5G Technology for Internet of Things Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 454

Special Issue Editor


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Guest Editor
School of Computing, Dublin City University, D09 Y074 Dublin, Ireland
Interests: 5G networks
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Special Issue Information

Dear Colleagues,

Recent years have seen a significant surge in the number of Internet of Things (IoT) applications and services across different industry verticals, including healthcare, manufacturing, and automotive. These applications have different requirements, e.g., bandwidth, latency, reliability, and energy, that the current mobile networks cannot fully accommodate given the way in which they are built and operated. Fifth-generation technology represents an auspicious solution to the ever-growing user demands as it endorses a new architecture, called Open-RAN (O-RAN), that provides flexible and programmable network infrastructure that can be tailored to the specific needs of every application. It also implements Artificial Intelligence (AI) and Machine Learning (ML) techniques across different layers, i.e., Radio Access Network (RAN) and Core, to enhance network management and energy efficiency. Although efforts have lately been devoted to study and enhance the performance of the O-RAN architecture, several open issues need to be addressed, including service and resource management, energy consumption, security, and standardisation. As a result, this Special Issue aims to provide a comprehensive overview of theoretical and experimental research to help shape the future development and deployment of Open RAN-enabled IoT systems. Prospective authors are invited to submit original contributions in areas including, but not limited to, the following:

  • Novel 5G-enabled IoT frameworks;
  • O-RAN deployments for IoT use cases and applications;
  • Discovery algorithms of devices and services in 5G-enabled IoT systems;
  • Device search algorithms for 5G-enabled IoT systems;
  • Blockchain-enabled 5G-based IoT systems;
  • Intelligent devices for 5G security;
  • Optimisation algorithms for O-RAN resource allocation;
  • Interference mitigation algorithms for O-RAN;
  • Machine learning techniques for efficient energy consumption in 5G-enabled IoT systems;
  • RAN intelligent controllers;
  • Design, implementation, and evaluation of xApps, rApps, and dApps for O-RAN;
  • Machine learning techniques to enhance O-RAN security;
  • Conflict mitigation in O-RAN networks;
  • O-RAN testbeds and trials;
  • IoT in the 6th generation.

Dr. Mohammed Amine Togou
Guest Editor

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Keywords

  • 5th generation cellular communications
  • Internet of Things
  • open-RAN
  • intelligent devices
  • performance optimisation
  • energy efficiency

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Published Papers (1 paper)

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Research

23 pages, 2131 KB  
Article
Communication Base Station Site Selection Method Based on an Improved Genetic Algorithm
by Jinxuan Li, Hongyan Wang, Shengliang Fang, Youchen Fan and Shuya Zhang
Electronics 2025, 14(20), 3977; https://doi.org/10.3390/electronics14203977 - 10 Oct 2025
Viewed by 241
Abstract
With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing [...] Read more.
With the large-scale deployment of 5G technology, the rationality of communication base station siting is crucial for network performance, construction costs, and operational efficiency. Traditional site selection methods rely heavily on manual experience, exhibiting strong subjectivity and difficulty in balancing multi-objective optimization. Existing heuristic algorithms suffer from slow convergence speeds and susceptibility to local optima. To address these challenges, this paper constructs a multi-objective base station site selection model that simultaneously minimizes costs, maximizes coverage contributions, and minimizes interference. It achieves quantitative balance among objectives through normalization and weight fusion, while introducing constraints to ensure engineering feasibility. Concurrently, the genetic algorithm underwent targeted optimization by introducing an adaptive migration strategy based on population diversity and a cosine-type parameter adjustment strategy. This approach was integrated with the particle swarm optimization algorithm to balance exploration and exploitation while mitigating premature convergence. Experimental validation demonstrates that the improved algorithm achieves faster convergence and greater stability compared to traditional genetic algorithms and particle swarm optimization, while satisfying engineering constraints such as base station quantity, coverage, and interference. This research provides an efficient and feasible solution for intelligent base station site planning. Full article
(This article belongs to the Special Issue 5G Technology for Internet of Things Applications)
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