Assessing Environmental Influences on Flash Storage for Vehicle Computing: A Quantitative and Analytical Investigation
Year: 2025
Type: article
Abstract: As automotive technology advances, ensuring efficient and reliable vehicle storage systems becomes increasingly important to vehicle edges. Environmental factors, like extreme cold or intense heat, can greatly affect how well these critical components function. In this paper, we study the effects of different temperatures on flash-based vehicle storage systems, especially how these conditions impact data storage workloads, machine learning workloads, and vehicle edge computing by analyzing the read and write performance of car flash memory. Our approach combines environmental simulations, performance testing, and data analysis to examine how temperature changes affect the performance and reliability of vehicle storage. By testing conditions from standard room temperatures to extreme heat, this study explores how such environments influence the speed, dependability, and overall functionality of flash memory in automotive systems. The results show detailed relationships between temperature changes and the speed (throughput) and delay (latency) in flash storage, identifying areas where these systems may be vulnerable or where improvements could be made. Understanding these dynamics is essential for improving the durability and flexibility of automotive storage systems and vehicle edges in various environmental conditions. We have refined our conclusions to note that while our findings provide insights into temperature-related performance shifts, they represent one piece of a broader set of design considerations for engineers and manufacturers. Rather than offering definitive guidance for policymakers, our findings primarily help illustrate potential thermal vulnerabilities, informing ongoing work toward more robust and reliable vehicle storage systems. As the automotive industry continues to innovate, this study offers an initial foundation for future developments in vehicle storage technology.
Source: Algorithms
DOI: 10.3390/a18050269
Authors: Ying He, Donger Chen, Isabella Xu, Wang Feng, Qing Yang, Ted Tsao, Song Fu
Institution: University of North Texas
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
Funder: National Science Foundation
Awards: CyberTraining: Implementation: Small: Collaborative and Integrated Training on Connected and Autonomous Vehicles Cyber Infrastructure, EAGER: SaTC: Privacy-Preserving Convolutional Neural Network for Cooperative Perception in Vehicular Edge Systems, IUCRC Phase I University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT), Developing a High-Quality Academic Environment for Broadening Participation of Hispanic Students in Computing, IUCRC Planning Grant University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)
OpenAlex ID: W4410077554