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  • Open Access

    ARTICLE

    Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

    Fang Xu1,2,3, Songhao Jiang1,2, Yi Ma1,2,3,*, Manzoor Ahmed1,3,*, Zenggang Xiong1,2,3, Yuanlin Lyu1,2

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1095-1113, 2024, DOI:10.32604/cmc.2023.046577

    Abstract Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections… More >

  • Open Access

    ARTICLE

    Enhancing IoT Security: Quantum-Level Resilience against Threats

    Hosam Alhakami*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 329-356, 2024, DOI:10.32604/cmc.2023.043439

    Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks, with a focus on developing… More >

  • Open Access

    ARTICLE

    Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

    Ming Luo1, Huili Dou2, Ning Zheng3,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067

    Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables. In terms… More >

  • Open Access

    ARTICLE

    A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

    Yurong Luo, Wei You*, Chao Shang, Xiongpeng Ren, Jin Cao, Hui Li

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2237-2260, 2024, DOI:10.32604/cmes.2023.045679

    Abstract The dynamic landscape of the Internet of Things (IoT) is set to revolutionize the pace of interaction among entities, ushering in a proliferation of applications characterized by heightened quality and diversity. Among the pivotal applications within the realm of IoT, as a significant example, the Smart Grid (SG) evolves into intricate networks of energy deployment marked by data integration. This evolution concurrently entails data interchange with other IoT entities. However, there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem. In this paper, we introduce a hierarchical secure data-sharing platform empowered… More >

  • Open Access

    ARTICLE

    IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

    Dinesh Mavaluru1,*, Chettupally Anil Carie2, Ahmed I. Alutaibi3, Satish Anamalamudi2, Bayapa Reddy Narapureddy4, Murali Krishna Enduri2, Md Ezaz Ahmed1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1487-1503, 2024, DOI:10.32604/cmes.2023.045277

    Abstract In this paper, we present a comprehensive system model for Industrial Internet of Things (IIoT) networks empowered by Non-Orthogonal Multiple Access (NOMA) and Mobile Edge Computing (MEC) technologies. The network comprises essential components such as base stations, edge servers, and numerous IIoT devices characterized by limited energy and computing capacities. The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption. The system operates in discrete time slots and employs a quasi-static approach, with a specific focus on the complexities of task partitioning and the management… More >

  • Open Access

    ARTICLE

    Hash Table Assisted Efficient File Level De-Duplication Scheme in SD-IoV Assisted Sensing Devices

    Ghawar Said1, Ata Ullah2, Anwar Ghani1,*, Muhammad Azeem1, Khalid Yahya3, Muhammad Bilal4, Sayed Chhattan Shah5,*

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 83-99, 2023, DOI:10.32604/iasc.2023.036079

    Abstract The Internet of Things (IoT) and cloud technologies have encouraged massive data storage at central repositories. Software-defined networks (SDN) support the processing of data and restrict the transmission of duplicate values. It is necessary to use a data de-duplication mechanism to reduce communication costs and storage overhead. Existing State of the art schemes suffer from computational overhead due to deterministic or random tree-based tags generation which further increases as the file size grows. This paper presents an efficient file-level de-duplication scheme (EFDS) where the cost of creating tags is reduced by employing a hash table with key-value pair for each… More >

  • Open Access

    ARTICLE

    The Effect of Soil Enzymes and Polysaccharides Secreted by the Roots of Salvia miltiorrhiza Bunge under Drought, High Temperature, and Nitrogen and Phosphorus Deficits

    Yong Qin1,2, Xiaoyu Li1,2, Yanhong Wu1,2, Hai Wang3, Guiqi Han1,2,3, Zhuyun Yan1,2,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 119-135, 2024, DOI:10.32604/phyton.2023.046075

    Abstract Root exudates serve as crucial mediators for information exchange between plants and soil, and are an important evolutionary mechanism for plants’ adaptation to environmental changes. In this study, 15 different abiotic stress models were established using various stress factors, including drought (D), high temperature (T), nitrogen deficiency (N), phosphorus deficiency (P), and their combinations. We investigated their effects on the seedling growth of Salvia miltiorrhiza Bunge and the activities of Solid-Urease (S-UE), Solid-Nitrite Reductase (S-NiR), Solid-Nitrate Reductase (S-NR), Solid-Phosphotransferase (S-PT), and Solid-Catalase (S-CAT), as well as the contents of polysaccharides in the culture medium. The results showed that the growth… More >

  • Open Access

    ARTICLE

    Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

    SangSeok Lee1, HaeWon Moon1, Lee Sael1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 319-336, 2024, DOI:10.32604/cmes.2023.031150

    Abstract How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data? Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors. For example, air quality tensor data consists of multiple sensory values gathered from wide locations for a long time. Such data, accumulated over time, is redundant and consumes a lot of memory in its raw form. We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks. To… More > Graphic Abstract

    Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

  • Open Access

    ARTICLE

    Integration of Digital Twins and Artificial Intelligence for Classifying Cardiac Ischemia

    Mohamed Ammar1,*, Hamed Al-Raweshidy2,*

    Journal on Artificial Intelligence, Vol.5, pp. 195-218, 2023, DOI:10.32604/jai.2023.045199

    Abstract Despite advances in intelligent medical care, difficulties remain. Due to its complicated governance, designing, planning, improving, and managing the cardiac system remains difficult. Oversight, including intelligent monitoring, feedback systems, and management practises, is unsuccessful. Current platforms cannot deliver lifelong personal health management services. Insufficient accuracy in patient crisis warning programmes. No frequent, direct interaction between healthcare workers and patients is visible. Physical medical systems and intelligent information systems are not integrated. This study introduces the Advanced Cardiac Twin (ACT) model integrated with Artificial Neural Network (ANN) to handle real-time monitoring, decision-making, and crisis prediction. THINGSPEAK is used to create an… More >

  • Open Access

    ARTICLE

    Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

    Abdulwahab Ali Almazroi1,*, Nasir Ayub2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3921-3942, 2023, DOI:10.32604/cmc.2023.046602

    Abstract Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimization methods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly-Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses… More >

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