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The Future of IoT: Advanced AI Based IoT Technologies and...
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The Future of IoT: Advanced AI Based IoT Technologies and Applications

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1960

Special Issue Editors

Dr. Laith Farhan

E-Mail Website
Guest Editor
1. School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
2. Electric Computer Center, University of Diyala, Baqubah, Iraq
Interests: energy optimisation in the IoT and WSN networks; network management; smart city design and planning; machine learning; big data analytics; routing; wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Xingwang Li

E-Mail Website
Guest Editor
School of Physics and Electronic Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
Interests: non-orthogonal multiple access (NOMA) schemes; physical layer security; backscatter communication for internet of things; reconfigurable intelligent metasurfaces for 6G communications; ultra-reliable and low-latency communication (URLLC); unmanned aerial vehicle (UAV) communication; hardware-constrained communication systems; unmanned aerial vehicle communication; multiple-input multiple-output (MIMO); energy harvesting
Special Issues, Collections and Topics in MDPI journals
Dr. Waled Gheth

E-Mail Website
Guest Editor
School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK
Interests: Monte Carlo methods; carrier transmission on power lines; Internet of Things; indoor communication; optical communication; relay networks (telecommunication); amplify and forward communication; broadband networks; channel capacity; wireless channels; wireless sensor networks

Special Issue Information

Dear Colleagues,

Applications of the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) can redefine the way academics, industries, businesses, and economies functions. IoT-based AI improves human–machine interactions and enhances data management and analytics and, thus, supports decision making processes. Typically, smart IoT networks are embedded with sensors that can sense and gather data from the environment. The connectivity of IoT applications creates seamless data flow and communication across various systems and platforms. These sensors can communicate with each other, exchange information, and transmit data to intelligent AI systems for analysis. AI methods can be used to analyse these data collections to derive meaningful insights. The integration of AI and IoT technologies introduces several benefits such as enhanced operational efficiency, increased system scalability, predictive maintenance, and the ability to control conditions in real time.

This Special Issue is focused on a specific area of research that falls within the scope of this volume. This Special Issue also aims to reveal various factors that affect the use of IoT-based AI technologies in different fields such as healthcare, wearable technology, smart grid, smart cities, autonomous vehicles, retail analytics, industrial, agriculture, etc. This Special Issue welcomes high-quality survey papers. Areas of interest include, but are not necessarily limited to, the following:

  • Artificial intelligence (AI) in Internet of Things (IoT) networks
  • AI-enabled smart cities.
  • AI-enabled IoT in healthcare.
  • AI-based green energy for IoT applications.
  • AI for data monitoring and management systems.
  • Machine learning-based data gathering for IoT.
  • Deep learning in biomedical data processing.
  • Network analysing and slicing recognition.
  • Cybersecurity challenges for smart cities.
  • Sustainability in IoT networks.
  • Cloud, AI, and IoT integration.
  • AI for wireless sensor networks (WSNs).
  • Cyber-security and privacy in IoT.

Dr. Laith Farhan
Dr. Xingwang Li
Dr. Waled Gheth
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things (IoT)
  • artificial intelligence (AI)
  • smart cities
  • AI-enabled IoT
  • wireless sensor networks (WSNs)
  • cyber-security
  • machine learning

Published Papers (2 papers)

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Research

16 pages, 3977 KiB  
Article
Energy Efficient CLB Design Based on Adiabatic Logic for IoT Applications
by Wu Yang, Milad Tanavardi Nasab and Himanshu Thapliyal
Electronics 2024, 13(7), 1309; https://doi.org/10.3390/electronics13071309 - 31 Mar 2024
Viewed by 662
Abstract
Many IoT applications require high computational performance and flexibility, and FPGA is a promising candidate. However, increased computation power results in higher energy dissipation, and energy efficiency is one of the key concerns for IoT applications. In this paper, we explore adiabatic logic [...] Read more.
Many IoT applications require high computational performance and flexibility, and FPGA is a promising candidate. However, increased computation power results in higher energy dissipation, and energy efficiency is one of the key concerns for IoT applications. In this paper, we explore adiabatic logic for designing an energy efficient configurable logic block (CLB) and compare it to the CMOS counterpart. The simulation results show that the proposed adiabatic-logic-based look-up table (LUT) has significant energy savings for the frequency range of 1 MHz to 40 MHz, and the least energy savings is at 40 MHz, which is 92.94% energy reduction compared to its CMOS counterpart. Further, the three proposed adiabatic-logic-based memory cells are 14T, 16T, and 12T designs with at least 88.2%, 84.2%, and 87.2% energy savings. Also, we evaluated the performance of the proposed CLBs using an adiabatic-logic-based LUT (AL-LUT) interfacing with adiabatic-logic-based memory cells. The proposed design shows significant energy reduction compared to a CMOS LUT interface with SRAM cells for different frequencies; the energy savings are at least 91.6% for AL-LUT 14T, 89.7% for AL-LUT 16T, and 91.3% AL-LUT 12T. Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
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21 pages, 420 KiB  
Article
Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks
by Yanping Zhou, Fang Deng and Shidang Li
Electronics 2024, 13(2), 299; https://doi.org/10.3390/electronics13020299 - 09 Jan 2024
Viewed by 514
Abstract
To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS [...] Read more.
To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics. Full article
(This article belongs to the Special Issue The Future of IoT: Advanced AI Based IoT Technologies and Applications)
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