Senshan Ouyang^1,2, Xiang Liu², Lei Liu², Shangchao Wang², Baichuan Shao³, Yang Zhao^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 903-915, 2024, DOI:10.32604/cmes.2023.028895

Abstract With the continuous expansion of the Industrial Internet of Things (IIoT), more and more organisations are placing large amounts of data in the cloud to reduce overheads. However, the channel between cloud servers and smart equipment is not trustworthy, so the issue of data authenticity needs to be addressed. The SM2 digital signature algorithm can provide an authentication mechanism for data to solve such problems. Unfortunately, it still suffers from the problem of key exposure. In order to address this concern, this study first introduces a key-insulated scheme, SM2-KI-SIGN, based on the SM2 algorithm. This scheme boasts strong key insulation… More >

Oliva Debnath¹, Saptarshi Debnath¹, Sreyashi Karmakar², MD Tausif Mallick³, Himadri Nath Saha^4,*

Journal on Internet of Things, Vol.5, pp. 13-43, 2023, DOI:10.32604/jiot.2023.039391

Abstract The Internet of Things (IoT) will significantly impact our social and economic lives in the near future. Many Internet of Things (IoT) applications aim to automate multiple tasks so inactive physical objects can behave independently of others. IoT devices, however, are also vulnerable, mostly because they lack the essential built-in security to thwart attackers. It is essential to perform the necessary adjustments in the structure of the IoT systems in order to create an end-to-end secure IoT environment. As a result, the IoT designs that are now in use do not completely support all of the advancements that have been… More >

Sun-Woo Yun¹, Na-Eun Park¹, Il-Gu Lee^1,2,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2897-2917, 2023, DOI:10.32604/iasc.2023.039940

Abstract As time and space constraints decrease due to the development of wireless communication network technology, the scale and scope of cyberattacks targeting the Internet of Things (IoT) are increasing. However, it is difficult to apply high-performance security modules to the IoT owing to the limited battery, memory capacity, and data transmission performance depending on the size of the device. Conventional research has mainly reduced power consumption by lightening encryption algorithms. However, it is difficult to defend large-scale information systems and networks against advanced and intelligent attacks because of the problem of deteriorating security performance. In this study, we propose wake-up… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^1,2, Sung-Jung Hsiao^3,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2179-2200, 2023, DOI:10.32604/cmc.2023.041022

Abstract Indonesia is a producer in the fisheries sector, with production reaching 14.8 million tons in 2022. The production potential of the fisheries sector can be optimally optimized through aquaculture management. One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions. IoT technology can be applied to support a fish pond aquaculture monitoring system, especially for catfish species (Siluriformes), in real-time and remotely. One of the technologies that can provide this convenience is the IoT. The problem of this study is how to integrate IoT devices with Firebase’s cloud data system to… More >

Anıl Sezgin^1,2,*, Aytuğ Boyacı³

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287

Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known as AID4I, makes use of… More >

Rahim Khan^1,3, Abdullah Ghani¹, Samia Allaoua Chelloug^2,*, Mohammed Amin⁴, Aamir Saeed⁵, Jason Teo¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1569-1584, 2023, DOI:10.32604/cmc.2023.039859

Abstract The Internet of Medical Things (IoMT) is mainly concerned with the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically, whereas machine learning approaches enable these smart systems to make informed decisions. Generally, broadcasting is used for the transmission of frames, whereas congestion, energy efficiency, and excessive load are among the common issues associated with existing approaches. In this paper, a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames, especially with the minimum possible communication overheads in the IoMT network. For this purpose, the proposed scheme utilises a well-known… More >

Alaa Omran Almagrabi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2079-2093, 2023, DOI:10.32604/cmc.2023.039683

Abstract A potential concept that could be effective for multiple applications is a “cyber-physical system” (CPS). The Internet of Things (IoT) has evolved as a research area, presenting new challenges in obtaining valuable data through environmental monitoring. The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction. This study employs a deep learning method, CNN-LSTM, and two-way feature extraction to classify audio systems within CPS. The primary objective of this system, which is built upon a convolutional neural network (CNN) with Long Short Term Memory (LSTM), is to analyze the vocalization patterns of two different… More >

Omar Reyad^1,2, Hazem Ibrahim Shehata^1,3, Mohamed Esmail Karar^1,4,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084

Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded random forest stages is automatically… More >

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 >

Ayman Khallel Al-Ani^1,*, Shams Ul Arfeen Laghari², Hariprasath Manoharan³, Shitharth Selvarajan⁴, Mueen Uddin⁵

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2261-2279, 2023, DOI:10.32604/cmc.2023.038534

Abstract In this paper, the application of transportation systems in real-time traffic conditions is evaluated with data handling representations. The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system. Compared to the existing approach, the design model in the proposed method is made by dividing the computing areas into several cluster regions, thereby reducing the complex monitoring system where control errors are minimized. Furthermore, a route management technique is combined with Artificial Intelligence (AI) algorithm to transmit the data to appropriate… More >