Rayan A. Alsemmeari^1,*, Mohamed Yehia Dahab², Badraddin Alturki¹, Abdulaziz A. Alsulami³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1853-1870, 2023, DOI:10.32604/cmc.2023.038589

Abstract Emerging telemedicine trends, such as the Internet of Medical Things (IoMT), facilitate regular and efficient interactions between medical devices and computing devices. The importance of IoMT comes from the need to continuously monitor patients’ health conditions in real-time during normal daily activities, which is realized with the help of various wearable devices and sensors. One major health problem is workplace stress, which can lead to cardiovascular disease or psychiatric disorders. Therefore, real-time monitoring of employees’ stress in the workplace is essential. Stress levels and the source of stress could be detected early in the fog layer so that the negative… More >

Wan Norsyafizan W. Muhamad¹, Kaharudin Dimyati², Muhammad Awais Javed³, Suzi Seroja Sarnin^1,*, Divine Senanu Ametefe¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1291-1308, 2023, DOI:10.32604/cmc.2023.037214

Abstract The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network.… More >

Baoling Qin^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 255-267, 2023, DOI:10.32604/iasc.2023.037556

Abstract Although the Internet of Things has been widely applied, the problems of cloud computing in the application of digital smart medical Big Data collection, processing, analysis, and storage remain, especially the low efficiency of medical diagnosis. And with the wide application of the Internet of Things and Big Data in the medical field, medical Big Data is increasing in geometric magnitude resulting in cloud service overload, insufficient storage, communication delay, and network congestion. In order to solve these medical and network problems, a medical big-data-oriented fog computing architecture and BP algorithm application are proposed, and its structural advantages and characteristics… More >

Anu^*, Anita Singhrova

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 199-219, 2023, DOI:10.32604/iasc.2023.035389

Abstract Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things (IoT) applications in the proximity of the network. Fog computing offers computational and storage services between cloud and terminal devices. However, an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services. A large number of heterogeneous shareable resources makes fog computing a complex environment. In the sight of these issues, this paper has proposed an efficient levy flight firefly-based resource allocation technique. The levy flight… More >

Abdulaziz Aldribi^1,2,*, Aman Singh^2,3, Jose Breñosa^3,4

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3865-3881, 2023, DOI:10.32604/csse.2023.037748

Abstract Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications. Internet of Things (IoT), fog computing, edge computing, cloud computing, and the edge of things are the spine of all real-time and scalable applications. Conspicuously, this study proposed a novel framework for a real-time and scalable application that changes dynamically with time. In this study, IoT deployment is recommended for data acquisition. The Pre-Processing of data with local edge and fog nodes is implemented in this study. The threshold-oriented data classification method is deployed to improve the intrusion detection mechanism’s performance.… More >

Abeera Ilyas^1,*, Saeed Mahfooz¹, Zahid Mehmood^2,3, Gauhar Ali⁴, Muhammad ElAffendi⁴

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3815-3829, 2023, DOI:10.32604/csse.2023.036316

Abstract Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and use while increasing real-time data transmission of device gateways used for sensors by making educated judgments… More >

Siguang Chen^1,2,*, Li Yang^1,2, Yanhang Shi^1,2, Qian Wang¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3781-3796, 2023, DOI:10.32604/cmc.2023.036437

Abstract As an essential component of intelligent transportation systems (ITS), electric vehicles (EVs) can store massive amounts of electric power in their batteries and send power back to a charging station (CS) at peak hours to balance the power supply and generate profits. However, when the system collects the corresponding power data, several severe security and privacy issues are encountered. The identity and private injection data may be maliciously intercepted by network attackers and be tampered with to damage the services of ITS and smart grids. Existing approaches requiring high computational overhead render them unsuitable for the resource-constrained Internet of Things… More >

J. Prabakaran^1,*, P. Selvaraj²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2141-2157, 2023, DOI:10.32604/csse.2023.034210

Abstract In healthcare systems, the Internet of Things (IoT) innovation and development approached new ways to evaluate patient data. A cloud-based platform tends to process data generated by IoT medical devices instead of high storage, and computational hardware. In this paper, an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography (CT) images of patients with pneumonia, Covid-19, tuberculosis (TB), and cancer. Firstly, the CT images are captured and transmitted to the fog node through IoT devices. In the fog node, the image gets modified into a convenient and efficient format… More >

Samah Ibrahim AlShathri, Dina S. M. Hassan^*, Samia Allaoua Chelloug

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1501-1526, 2023, DOI:10.32604/cmc.2023.035602

Abstract Task offloading is a key strategy in Fog Computing (FC). The definition of resource-constrained devices no longer applies to sensors and Internet of Things (IoT) embedded system devices alone. Smart and mobile units can also be viewed as resource-constrained devices if the power, cloud applications, and data cloud are included in the set of required resources. In a cloud-fog-based architecture, a task instance running on an end device may need to be offloaded to a fog node to complete its execution. However, in a busy network, a second offloading decision is required when the fog node becomes overloaded. The possibility… More >

D. Deepa, K. R. Jothi^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 137-151, 2023, DOI:10.32604/csse.2023.032485

Abstract Every day, more and more data is being produced by the Internet of Things (IoT) applications. IoT data differ in amount, diversity, veracity, and velocity. Because of latency, various types of data handling in cloud computing are not suitable for many time-sensitive applications. When users move from one site to another, mobility also adds to the latency. By placing computing close to IoT devices with mobility support, fog computing addresses these problems. An efficient Load Balancing Algorithm (LBA) improves user experience and Quality of Service (QoS). Classification of Request (CoR) based Resource Adaptive LBA is suggested in this research. This… More >