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A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities

People in Motion Lab, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
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ISPRS Int. J. Geo-Inf. 2020, 9(4), 272; https://doi.org/10.3390/ijgi9040272
Received: 5 March 2020 / Revised: 3 April 2020 / Accepted: 17 April 2020 / Published: 21 April 2020
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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. View Full-Text
Keywords: IoT; Internet of Moving Things; anticipatory learning; GIS; smart cities IoT; Internet of Moving Things; anticipatory learning; GIS; smart cities
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Cao, H.; Wachowicz, M. A Holistic Overview of Anticipatory Learning for the Internet of Moving Things: Research Challenges and Opportunities. ISPRS Int. J. Geo-Inf. 2020, 9, 272.

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