Smart City Gnosys

Smart city article details

Title	A Holistic Overview Of Anticipatory Learning For The Internet Of Moving Things: Research Challenges And Opportunities
ID_Doc	2102
Authors	Cao H.; Wachowicz M.
Year	2020
Published	ISPRS International Journal of Geo-Information, 9, 4
DOI	http://dx.doi.org/10.3390/ijgi9040272
Abstract	The proliferation of Internet of Things (IoT) systems has received much attention from the research community, and it has brought many innovations to smart cities, particularly through the Internet of Moving Things (IoMT). The dynamic geographic distribution of IoMT devices enables the devices to sense themselves and their surroundings on multiple spatio-temporal scales, interact with each other across a vast geographical area, and perform automated analytical tasks everywhere and anytime. Currently, most of the geospatial applications of IoMT systems are developed for abnormal detection and control monitoring. However, it is expected that, in the near future, optimization and prediction tasks will have a larger impact on the way citizens interact with smart cities. This paper examines the state of the art of IoMT systems and discusses their crucial role in supporting anticipatory learning. The maximum potential of IoMT systems in future smart cities can be fully exploited in terms of proactive decision making and decision delivery via an anticipatory action/feedback loop. We also examine the challenges and opportunities of anticipatory learning for IoMT systems in contrast to GIS. The holistic overview provided in this paper highlights the guidelines and directions for future research on this emerging topic. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	Anticipatory learning; GIS; Internet of moving things; IoT; Smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
8624	0.87	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)
52080	0.864	Zhao, YF; Liu, ZC; Qiu, C; Li, XH; Wang, XF; Hu, QH	Socialized Learning For Smart Cities: Cognitive Paradigm, Methodology, And Solution	IEEE WIRELESS COMMUNICATIONS, 28, 5 (2021)
33845	0.862	Salih S.; Abdelmaboud A.; Husain O.; Motwakel A.; Elshaﬁe H.; Sharif M.; Hamdan M.	Iot In Urban Development: Insight Into Smart City Applications, Case Studies, Challenges, And Future Prospects	PeerJ Computer Science, 11 (2025)
55972	0.862	Motta R.C.; Batista T.V.; Delicato F.C.	The Intersection Of The Internet Of Things And Smart Cities: A Tertiary Study	Journal of Internet Services and Applications, 15, 1 (2024)
22787	0.86	Rani S.	Emerging Technologies And The Application Of Wsn And Iot: Smart Surveillance, Public Security, And Safety Challenges	Emerging Technologies and the Application of WSN and IoT: Smart Surveillance, Public Security, and Safety Challenges (2024)
35974	0.859	Luan B.; Feng X.	Machine Learning For Resilient And Sustainable Cities: A Bibliometric Analysis Of Smart Urban Technologies	Buildings, 15, 7 (2025)
35091	0.858	Ahmad M.; Mumtaz R.; Khan M.A.	Leveraging Iot And Machine Learning For Smart Urban Planning	Leveraging IoT and Machine Learning for Smart Urban Planning (2025)
25991	0.858	Samui P.; Guharay A.; Mahmoudi E.	Facets Of A Smart City: Computational And Experimental Techniques For Sustainable Urban Development	Facets of a Smart City: Computational and Experimental Techniques for Sustainable Urban Development (2022)
7229	0.858	De Falco C.C.; Romeo E.	Algorithms And Geo-Discrimination Risk: What Hazards For Smart Cities’ Development?	Smart Cities: Lock-in, Path-dependence and Non-linearity of Digitalization and Smartification (2024)
899	0.857	Priyadarshi R.; Ranjan R.; Vishwakarma A.K.; Kumar R.R.	A Comprehensive Overview Of Transformative Potential Of Machine Learning And Wireless Sensor Networks In Sustainable Urban Development	2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024 (2024)