Muntaham Inaam Hashmi¹, Muhammad Ayaz Khan², Khwaja Mansoor ul Hassan¹, Suliman A. Alsuhibany^3,*, Ainur Abduvalova⁴, Asfandyar Khan⁵

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071452 - 12 January 2026

Abstract Cyber-criminals target smart connected devices for spyware distribution and security breaches, but existing Internet of Things (IoT) security standards are insufficient. Major IoT industry players prioritize market share over security, leading to insecure smart products. Traditional host-based protection solutions are less effective due to limited resources. Overcoming these challenges and enhancing the security of IoT Devices requires a security design at the network level that uses lightweight cryptographic parameters. In order to handle control, administration, and security concerns in traditional networking, the Gateway Node offers a contemporary networking architecture. By managing all network-level computations and… More >

Fatima Asiri¹, Wajdan Al Malwi¹, Fahad Masood², Mohammed S. Alshehri³, Tamara Zhukabayeva⁴, Syed Aziz Shah⁵, Jawad Ahmad^6,*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3943-3960, 2025, DOI:10.32604/cmc.2025.066498 - 03 July 2025

Abstract Edge computing (EC) combined with the Internet of Things (IoT) provides a scalable and efficient solution for smart homes. The rapid proliferation of IoT devices poses real-time data processing and security challenges. EC has become a transformative paradigm for addressing these challenges, particularly in intrusion detection and anomaly mitigation. The widespread connectivity of IoT edge networks has exposed them to various security threats, necessitating robust strategies to detect malicious activities. This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory (BGT) and double deep Q-learning (DDQL). The proposed framework integrates BGT… More >

Murad Khan^1,*, Mohammed Faisal¹, Fahad R. Albogamy², Muhammad Diyan³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2027-2052, 2025, DOI:10.32604/cmes.2025.063764 - 30 May 2025

Abstract The rapid advancements in distributed generation technologies, the widespread adoption of distributed energy resources, and the integration of 5G technology have spurred sharing economy businesses within the electricity sector. Revolutionary technologies such as blockchain, 5G connectivity, and Internet of Things (IoT) devices have facilitated peer-to-peer distribution and real-time response to fluctuations in supply and demand. Nevertheless, sharing electricity within a smart community presents numerous challenges, including intricate design considerations, equitable allocation, and accurate forecasting due to the lack of well-organized temporal parameters. To address these challenges, this proposed system is focused on sharing extra electricity… More >

Sungbum Kim¹, Gwangsik Lee², Jian Song², Insoo Lee², Taeshik Shon^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 661-683, 2025, DOI:10.32604/cmc.2025.063687 - 26 March 2025

Abstract The smart home platform integrates with Internet of Things (IoT) devices, smartphones, and cloud servers, enabling seamless and convenient services. It gathers and manages extensive user data, including personal information, device operations, and patterns of user behavior. Such data plays an essential role in criminal investigations, highlighting the growing importance of specialized smart home forensics. Given the rapid advancement in smart home software and hardware technologies, many companies are introducing new devices and services that expand the market. Consequently, scalable and platform-specific forensic research is necessary to support efficient digital investigations across diverse smart home… More >

Zainatul Yushaniza Mohamed Yusoff¹, Mohamad Khairi Ishak^2,*, Lukman A. B. Rahim³, Mohd Shahrimie Mohd Asaari¹

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1669-1697, 2024, DOI:10.32604/csse.2024.056741 - 22 November 2024

Abstract The rapid adoption of Internet of Things (IoT) technologies has introduced significant security challenges across the physical, network, and application layers, particularly with the widespread use of the Message Queue Telemetry Transport (MQTT) protocol, which, while efficient in bandwidth consumption, lacks inherent security features, making it vulnerable to various cyber threats. This research addresses these challenges by presenting a secure, lightweight communication proxy that enhances the scalability and security of MQTT-based Internet of Things (IoT) networks. The proposed solution builds upon the Dang-Scheme, a mutual authentication protocol designed explicitly for resource-constrained environments and enhances it… More >

Brij B. Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2689-2706, 2024, DOI:10.32604/cmes.2024.050825 - 08 July 2024

Abstract This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion More >

Nazik Alturki¹, Raed Alharthi², Muhammad Umer^3,*, Oumaima Saidani¹, Amal Alshardan¹, Reemah M. Alhebshi⁴, Shtwai Alsubai⁵, Ali Kashif Bashir^6,7,8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3387-3415, 2024, DOI:10.32604/cmes.2023.044700 - 11 March 2024

Abstract The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets… More >

Oscar Peña-Cáceres^1,2,*, Henry Silva-Marchan³, Manuela Albert⁴, Miriam Gil¹

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1873-1891, 2023, DOI:10.32604/cmc.2023.043176 - 29 November 2023

Abstract The development of artificial intelligence (AI) and smart home technologies has driven the need for speech recognition-based solutions. This demand stems from the quest for more intuitive and natural interaction between users and smart devices in their homes. Speech recognition allows users to control devices and perform everyday actions through spoken commands, eliminating the need for physical interfaces or touch screens and enabling specific tasks such as turning on or off the light, heating, or lowering the blinds. The purpose of this study is to develop a speech-based classification model for recognizing human actions in… More >

Muhammad Irfan¹, Ch. Anwar Ul Hassan², Faisal Althobiani³, Nasir Ayub^4,*, Raja Jalees Ul Hussen Khan⁵, Emad Ismat Ghandourah⁶, Majid A. Almas⁷, Saleh Mohammed Ghonaim³, V. R. Shamji³, Saifur Rahman¹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1723-1740, 2023, DOI:10.32604/csse.2023.035141 - 09 February 2023

Abstract The smart grid has enabled users to control their home energy more effectively and efficiently. A home energy management system (HEM) is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy. Here, we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm (GWA) and Harmony Search Algorithms (HSA). Moreover, a fusion initiated on HSA and GWA operators is used to optimize energy intake. Furthermore, many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge. Hybridization has… More >

Abdulaziz Alorf^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 273-286, 2023, DOI:10.32604/csse.2023.035437 - 20 January 2023

Abstract Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peak-hours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. More >