Keywords: internet of vehicles (IoV), peripheral computing, intelligent transport systems, communication reliability, data transmission latency, cluster interaction, computing resource allocation, openStack, cloud services, on-board sensors
Efficient collaborative peripheral computing for a transport network using a clustering service
UDC 004.75
DOI: 10.26102/2310-6018/2025.50.3.005
Modern Internet of Vehicles (IoV) applications place high demands on reliability and minimal response time in dynamic traffic conditions. However, high-speed vehicles and complex infrastructure such as intersections can lead to loss of communication and increased delays in data transmission and processing. The paper proposes an innovative framework for cluster interaction of vehicles based on peripheral computing (CCVEC), implemented on the OpenStack platform. The development is focused on ensuring stable communication and rational allocation of computing resources in intelligent transport systems. The conducted testing covered various traffic scenarios, including high-density traffic areas. The results showed that the proposed solution supports stable communication between onboard sensors and cloud services. Under optimal conditions, the average latency was about 390 ms, and the throughput reached 30 kB/s. The platform has demonstrated high performance and efficient memory usage when allocating resources. Thus, the CCVEC framework is able to reduce delays, increase connection reliability and efficiently use local resources, which makes it promising for implementation in IoV-based systems and peripheral computing.
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Keywords: internet of vehicles (IoV), peripheral computing, intelligent transport systems, communication reliability, data transmission latency, cluster interaction, computing resource allocation, openStack, cloud services, on-board sensors
For citation: Komarenko Y.A., Krepyshev D.A. Efficient collaborative peripheral computing for a transport network using a clustering service. Modeling, Optimization and Information Technology. 2025;13(3). URL: https://moitvivt.ru/ru/journal/pdf?id=1925 DOI: 10.26102/2310-6018/2025.50.3.005 (In Russ).
Received 06.05.2025
Revised 06.06.2025
Accepted 25.06.2025