TY - GEN
T1 - A Novel Device Based Edge-Cloud Architecture for Vehicular Edge Computing
AU - Raj, P. Herbert
AU - Kumar, P. Ravi
AU - Juwono, Filbert H.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - The rapid advancement of vehicular technology and the proliferation of connected vehicles have given rise to the demand for efficient and responsive computing solutions within the vehicular environment. Vehicular Edge Computing (VEC) emerges as a promising paradigm to meet these demands by leveraging the computational resources at the network edge. This paper presents an in-depth exploration of Vehicular Edge Computing Architecture (VECA), a novel framework designed to enhance the capabilities of connected vehicles through edge computing. VECA integrates edge computing nodes, vehicle-to-everything (V2X) communication technologies, and intelligent algorithms to create a dynamic and distributed computing environment within the vehicular network. This architecture addresses critical challenges related to latency, bandwidth, and scalability, enabling a wide range of applications, including real-time navigation, autonomous driving, traffic management, and infotainment services. Key components of VECA include edge servers strategically placed at roadside infrastructure and within vehicles, a robust communication infrastructure that supports low-latency data exchange, and machine learning algorithms for predictive analytics and decision-making. The architecture fosters efficient resource allocation, load balancing, and secure data management, ensuring optimal utilization of computational resources while preserving data privacy. This paper provides a comprehensive overview of VECA’s architecture, highlighting its technical specifications, benefits, and potential use cases. This research paper also discusses the integration of the novel Device Based Edge-Cloud Architecture into existing vehicular networks, along with challenges and future research directions. Through the adoption of this new architecture, connected vehicles can harness the power of edge computing to enhance safety, efficiency, and user experience, ushering in a new era of intelligent and responsive vehicular systems.
AB - The rapid advancement of vehicular technology and the proliferation of connected vehicles have given rise to the demand for efficient and responsive computing solutions within the vehicular environment. Vehicular Edge Computing (VEC) emerges as a promising paradigm to meet these demands by leveraging the computational resources at the network edge. This paper presents an in-depth exploration of Vehicular Edge Computing Architecture (VECA), a novel framework designed to enhance the capabilities of connected vehicles through edge computing. VECA integrates edge computing nodes, vehicle-to-everything (V2X) communication technologies, and intelligent algorithms to create a dynamic and distributed computing environment within the vehicular network. This architecture addresses critical challenges related to latency, bandwidth, and scalability, enabling a wide range of applications, including real-time navigation, autonomous driving, traffic management, and infotainment services. Key components of VECA include edge servers strategically placed at roadside infrastructure and within vehicles, a robust communication infrastructure that supports low-latency data exchange, and machine learning algorithms for predictive analytics and decision-making. The architecture fosters efficient resource allocation, load balancing, and secure data management, ensuring optimal utilization of computational resources while preserving data privacy. This paper provides a comprehensive overview of VECA’s architecture, highlighting its technical specifications, benefits, and potential use cases. This research paper also discusses the integration of the novel Device Based Edge-Cloud Architecture into existing vehicular networks, along with challenges and future research directions. Through the adoption of this new architecture, connected vehicles can harness the power of edge computing to enhance safety, efficiency, and user experience, ushering in a new era of intelligent and responsive vehicular systems.
KW - Connected devices
KW - Content caching
KW - MANET
KW - QoS and SDN
KW - RSU
KW - Task offloading
KW - V2I
KW - V2V
KW - V2X
KW - VANET
KW - VECA (Vehicular Edge Computing Architecture)
UR - http://www.scopus.com/inward/record.url?scp=85208169277&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-8043-3_175
DO - 10.1007/978-981-97-8043-3_175
M3 - Conference Proceeding
AN - SCOPUS:85208169277
SN - 9789819780426
T3 - Lecture Notes in Electrical Engineering
SP - 1144
EP - 1155
BT - Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications - ICDSMLA 2023
A2 - Kumar, Amit
A2 - Gunjan, Vinit Kumar
A2 - Senatore, Sabrina
A2 - Hu, Yu-Chen
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023
Y2 - 15 December 2023 through 16 December 2023
ER -