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Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges

Submission Deadline: 15 February 2023 (closed)

Guest Editors

Prof. Amr Tolba, King Saud University, Saudi Arabia
Prof. Zhaolong Ning, Chongqing University of Posts and Telecommunications, China
Dr. Ashutosh Dhar Dwivedi, Technical University of Denmark, Denmark


The fast growth of communication, processing and networking technology has enabled connected devices while bringing massive amounts of data to a variety of fields. The Internet of Things (IoT) is a technology that is assisting people in reimagining daily life, but AI is the true driving force behind the IoT's full potential. The IoT is empowered by three key emerging technologies: artificial intelligence (AI), 5G networks, and big data. The AIoT (Artificial Intelligence of Things) is being created as a result of these interconnected technologies. They are going to change the way people interact with devices at home and work. Wearables, smart homes, smart cities, and smart industries are the four primary areas where AIoT is having an influence on.

The AIoT's purpose is to bring intelligence to the edge, allowing devices to recognize data, assess their surroundings, and choose the best course of action. IoT devices have grown into intelligent machines capable of executing self-driven analytics and responding independently, thanks to the power of AI. Hence, IoT devices are no longer just messengers delivering messages to the control center. Although the integration of IoT and AI has created a variety of new technologies and applications, a number of challenges have emerged and need to be addressed by the research community. One important step in achieving AIoT is to connect multiple things in a collaborative manner. Connecting things or objects without collaboration results in AIoT issues such as wasteful energy consumption, unclear security, and inconsistent performance. Another crucial step is to connect AIoT with other modern technologies leading to convergence. Both academia and industry should pay close attention to how to properly connect AIoT with 5G networks, cloud computing, and blockchain, among other technologies.

Therefore, this special issue welcomes high-quality contributions in AIoT, machine learning, 5G networks, and big data from academic and industry-related experts who perform research on AI model creation for IoT systems and present the most cutting-edge approaches and applications.

Potential topics include but are not limited to the following:

• The AI technologies for IoT devices

• AIoT approaches for smart homes, cities, and industries

• Performance evaluations, simulation tools, and methodologies for the AIoT

• 5G network and communication technologies enabled AIoT

• Frameworks, algorithms, and applications that combine AI with IoT

• AIoT security, reliability, trust, and privacy

• Intelligent big data processing and IoT applications

• Intelligent resource management solutions

• Intelligent sensing and network connectivity

• Intelligent edge/fog/cloud computing

• Knowledge discovery for Big Data

• Learning models for IoT system

• IoT Wearables Solutions

• Blockchain and AI for smart homes, cities, and industries


Artificial Intelligence, Internet of Things, Big Data, Smart Cities, Wearables, 5G Networks, Blockchain

Published Papers

  • Open Access


    New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

    Shimaa M. Amer, Ashraf A. M. Khalaf, Amr H. Hussein, Salman A. Alqahtani, Mostafa H. Dahshan, Hossam M. Kassem
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2749-2767, 2024, DOI:10.32604/cmes.2023.029138
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract Side lobe level reduction (SLL) of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service (QOS) in recent and future wireless communication systems starting from 5G up to 7G. Furthermore, it improves the array gain and directivity, increasing the detection range and angular resolution of radar systems. This study proposes two highly efficient SLL reduction techniques. These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm (GA) to develop the Conv/GA and DConv/GA, respectively. The convolution process determines the element’s excitations while the GA optimizes… More >

  • Open Access


    SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks

    Zhihao Zeng, Zixiang Xia, Xiaoning Zhang, Yexiao He
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1791-1814, 2023, DOI:10.32604/cmes.2022.021648
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract The development of Fifth-Generation (5G) mobile communication technology has remarkably promoted the spread of the Internet of Things (IoT) applications. As a promising paradigm for IoT, edge computing can process the amount of data generated by mobile intelligent devices in less time response. Network Function Virtualization (NFV) that decouples network functions from dedicated hardware is an important architecture to implement edge computing, deploying heterogeneous Virtual Network Functions (VNF) (such as computer vision, natural language processing, intelligent control, etc.) on the edge service nodes. With the NFV MANO (Management and Orchestration) framework, a Service Function Chain (SFC) that contains a set… More >

  • Open Access


    An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

    Passent El-kafrawy, Maie Aboghazalah, Abdelmoty M. Ahmed, Hanaa Torkey, Ayman El-Sayed
    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted.… More >

  • Open Access


    A Dynamic Management Scheme for Internet of Things (IoT) Environments: Simulation and Performance Evaluation

    Omar Said
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 673-695, 2022, DOI:10.32604/cmes.2022.021160
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract In recent years, the Internet of Things (IoT) technology has been considered one of the most attractive fields for researchers due to its aspirations and implications for society and life as a whole. The IoT environment contains vast numbers of devices, equipment, and heterogeneous users who generate massive amounts of data. Furthermore, things’ entry into and exit from IoT systems occur dynamically, changing the topology and content of IoT networks very quickly. Therefore, managing IoT environments is among the most pressing challenges. This paper proposes an adaptive and dynamic scheme for managing IoT environments is proposed. This management scheme depends… More >

  • Open Access


    Performance Analysis of an Artificial Intelligence Nanosystem with Biological Internet of Nano Things

    Saied M. Abd El-atty, Nancy A. Arafa, Atef Abouelazm, Osama Alfarraj, Konstantinos A. Lizos, Farid Shawki
    CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 111-131, 2022, DOI:10.32604/cmes.2022.020793
    (This article belongs to this Special Issue: Artificial Intelligence of Things (AIoT): Emerging Trends and Challenges)
    Abstract Artificial intelligence (AI) has recently been used in nanomedical applications, in which implanted intelligent nanosystems inside the human body were used to diagnose and treat a variety of ailments with the help of the Internet of biological Nano Things (IoBNT). Biological circuit engineering or nanomaterial-based architectures can be used to approach the nanosystem. In nanomedical applications, the blood vascular medium serves as a communication channel, demonstrating a molecular communication system based on flow and diffusion. This paper presents a performance study of the channel capacity for flow-based-diffusive molecular communication nanosystems that takes into account the ligand-receptor binding mechanism. Unlike earlier… More >

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