Managing nonuniformities and uncertainties in vehicle-oriented sensor data over next generation networks
Stavros Nousias, Christos Tseliosl, Dimitris uitzasl, Olivier Orfila, Samantha L. Jamson, Pablo Mejuto, Dimitrios Amaxilatis, Orestis Akrivopoulos, Ioannis Chatzigiannakis, Aris S. Lalosl, Konstantinos Moustakasl · 2018
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.