Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf

Intelligent Automation & Soft Computing, Vol.40, pp. 29-52, 2025, DOI:10.32604/iasc.2024.060390 - 10 January 2025

Abstract The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, More >

Kwang Chee Seng, Siti Fatimah Abdul Razak^*, Sumendra Yogarayan

Computer Systems Science and Engineering, Vol.49, pp. 239-258, 2025, DOI:10.32604/csse.2024.056582 - 10 January 2025

Abstract Vehicle overtaking poses significant risks and leads to injuries and losses on Malaysia’s roads. In most scenarios, insufficient and untimely information available to drivers for accessing road conditions and their surrounding environment is the primary factor that causes these incidents. To address these issues, a comprehensive system is required to provide real-time assistance to drivers. Building upon our previous research on a LoRa-based lane change decision-aid system, this study proposes an enhanced Vehicle Overtaking System (VOS). This system utilizes long-range (LoRa) communication for reliable real-time data exchange between vehicles (V2V) and the cloud (V2C). By More >

Tabassum Kanwal¹, Saif Ur Rehman^1,*, Azhar Imran², Haitham A. Mahmoud³

Computer Systems Science and Engineering, Vol.49, pp. 185-212, 2025, DOI:10.32604/csse.2024.056535 - 10 January 2025

Abstract This study presents an energy-efficient Internet of Things (IoT)-based wireless sensor network (WSN) framework for autonomous data validation in remote environmental monitoring. We address two critical challenges in WSNs: ensuring data reliability and optimizing energy consumption. Our novel approach integrates an artificial neural network (ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture. The proposed ANN model is designed to simultaneously detect multiple fault types, including spike faults, stuck-at faults, outliers, and out-of-range faults. We collected sensor data at 5-minute intervals over three months, using temperature and humidity sensors. The ANN was trained on 70%… More >

Sarvenaz Sadat Khatami¹, Mehrdad Shoeibi², Anita Ershadi Oskouei³, Diego Martín^4,*, Maral Keramat Dashliboroun⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 881-911, 2025, DOI:10.32604/cmc.2024.059999 - 03 January 2025

Abstract The Internet of Things (IoT) is integral to modern infrastructure, enabling connectivity among a wide range of devices from home automation to industrial control systems. With the exponential increase in data generated by these interconnected devices, robust anomaly detection mechanisms are essential. Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns. This paper presents a novel approach utilizing generative adversarial networks (GANs) for anomaly detection in IoT systems. However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms,… More >

Jinlong Wang^1,2,*, Zhenyu Liu¹, Xingtao Yang¹, Min Li¹, Zhihan Lyu³

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1-39, 2025, DOI:10.32604/cmc.2024.058926 - 03 January 2025

Abstract With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices generate massive data, but data security and privacy protection have become a serious challenge. Federated learning (FL) can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing. This review aims to deeply explore the combination of FL and the IoT, and analyze the application of federated learning in the IoT from More >

Bhanu Pratap Reddy Bhavanam, Prashanth Ragam^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 425-466, 2025, DOI:10.32604/cmes.2024.052355 - 17 December 2024

Abstract The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantly to the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development. This study provides valuable insights into optimizing wireless communication, paving the way for a more connected and productive future in the mining industry. The IoT revolution is advancing across industries, but harsh geometric environments, including open-pit mines, pose unique challenges for reliable communication. The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency… More >

Dayong Wang^1,*, Kamalrulnizam Bin Abu Bakar¹, Babangida Isyaku², Liping Lei³

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4465-4483, 2024, DOI:10.32604/cmc.2024.059616 - 19 December 2024

Abstract In recent years, task offloading and its scheduling optimization have emerged as widely discussed and significant topics. The multi-objective optimization problems inherent in this domain, particularly those related to resource allocation, have been extensively investigated. However, existing studies predominantly focus on matching suitable computational resources for task offloading requests, often overlooking the optimization of the task data transmission process. This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network, resulting in increased service times due to elevated network transmission latencies and idle computational resources.… More >

Monir Abdullah^*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4265-4283, 2024, DOI:10.32604/cmc.2024.059271 - 19 December 2024

Abstract The rapid growth and pervasive presence of the Internet of Things (IoT) have led to an unparalleled increase in IoT devices, thereby intensifying worries over IoT security. Deep learning (DL)-based intrusion detection (ID) has emerged as a vital method for protecting IoT environments. To rectify the deficiencies of current detection methodologies, we proposed and developed an IoT cyberattacks detection system (IoT-CDS) based on DL models for detecting bot attacks in IoT networks. The DL models—long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional neural network-LSTM (CNN-LSTM) were suggested to detect and classify IoT attacks.… More >

Chiranjeevi Karri^1,2,*, José J. M. Machado³, João Manuel R. S. Tavares¹, Deepak Kumar Jain⁴, Suresh Dannana⁵, Santosh Kumar Gottapu⁶, Amir H. Gandomi^7,8

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3617-3663, 2024, DOI:10.32604/cmc.2024.058461 - 19 December 2024

Abstract The rapid population growth, insecure lifestyle, wastage of natural resources, indiscipline behavior of human beings, urgency in the medical field, security of patient information, agricultural-related problems, and automation requirements in industries are the reasons for invention of technologies. Smart cities aim to address these challenges through the integration of technology, data, and innovative practices. Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living, improve resource efficiency, and create sustainable environments. This review presents five of the most critical technologies for smart and/or safe cities, addressing pertinent topics such as More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >