Jiarui Deng^1,2, Wuqiang Shen^1,3, Yihua Feng⁴, Guosheng Lu⁵, Guiquan Shen^1,3, Lei Cui^1,3, Shanxiang Lyu^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1345-1361, 2024, DOI:10.32604/cmc.2023.047079

Abstract The blockchain-empowered Internet of Vehicles (IoV) enables various services and achieves data security and privacy, significantly advancing modern vehicle systems. However, the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks. As a result, an efficient intrusion detection system (IDS) becomes crucial for securing the IoV environment. Existing IDSs based on convolutional neural networks (CNN) often suffer from high training time and storage requirements. In this paper, we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats. Our approach achieves superior performance, as demonstrated by… More >

Hongliang Tian, Junyuan Tian^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1297-1310, 2024, DOI:10.32604/cmc.2024.047058

Abstract The Internet of Things (IoT) access control mechanism may encounter security issues such as single point of failure and data tampering. To address these issues, a blockchain-based IoT reputation value attribute access control scheme is proposed. Firstly, writing the reputation value as an attribute into the access control policy, and then deploying the access control policy in the smart contract of the blockchain system can enable the system to provide more fine-grained access control; Secondly, storing a large amount of resources from the Internet of Things in Inter Planetary File System (IPFS) to improve system throughput; Finally, map resource access… More >

Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports a multi-output strategy (BONUS) for… More >

Hosam Alhakami^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 329-356, 2024, DOI:10.32604/cmc.2023.043439

Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks, with a focus on developing… More >

K. Akilandeswari¹, Nithya Rekha Sivakumar^2,*, Hend Khalid Alkahtani³, Shakila Basheer³, Sara Abdelwahab Ghorashi²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1189-1205, 2024, DOI:10.32604/cmc.2023.034815

Abstract In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression Feature Extraction model is used… More >

Mohamed Ammar^1,*, Alireza Mousavi¹, Hamed Al-Raweshidy^2,*

Digital Engineering and Digital Twin, Vol.2, pp. 1-31, 2024, DOI:10.32604/dedt.2023.044930

Abstract The advent of technology around the globe based on the Internet of Things, Cloud Computing, Big Data, Cyber-Physical Systems, and digitalisation. This advancement introduced industry 4.0. It is challenging to measure how enterprises adopt the new technologies. Industry 4.0 introduced Digital Twins, given that no specific terms or definitions are given to Digital Twins still challenging to define or conceptualise the Digital Twins. Many academics and industries still use old technologies, naming it Digital Twins. This young technology is in danger of reaching the plateau despite the immense benefit to sectors. This paper proposes a novel and unique definition for… More >

Dinesh Mavaluru^1,*, Chettupally Anil Carie², Ahmed I. Alutaibi³, Satish Anamalamudi², Bayapa Reddy Narapureddy⁴, Murali Krishna Enduri², Md Ezaz Ahmed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… More >

Xingwang Li^1,2, Hua Pang¹, Geng Li^1,*, Junjie Jiang¹, Hui Zhang³, Changfei Gu⁴, Dong Yuan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1135-1166, 2024, DOI:10.32604/cmes.2023.031534

Abstract The development of the fifth-generation (5G) mobile communication systems has entered the commercialization stage. 5G has a high data rate, low latency, and high reliability that can meet the basic demands of most industries and daily life, such as the Internet of Things (IoT), intelligent transportation systems, positioning, and navigation. The continuous progress and development of society have aroused wide concern. Positioning accuracy is the core demand for the applications, especially in complex environments such as airports, warehouses, supermarkets, and basements. However, many factors also affect the accuracy of positioning in those environments, for example, multipath effects, non-line-of-sight, and clock… More >

Wan Xiao^1,*, Miaoting Cheng^2,*

International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1303-1311, 2023, DOI:10.32604/ijmhp.2023.042976

Abstract Internet addiction and cyberbullying have emerged as significant global mental health concerns in recent years. Although previous studies have shown a close association between Internet addiction and cyberbullying, the underlying mechanisms connecting these two phenomena remain unclear. Therefore, this study aimed to reveal the mechanisms involved between Internet addiction and cyberbullying perpetration from the perspective of cognition function. This study recruited 976 Chinese youth through online survey, using the short version of Internet Addiction Test (s-IAT), Chinese Cyberbullying Intervention Project Questionnaire (C-CIPQ), Cyberbullying Moral Disengagement Scale (CMDS), and Internet Literacy Questionnaire (ILQ) to investigate the relationship between Internet addiction, moral… More >

Yichi Zhang^#, Yuxiang Feng^#, Shan Hu^*, Huiyong Fan^*

International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1323-1341, 2023, DOI:10.32604/ijmhp.2023.029096

Abstract Theoretical models have predicted a positive association between anxiety and Internet use disorders. However, the findings of previous studies are conflicting, with some reporting a positive association and others proposing no relationship between the two. To explore the true relationship between the two and analyze the reasons for the differences, 100 primary studies involving 108,539 subjects were entered into a meta-analysis. The results showed that (1) there was a significant positive correlation between students’ anxiety and Internet use disorder (r = 0.330); (2) the moderating effect of anxiety type was significant. (3) The moderating effects of the measurement instrument for… More >