Journal Article
Machine Learning

Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios

Kinza Shafique(DHA Suffa University), Bilal A. Khawaja(Islamic University of Madinah), Farah Sabir(Karachi Institute of Economics and Technology), Sameer Qazi(Karachi Institute of Economics and Technology), Muhammad Mustaqim(National University of Sciences and Technology)
January 1, 2020IEEE Access1,315 citations
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Abstract

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird's eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Analysis

Why This Paper Matters

This paper is significant because it provides a comprehensive and structured overview of the Internet of Things (IoT) in the context of next-generation 5G networks. As IoT applications like augmented reality, autonomous vehicles, and smart healthcare demand higher data rates, low latency, and massive connectivity, understanding how 5G enabling technologies can support these requirements is critical. The paper bridges the gap between IoT and 5G by systematically reviewing key technologies such as massive MIMO, device-to-device communications, and network function virtualization, making it a valuable resource for both newcomers and experienced researchers.

Furthermore, the paper highlights the role of artificial intelligence, machine learning, and deep learning in driving new 5G-IoT use cases. By discussing ongoing 5G initiatives, QoS requirements, and standardization issues, it provides a forward-looking perspective that helps guide future research and development. The emphasis on edge computing as a complement to cloud-based platforms addresses a key implementation challenge, making the review practically relevant.

Technical Contributions

  • Comprehensive taxonomy of 5G-IoT enabling technologies: The paper categorizes and explains carrier aggregation, MIMO, massive-MIMO, CoMP, D2D, CRAN, SD-WSN, NFV, and cognitive radios, detailing their roles in IoT.
  • Integration of AI/ML with 5G-IoT: It discusses how advances in artificial intelligence, machine learning, and deep learning are creating new use cases and optimizing network operations.
  • Focus on QoS and standardization: The paper reviews quality of service requirements specific to 5G-IoT and highlights ongoing standardization efforts.
  • Edge-cloud computing trade-off analysis: It identifies the challenge of balancing high data-rate demands between cloud platforms and IoT edge devices.

Results

As a review paper, no experimental results or quantitative metrics are provided. The paper's value lies in its synthesis of existing knowledge and identification of trends, challenges, and future directions. It cites 1315 references, indicating its role as a comprehensive survey that aggregates a large body of prior work.

Significance

This paper has broad impact as a reference for researchers and engineers working on IoT and 5G convergence. By systematically organizing the landscape of enabling technologies and emerging use cases, it helps accelerate understanding and innovation in smart systems. Its discussion of AI-driven 5G-IoT scenarios points to future research directions where machine learning can optimize network performance, resource allocation, and security. The paper also underscores the importance of edge computing, which is critical for latency-sensitive applications. Overall, it serves as a foundational document that informs both academic research and industrial development in the rapidly evolving field of IoT and 5G.

iot5gsmart-systemsmachine-learningdeep-learningedge-computingnetwork-function-virtualization

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