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  • Open Access

    ARTICLE

    Internet of Things Based Smart Irrigation System Using ESP WROOM 32

    Krish R. Mehta, K. Jayant Naidu, Madhav Baheti, Dev Parmar, A. Sharmila*

    Journal on Internet of Things, Vol.5, pp. 45-55, 2023, DOI:10.32604/jiot.2023.043102

    Abstract Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field… More >

  • Open Access

    REVIEW

    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

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road… More >

  • Open Access

    ARTICLE

    ChainApparel: A Trustworthy Blockchain and IoT-Based Traceability Framework for Apparel Industry 4.0

    Muhammad Shakeel Faridi1, Saqib Ali1,2,*, Guojun Wang2,*, Salman Afsar Awan1, Muhammad Zafar Iqbal3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.041929

    Abstract Trustworthiness and product traceability are essential factors in the apparel industry 4.0 for establishing successful business relationships among stakeholders such as customers, manufacturers, suppliers, and consumers. Each stakeholder has implemented different technology-based systems to record and track product transactions. However, these systems work in silos, and there is no intra-system communication, leading to a lack of complete supply chain traceability for all apparel stakeholders. Moreover, apparel stakeholders are reluctant to share their business information with business competitors; thus, they involve third-party auditors to ensure the quality of the final product. Furthermore, the apparel manufacturing industry faces challenges with counterfeit products,… More >

  • Open Access

    ARTICLE

    Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

    Abdennasser Dahmani1, Yamina Ammi2, Nadjem Bailek3,4,*, Alban Kuriqi5,6, Nadhir Al-Ansari7,*, Salah Hanini2, Ilhami Colak8, Laith Abualigah9,10,11,12,13,14, El-Sayed M. El-kenawy15

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2579-2594, 2023, DOI:10.32604/cmc.2023.040625

    Abstract Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic… More >

  • Open Access

    ARTICLE

    Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

    Medhat A. Tawfeek1,2, Nacim Yanes3,4, Leila Jamel5,*, Ghadah Aldehim5, Mahmood A. Mahmood1,6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2545-2564, 2023, DOI:10.32604/cmc.2023.042179

    Abstract The trend towards smart greenhouses stems from various factors, including a lack of agricultural land area owing to population concentration and housing construction on agricultural land, as well as water shortages. This study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research centers. The proposed model uses a one-dimensional convolutional neural network (CNN) deep learning model to control the growth of strategic crops, including cucumber, pepper, tomato, and bean. The proposed model uses the Internet of Things (IoT) to collect data on agricultural operations and then uses this data… More >

  • Open Access

    ARTICLE

    Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

    Muhammad Tahir1,2,*, Mingchu Li1,2, Irfan Khan1,2, Salman A. Al Qahtani3, Rubia Fatima4, Javed Ali Khan5, Muhammad Shahid Anwar6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2529-2544, 2023, DOI:10.32604/cmc.2023.042403

    Abstract Real-time health data monitoring is pivotal for bolstering road services’ safety, intelligence, and efficiency within the Internet of Health Things (IoHT) framework. Yet, delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems. We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this. This strategy is devised to streamline the data retrieval path, subsequently diminishing network strain. Crafting an adept cache processing scheme poses its own set of challenges, especially given the transient nature of monitoring data and the imperative for swift data transmission, intertwined with resource allocation tactics.… More >

  • Open Access

    ARTICLE

    Consortium Chain Consensus Vulnerability and Chain Generation Mechanism

    Rui Qiao, Shi Dong*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2505-2527, 2023, DOI:10.32604/cmc.2023.043476

    Abstract Effectively identifying and preventing the threat of Byzantine nodes to the security of distributed systems is a challenge in applying consortium chains. Therefore, this paper proposes a new consortium chain generation model, deeply analyzes the vulnerability of the consortium chain consensus based on the behavior of the nodes, and points out the effects of Byzantine node proportion and node state verification on the consensus process and system security. Furthermore, the normalized verification node aggregation index that represents the consensus ability of the consortium organization and the trust evaluation function of the verification node set is derived. When either of the… More >

  • Open Access

    ARTICLE

    BLECA: A Blockchain-Based Lightweight and Efficient Cross-Domain Authentication Scheme for Smart Parks

    Fengting Luo, Ruwei Huang*, Yuyue Chen

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1815-1835, 2023, DOI:10.32604/cmc.2023.041676

    Abstract Smart parks serve as integral components of smart cities, where they play a pivotal role in the process of urban modernization. The demand for cross-domain cooperation among smart devices from various parks has witnessed a significant increase. To ensure secure communication, device identities must undergo authentication. The existing cross-domain authentication schemes face issues such as complex authentication paths and high certificate management costs for devices, making it impractical for resource-constrained devices. This paper proposes a blockchain-based lightweight and efficient cross-domain authentication protocol for smart parks, which simplifies the authentication interaction and requires every device to maintain only one certificate. To… More >

  • Open Access

    ARTICLE

    Identification of tumor-suppressor genes in lung squamous cell carcinoma through integrated bioinformatics analyses

    HENG LI1,#, YOUMING LEI3,#, GAOFENG LI1, YUNCHAO HUANG2,*

    Oncology Research, Vol.32, No.1, pp. 187-197, 2024, DOI:10.32604/or.2023.030656

    Abstract Lung cancer is a prevalent malignancy, and fatalities of the disease exceed 400,000 cases worldwide. Lung squamous cell carcinoma (LUSC) has been recognized as the most common pathological form of lung cancer. The comprehensive understanding of molecular features related to LUSC progression has great significance in LUSC prognosis assessment and clinical management. In this study, we aim to identify a panel of signature genes closely associated with LUSC, which can provide novel insights into the progression of LUSC. Gene expression profiles were retrieved from public resources including gene expression omnibus (GEO) and the cancer genome atlas (TCGA) database. Differentially expressed… More >

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