Muchang Rao, Hang Qin^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380

Abstract More devices in the Intelligent Internet of Things (AIoT) result in an increased number of tasks that require low latency and real-time responsiveness, leading to an increased demand for computational resources. Cloud computing’s low-latency performance issues in AIoT scenarios have led researchers to explore fog computing as a complementary extension. However, the effective allocation of resources for task execution within fog environments, characterized by limitations and heterogeneity in computational resources, remains a formidable challenge. To tackle this challenge, in this study, we integrate fog computing and cloud computing. We begin by establishing a fog-cloud environment framework, followed by the formulation… More >

Weijian Fan^1,*, Ziwei Shi²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency and inconsistency… More >

Mingliang Sun, Jie Yuan^*, Xiaoyong Li, Dongxiao Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2625-2646, 2024, DOI:10.32604/cmc.2024.050337

Abstract Images are the most important carrier of human information. Moreover, how to safely transmit digital images through public channels has become an urgent problem. In this paper, we propose a novel image encryption algorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiency of image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSE can fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks, such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext… More >

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for different vulnerabilities, which are used… More >

Muhammad Salman Qamar^1,*, Ihsan ul Haq¹, Amil Daraz², Atif M. Alamri³, Salman A. AlQahtani⁴, Muhammad Fahad Munir¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168

Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs subsequently transmit packets to neighboring… More >

Abdulwadood Alawadhi^1,*, Mohd. Hasbullah Omar¹, Abdullah Almogahed², Noradila Nordin³, Salman A. Alqahtani⁴, Atif M. Alamri⁵

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2851-2878, 2024, DOI:10.32604/cmc.2024.050154

Abstract The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next… More >

Zheng Yang, Ke Gu^*, Yiming Zuo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2813-2831, 2024, DOI:10.32604/cmc.2024.050025

Abstract Federated learning is widely used to solve the problem of data decentralization and can provide privacy protection for data owners. However, since multiple participants are required in federated learning, this allows attackers to compromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciously created local models to the server to affect the prediction performance and training speed of the global model. To defend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoor triggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious local models can be identified… More >

Jiakai Li, Jianpeng Hu^*, Geng Zhang

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2481-2503, 2024, DOI:10.32604/cmc.2024.050005

Abstract In the process of constructing domain-specific knowledge graphs, the task of relational triple extraction plays a critical role in transforming unstructured text into structured information. Existing relational triple extraction models face multiple challenges when processing domain-specific data, including insufficient utilization of semantic interaction information between entities and relations, difficulties in handling challenging samples, and the scarcity of domain-specific datasets. To address these issues, our study introduces three innovative components: Relation semantic enhancement, data augmentation, and a voting strategy, all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks. We first propose an innovative attention interaction… More >

Qirui Zhong¹, Xiaogang Cheng^2,*, Yuxin Song³, Han Wang²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3417-3433, 2024, DOI:10.32604/cmc.2024.049992

Abstract This paper introduces an intelligent computational approach for extracting salient objects from images and estimating their distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications in numerous public places, serving various purposes such as public security management, natural disaster monitoring, and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructural projects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal and pitch rotation, as well as optical zoom, to estimate the distance of the object. We present a novel monocular object distance estimation model based on the… More >

Jiehao Ye, Wen Cheng, Xiaolong Liu, Wenyi Zhu, Xuan’ang Wu, Shigen Shen^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2743-2769, 2024, DOI:10.32604/cmc.2024.049985

Abstract The Internet of Things (IoT) has characteristics such as node mobility, node heterogeneity, link heterogeneity, and topology heterogeneity. In the face of the IoT characteristics and the explosive growth of IoT nodes, which brings about large-scale data processing requirements, edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions. However, the defense mechanism of Edge Computing-enabled IoT Nodes (ECIoTNs) is still weak due to their limited resources, so that they are susceptible to malicious software spread, which can compromise data confidentiality and network service… More >