Hawazen Alzahrani¹, Tarek Sheltami¹, Abdulaziz Barnawi², Muhammad Imam^2,*, Ansar Yaser³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4703-4728, 2024, DOI:10.32604/cmc.2024.054203 - 12 September 2024

Abstract The Internet of Things (IoT) links various devices to digital services and significantly improves the quality of our lives. However, as IoT connectivity is growing rapidly, so do the risks of network vulnerabilities and threats. Many interesting Intrusion Detection Systems (IDSs) are presented based on machine learning (ML) techniques to overcome this problem. Given the resource limitations of fog computing environments, a lightweight IDS is essential. This paper introduces a hybrid deep learning (DL) method that combines convolutional neural networks (CNN) and long short-term memory (LSTM) to build an energy-aware, anomaly-based IDS. We test this… More >

Linghong Kuang^1,2, Wenlong Shi^1,2, Jing Zhang^1,2,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3193-3219, 2024, DOI:10.32604/cmc.2024.054377 - 15 August 2024

Abstract The Advanced Metering Infrastructure (AMI), as a crucial subsystem in the smart grid, is responsible for measuring user electricity consumption and plays a vital role in communication between providers and consumers. However, with the advancement of information and communication technology, new security and privacy challenges have emerged for AMI. To address these challenges and enhance the security and privacy of user data in the smart grid, a Hierarchical Privacy Protection Model in Advanced Metering Infrastructure based on Cloud and Fog Assistance (HPPM-AMICFA) is proposed in this paper. The proposed model integrates cloud and fog computing… More >

Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2557-2578, 2024, DOI:10.32604/cmc.2024.051634 - 15 August 2024

Abstract In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework… More >

Abdulelah Alwabel^1,*, Chinmaya Kumar Swain²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4127-4148, 2024, DOI:10.32604/cmc.2024.048833 - 20 June 2024

Abstract Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data sources. However, the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes, thus making the application placement problem more complex than that in cloud computing. An approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the… More >

Muchang Rao, Hang Qin^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2647-2672, 2024, DOI:10.32604/cmc.2024.050380 - 15 May 2024

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… More >

Nagaraju Pacharla, K. Srinivasa Reddy^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3089-3110, 2024, DOI:10.32604/cmc.2024.048133 - 15 May 2024

Abstract With the recent technological developments, massive vehicular ad hoc networks (VANETs) have been established, enabling numerous vehicles and their respective Road Side Unit (RSU) components to communicate with one another. The best way to enhance traffic flow for vehicles and traffic management departments is to share the data they receive. There needs to be more protection for the VANET systems. An effective and safe method of outsourcing is suggested, which reduces computation costs by achieving data security using a homomorphic mapping based on the conjugate operation of matrices. This research proposes a VANET-based data outsourcing… More >

Asier Garmendia-Orbegozo¹, Jose David Nunez-Gonzalez^1,*, Miguel Angel Anton²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2649-2671, 2024, DOI:10.32604/cmes.2024.045912 - 11 March 2024

Abstract In a network environment composed of different types of computing centers that can be divided into different layers (clod, edge layer, and others), the interconnection between them offers the possibility of peer-to-peer task offloading. For many resource-constrained devices, the computation of many types of tasks is not feasible because they cannot support such computations as they do not have enough available memory and processing capacity. In this scenario, it is worth considering transferring these tasks to resource-rich platforms, such as Edge Data Centers or remote cloud servers. For different reasons, it is more exciting and… More > Graphic Abstract

Yuecheng Yu^1,*, Liming Cai¹, Anqi Ning¹, Jinlong Shi¹, Xudong Chen², Shixin Huang¹

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1915-1931, 2024, DOI:10.32604/cmc.2024.046125 - 27 February 2024

Abstract Under the influence of air humidity, dust, aerosols, etc., in real scenes, haze presents an uneven state. In this way, the image quality and contrast will decrease. In this case, It is difficult to detect the target in the image by the universal detection network. Thus, a dual subnet based on multi-task collaborative training (DSMCT) is proposed in this paper. Firstly, in the training phase, the Gated Context Aggregation Network (GCANet) is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes. In the test phase, only the… More >

Yurong Luo, Wei You^*, Chao Shang, Xiongpeng Ren, Jin Cao, Hui Li

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2237-2260, 2024, DOI:10.32604/cmes.2023.045679 - 29 January 2024

Abstract The dynamic landscape of the Internet of Things (IoT) is set to revolutionize the pace of interaction among entities, ushering in a proliferation of applications characterized by heightened quality and diversity. Among the pivotal applications within the realm of IoT, as a significant example, the Smart Grid (SG) evolves into intricate networks of energy deployment marked by data integration. This evolution concurrently entails data interchange with other IoT entities. However, there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem. In this paper, we introduce a More >

Hayder Makki Shakir, Jaber Karimpour^*, Jafar Razmara

Computer Systems Science and Engineering, Vol.48, No.1, pp. 35-55, 2024, DOI:10.32604/csse.2023.038545 - 26 January 2024

Abstract As the volume of data and data-generating equipment in healthcare settings grows, so do issues like latency and inefficient processing inside health monitoring systems. The Internet of Things (IoT) has been used to create a wide variety of health monitoring systems. Most modern health monitoring solutions are based on cloud computing. However, large-scale deployment of latency-sensitive healthcare applications is hampered by the cloud’s design, which introduces significant delays during the processing of vast data volumes. By strategically positioning servers close to end users, fog computing mitigates latency issues and dramatically improves scaling on demand, resource… More >