Advanced Intelligent Systems Based on Internet of Things for 6G Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 April 2024 | Viewed by 948

Special Issue Editors


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Guest Editor
Department of Computer Science, Middlesex University London, London NW4 4BT, UK
Interests: cloud/edge computing; IoT; edge intelligence; 5G/6G; network security; NFV/SDN; network optimization

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Guest Editor
Department of Computer Science, Karlstad University, 651 88 Karlstad, Sweden
Interests: cloud computing; edge computing; optimization; artificial intelligence; high-performance computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Information Technology, Uppsala University, 752 36 Uppsala, Sweden
Interests: network management; network measurement; machine learning; network performance; IoT

Special Issue Information

Dear Colleagues

The Internet of Things (IoT) has the potential to play a significant role in creating more advanced systems in various applications and industries such as smart cities and homes, cloud/edge computing, 5G/6G, healthcare, smart manufacturing, environment monitoring, retail, agriculture, etc. Integrating artificial intelligence and data analytics, IoT presents opportunities to enhance these systems towards advanced intelligent systems (AIS), revolutionizing various industries by providing improvements in efficiency and reducing costs. AIS leverage AI algorithms to make sense of vast amount of data generated by IoT devices, enabling optimization, automation, and smart decision-making. However, there are still many open challenges in this area to be explored, especially in the context of future mobile networks and their use cases.

This Special Issue aims to showcase research articles that contribute significant advancements in applying IoT and AI techniques in different use cases of the next generation of mobile networks (6G); 6G technology is still in its early stages of research and development, and the actual implementation and capabilities may evolve over time. However, the potential for IoT applications in 6G networks is vast, promising to bring about transformative changes across various industries and aspects of daily life.

This Special Issue welcomes submissions investigating IoT’s benefits and applications in 6G networks while addressing the challenges of current applications and systems such as energy efficacy, security and privacy, interoperability, reliability, data management, connectivity, etc. The topics of interest for this Special Issue include, but are not limited to, the application of AI, IoT and data analytics for:

  • Massive IoT connectivity
  • Ubiquitous sensing
  • Holographic communications
  • Tactile Internet
  • AI-driven autonomous systems
  • Smart infrastructure
  • Enhanced wearables
  • Supply chain and logistics
  • Remote education and training
  • Agriculture and environment
  • Wildlife conservation
  • Entertainment and media

Dr. Ali Khoshkholghi
Prof. Dr. Javid Taheri
Dr. Andreas Johnsson
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • Internet of Things
  • artificial intelligence
  • security and privacy for intelligent systems
  • cloud/edge computing
  • federated learning
  • big data analytics

Published Papers (1 paper)

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Research

16 pages, 3142 KiB  
Article
A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical Wireless Sensor Networks Using Long Short-Term Memory
by Issam Al-Nader, Aboubaker Lasebae, Rand Raheem and Ali Khoshkholghi
Electronics 2023, 12(23), 4766; https://doi.org/10.3390/electronics12234766 - 24 Nov 2023
Cited by 2 | Viewed by 705
Abstract
The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these [...] Read more.
The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these three requirements is no longer considered the standard approach for enhancing system dependability. To illustrate, a prospective solution may optimise one or two of these requirements while bolstering overall network energy efficiency. Nonetheless, prior endeavours documented in the extant literature reveal unexplored avenues for enhancement. Hence, this paper introduces a new methodology aimed at alleviating MOO concerns and thereby enhancing the quality of service (QoS) in WSNs. A long short-term memory (LSTM) model is proposed as an analytical tool to deliver an energy-efficient scheduling solution that aligns and optimises WSN parameters, striving to attain the most favourable system performance. The LSTM algorithm’s effectiveness is assessed through the iterative application of periods, confirming the desired QoS levels. The unique feature of LSTM lies in its capability to observe specific event sequences and subsequently establish them as the system’s default configuration for its entire operational lifespan. Once these favourable parameters are identified, LSTM automatically ensures consistent service availability and reliability throughout the network’s lifespan. The results obtained demonstrate the superiority of the proposed LSTM-based scheduling algorithm in comparison to the self-organising map (SOFM)-based node scheduling algorithm. The LSTM-based approach outperforms the SOFM-based alternative by a remarkable 75% in terms of coverage and exhibits a 20% enhancement in network lifetime, all while maintaining equivalent levels of connectivity (i.e., 99%) in both algorithms. Full article
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