Managing Nonuniformities And Uncertainties In Vehicle-Oriented Sensor Data Over Next Generation Networks
2018 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS (PERCOM WORKSHOPS)(2018)
摘要
Detailed and accurate vehicle-oriented sensor data is considered fundamental for efficient vehicle-to-everything (V2X) communication applications, especially in the upcoming highly heterogeneous, brisk and agile 5G networking era. Information retrieval, transfer and manipulation in real-time offers a small margin for erratic behavior, regardless of its root cause. This paper presents a method for managing nonuniformities and uncertainties found on datasets, based on an elaborate Matrix Completion technique, with superior performance in three distinct cases of vehicle-related sensor data, collected under real driving conditions. Our approach appears capable of handling sensing and communication irregularities, minimizing at the same time the storage and transmission requirements of Multi-access Edge Computing applications.
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关键词
Laplacian Matrix Completion, V2X, MEC, Sensor Data
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