Learning Solutions And Research Directions	Mobile Networks and Applications, 28, 1 (2023)