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  • Open Access


    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

    Sheetal Sharma1,2, Kamali Gupta1, Deepali Gupta1, Shalli Rani1,*, Gaurav Dhiman3,4,5,6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997

    Abstract The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments. To address these concerns, various techniques and… More > Graphic Abstract

    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

  • Open Access


    QL-CBR Hybrid Approach for Adapting Context-Aware Services

    Somia Belaidouni1,2, Moeiz Miraoui3,4,*, Chakib Tadj1

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.024056

    Abstract A context-aware service in a smart environment aims to supply services according to user situational information, which changes dynamically. Most existing context-aware systems provide context-aware services based on supervised algorithms. Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trial-and-error interactions. They also have the ability to build excellent self-adaptive systems. In this study, we aim to incorporate reinforcement algorithms (Q-learning) into a context-aware system to provide relevant services based on a user’s dynamic context. To accelerate the convergence of reinforcement learning (RL) algorithms and provide the correct services in… More >

  • Open Access


    A Hybrid Modular Context-aware Services Adaptation for a Smart Living Room

    Moeiz Miraouia, Sherif El-Etribyb, Chakib Tadjc, Abdulbasit Zaid Abida

    Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 299-308, 2018, DOI:10.1080/10798587.2017.1281565

    Abstract Smart spaces have attracted considerable amount of interest over the past few years. The introduction of sensor networks, powerful electronics and communication infrastructures have helped a lot in the realization of smart homes. The main objective of smart homes is the automation of tasks that might be complex or tedious for inhabitants by distracting them from concentrating on setting and configuring home appliances. Such automation could improve comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Context awareness is a key enabling feature for development of smart homes. It allows the automation task… More >

  • Open Access


    State-Space Based Linear Modeling for Human Activity Recognition in Smart Space

    M. Humayun Kabir1, Keshav Thapa2, Jae-Young Yang2, Sung-HyunYang2

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 673-681, 2019, DOI:10.31209/2018.100000035

    Abstract Recognition of human activity is a key element for building intelligent and pervasive environments. Inhabitants interact with several objects and devices while performing any activity. Interactive objects and devices convey information that can be essential factors for activity recognition. Using embedded sensors with devices or objects, it is possible to get object-use sequencing data. This approach does not create discomfort to the user than wearable sensors and has no impact or issue in terms of user privacy than image sensors. In this paper, we propose a linear model for activity recognition based on the state-space method. The activities and sensor… More >

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