Yu-Ting Huang^1,#, Daniel Kwasi Ahorsu^2,#, Emma Sethina Adjaottor^3,*, Frimpong-Manso Addo³, Mark D. Griffiths⁴, Amir H. Pakpour⁵, Chung-Ying Lin^1,6,7,8,*

International Journal of Mental Health Promotion, Vol.26, No.11, pp. 875-885, 2024, DOI:10.32604/ijmhp.2024.057049 - 28 November 2024

Abstract Background: The present study evaluated the psychometric properties of Problematic Internet Use (PIU) instruments and their correlation with psychological distress and time spent on Internet activities among university students in Ghana. Methods: In the present cross-sectional survey design study, 520 participants (35.96% female) were recruited with a mean age of 19.55 years (SD = 1.94) from several university departments (i.e., Behavioral Sciences, Materials Engineering, Nursing and Midwifery, and Biochemistry and Biotechnology) of Kwame Nkrumah University of Science and Technology (KNUST) between 19 July and 04 August, 2023. Participants completed a survey that included the following… More >

Altaf Hussain¹, Shuaiyong Li², Tariq Hussain³, Razaz Waheeb Attar⁴, Farman Ali^5,*, Ahmed Alhomoud⁶, Babar Shah⁷

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2357-2394, 2024, DOI:10.32604/cmc.2024.056694 - 18 November 2024

Abstract Ad hoc networks offer promising applications due to their ease of use, installation, and deployment, as they do not require a centralized control entity. In these networks, nodes function as senders, receivers, and routers. One such network is the Flying Ad hoc Network (FANET), where nodes operate in three dimensions (3D) using Unmanned Aerial Vehicles (UAVs) that are remotely controlled. With the integration of the Internet of Things (IoT), these nodes form an IoT-enabled network called the Internet of UAVs (IoU). However, the airborne nodes in FANET consume high energy due to their payloads and… More >

Ilsun You^1,*, Xiaofeng Chen², Vishal Sharma³, Gaurav Choudhary⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1907-1909, 2024, DOI:10.32604/cmes.2024.058939 - 31 October 2024

Abstract This article has no abstract. More >

Hamad Naeem¹, Amjad Alsirhani^2,*, Faeiz M. Alserhani³, Farhan Ullah⁴, Ondrej Krejcar¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2185-2223, 2024, DOI:10.32604/cmes.2024.056308 - 31 October 2024

Abstract When it comes to smart healthcare business systems, network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults. To protect IoMT devices and networks in healthcare and medical settings, our proposed model serves as a powerful tool for monitoring IoMT networks. This study presents a robust methodology for intrusion detection in Internet of Medical Things (IoMT) environments, integrating data augmentation, feature selection, and ensemble learning to effectively handle IoMT data complexity. Following rigorous preprocessing, including feature extraction, correlation removal, and Recursive Feature Elimination (RFE), selected features are standardized… More >

Attiya Khan¹, Muhammad Rizwan², Ovidiu Bagdasar^2,3, Abdulatif Alabdulatif^4,*, Sulaiman Alamro⁴, Abdullah Alnajim⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2121-2141, 2024, DOI:10.32604/cmes.2024.054380 - 31 October 2024

Abstract The Internet of Medical Things (IoMT) is an emerging technology that combines the Internet of Things (IoT) into the healthcare sector, which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs. As IoMT devices become more scalable, Smart Healthcare Systems (SHS) have become increasingly vulnerable to cyberattacks. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security. An IDS monitors systems or networks for suspicious activities or potential threats, safeguarding internal networks. This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets. We propose More >

Muhammad Imran Ghafoor¹, Awad Bin Naeem^2,*, Biswaranjan Senapati³, Md. Sakiul Islam Sudman⁴, Satyabrata Pradhan⁵, Debabrata Das⁶, Friban Almeida⁶, Hesham A. Sakr⁷

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 783-803, 2024, DOI:10.32604/iasc.2024.050819 - 31 October 2024

Abstract The confidentiality of pseudonymous authentication and secure data transmission is essential for the protection of information and mitigating risks posed by compromised vehicles. The Internet of Vehicles has meaningful applications, enabling connected and autonomous vehicles to interact with infrastructure, sensors, computing nodes, humans, and fellow vehicles. Vehicular hoc networks play an essential role in enhancing driving efficiency and safety by reducing traffic congestion while adhering to cryptographic security standards. This paper introduces a privacy-preserving Vehicle-to-Infrastructure authentication, utilizing encryption and the Moore curve. The proposed approach enables a vehicle to deduce the planned itinerary of Roadside More >

Arkan Kh Shakr Sabonchi^*

Journal on Artificial Intelligence, Vol.6, pp. 261-282, 2024, DOI:10.32604/jai.2024.056552 - 18 October 2024

Abstract The phenomenal increase in device connectivity is making the signaling and resource-based operational integrity of networks at the node level increasingly prone to distributed denial of service (DDoS) attacks. The current growth rate in the number of Internet of Things (IoT) attacks executed at the time of exchanging data over the Internet represents massive security hazards to IoT devices. In this regard, the present study proposes a new hybrid optimization technique that combines the firefly optimization algorithm with global searches for use in attack detection on IoT devices. We preprocessed two datasets, CICIDS and UNSW-NB15,… More >

Zerong Tang, Tiecheng Song^*, Jing Hu

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1835-1854, 2024, DOI:10.32604/cmc.2024.056983 - 15 October 2024

Abstract The Internet of Vehicles (IoV) will carry a large amount of security and privacy-related data, which makes the secure communication between the IoV terminals increasingly critical. This paper studies the joint beamforming for physical-layer security transmission in the coexistence of Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communication with Reconfigurable Intelligent Surface (RIS) assistance, taking into account hardware impairments. A communication model for physical-layer security transmission is established when the eavesdropping user is present and the base station antenna has hardware impairments assisted by RIS. Based on this model, we propose to maximize the V2I physical-layer security… More >

Jianhua Liu^*, Jincheng Wei, Rongxin Luo, Guilin Yuan, Jiajia Liu, Xiaoguang Tu

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1337-1361, 2024, DOI:10.32604/cmc.2024.056286 - 15 October 2024

Abstract With the rapid advancement of Internet of Vehicles (IoV) technology, the demands for real-time navigation, advanced driver-assistance systems (ADAS), vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and multimedia entertainment systems have made in-vehicle applications increasingly computing-intensive and delay-sensitive. These applications require significant computing resources, which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks, energy consumption, and cost constraints. To address this issue in IoV-based edge computing, particularly in scenarios where available computing resources in vehicles are scarce, a multi-master and multi-slave double-layer game model More >

Chi Cui^1,2, Haiping Du², Zhijuan Jia^1,*, Yuchu He¹, Lipeng Wang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1581-1593, 2024, DOI:10.32604/cmc.2024.055557 - 15 October 2024

Abstract The rapid evolution of artificial intelligence (AI) technologies has significantly propelled the advancement of the Internet of Vehicles (IoV). With AI support, represented by machine learning technology, vehicles gain the capability to make intelligent decisions. As a distributed learning paradigm, federated learning (FL) has emerged as a preferred solution in IoV. Compared to traditional centralized machine learning, FL reduces communication overhead and improves privacy protection. Despite these benefits, FL still faces some security and privacy concerns, such as poisoning attacks and inference attacks, prompting exploration into blockchain integration to enhance its security posture. This paper… More >