Duan Xue^1,2, Yan Guo^1,*, Ning Li¹, Xiaoxiang Song¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 609-631, 2023, DOI:10.32604/cmc.2023.035177

Abstract The traditional multi-access edge computing (MEC) capacity is overwhelmed by the increasing demand for vehicles, leading to acute degradation in task offloading performance. There is a tremendous number of resource-rich and idle mobile connected vehicles (CVs) in the traffic network, and vehicles are created as opportunistic ad-hoc edge clouds to alleviate the resource limitation of MEC by providing opportunistic computing services. On this basis, a novel scalable system framework is proposed in this paper for computation task offloading in opportunistic CV-assisted MEC. In this framework, opportunistic ad-hoc edge cloud and fixed edge cloud cooperate to form a novel hybrid cloud.… More >

P. Thamizhazhagan^1,*, M. Sujatha², S. Umadevi³, K. Priyadarshini⁴, Velmurugan Subbiah Parvathy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3333-3347, 2022, DOI:10.32604/cmc.2022.020197

Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More >

Jaekyu Lee^1,2, Sangyub Lee¹, Hyosub Choi¹, Hyeonjoong Cho^2,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 2709-2727, 2021, DOI:10.32604/cmc.2021.015174

Abstract In this study, we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles. We designed two software modules: The first to derive the Pearson correlation coefficients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data. In particular, we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority. We also analyzed seasonal fuel efficiency (four seasons) and mileage of vehicles, and identified rapid acceleration, rapid deceleration, sudden stopping (harsh braking), quick starting, sudden left turn, sudden right… More >

Dae-Young Kim¹, Seokhoon Kim^2,*

CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 375-387, 2019, DOI:10.32604/cmc.2019.06077

Abstract Communication is important for providing intelligent services in connected vehicles. Vehicles must be able to communicate with different places and exchange information while driving. For service operation, connected vehicles frequently attempt to download large amounts of data. They can request data downloading to a road side unit (RSU), which provides infrastructure for connected vehicles. The RSU is a data bottleneck in a transportation system because data traffic is concentrated on the RSU. Therefore, it is not appropriate for a connected vehicle to always attempt a high speed download from the RSU. If the mobile network between a connected vehicle and… More >