A New Approach to Recommender Conceptual Architecture in Learning Internet of Mobile Things

Authors

  • Masoud Kavosi * Deputy of Transport and Traffic, Shiraz Municipality, Shiraz, Iran.
  • Bahram Keshavarzdoost Head of Intelligent Systems Design and Development Department, Shiraz, Iran.
  • Fatemeh Hamedi Deputy of Transport and Traffic, Shiraz Municipality, Shiraz, Iran.

https://doi.org/10.48314/apem.v2i1.25

Abstract

Mobile Internet of Things (MIoT), one of the important sub-fields of the Internet of Things (IoT), faces several challenges, including mobility, dynamic environmental changes, resource constraints, and real-time data processing. Predictive learning, as a powerful approach to data analysis and prediction of future events, can play an important role in improving the performance of MIoT systems. This paper comprehensively reviews predictive learning in the MIoT field and examines the challenges and research opportunities. The expansion of IoT systems has attracted significant attention from the research community. It has brought many innovations to smart cities, primarily through the Internet of Mobile Things (IoMT). The dynamic geographic distribution of IoMT devices allows devices to sense themselves and their surroundings at multiple spatiotemporal scales, interact with each other over a large geographic area, and perform automated analytical tasks anywhere and anytime. Most geographic applications of IoMT systems are currently developed for anomaly detection and monitoring. However, shortly, optimization and prediction tasks are expected to have a greater impact on how citizens interact with smart cities. This paper reviews the state of the art of IoMT systems and discusses their critical role in supporting predictive learning. Predictive learning in MIoT leverages various machine learning and artificial intelligence methods. These methods fall into two broad categories: Supervised learning and unsupervised learning. In addition, more advanced hybrid methods such as deep learning and reinforcement learning are also being used in this area.

The maximum potential of IoMT systems in future smart cities can be fully exploited in active decision-making and decision delivery through a predictive action/feedback loop. We also examine the challenges and opportunities of predictive learning for IoMT systems in contrast to Geographic Information System (GIS). The overview presented in this paper highlights guidelines and policies for future research on this emerging topic.

Keywords:

Internet of things, Predictive learning, Smart cities

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Published

2025-03-10

Issue

Vol. 2 No. 1 (2025): Annals of Process Engineering and Management

Section

Articles

How to Cite

A New Approach to Recommender Conceptual Architecture in Learning Internet of Mobile Things. (2025). Annals of Process Engineering and Management, 2(1), 36-47. https://doi.org/10.48314/apem.v2i1.25