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  • Open Access

    ARTICLE

    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194 - 28 December 2022

    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and… More >

  • Open Access

    ARTICLE

    Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

    Manar Ahmed Hamza1,*, Masoud Alajmi2, Jaber S. Alzahrani3, Siwar Ben Haj Hassine4, Abdelwahed Motwakel1, Ishfaq Yaseen1

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3465-3479, 2022, DOI:10.32604/cmc.2022.026066 - 29 March 2022

    Abstract Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type More >

  • Open Access

    ARTICLE

    Automotive Lighting Systems Based on Luminance/Intensity Grids: A Proposal Based on Real-Time Monitoring and Control for Safer Driving

    Antonio Peña-García1,*, Huchang Liao2

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2373-2383, 2021, DOI:10.32604/cmc.2021.013151 - 28 December 2020

    Abstract The requirements for automotive lighting systems, especially the light patterns ensuring driver perception, are based on criteria related to the headlamps, rather than the light perceived by drivers and road users. Consequently, important factors such as pavement reflectance, driver age, or time of night, are largely ignored. Other factors such as presence of other vehicles, vehicle speed and weather conditions are considered by the Adaptive Driving Beam (ADB) and Adaptive Front-lighting System (AFS) respectively, though with no information regarding the visual perception of drivers and other road users. Evidently, it is simpler to simulate and… More >

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