Research Summary: Enhancing Localization, Selection, and Processing of Data in Vehicular Cyber-Physical Systems
Pages 1 - 5
Abstract
Increasing amounts of data are sensed at the edge of the Edge-to-Cloud (E2C) continuum, enabling the rapid development of data-driven applications based on, e.g., Machine Learning. This is especially true for Vehicular Cyber-Physical Systems (VCPSs), networks of connected vehicles equipped with high-bandwidth sensors, where Big Data originating on the vehicles is crucial for the advancement of autonomous drive, developing new cars, and more. Limited bandwidth and storage mean that moving this vehicular Big Data from the edge to central processing increasingly poses challenges. In this work, we present our research on how to alleviate these through efficiently localizing data on the edge, selecting relevant data in a data stream, and distributing the processing of data in a VCPS.
References
[1]
Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael J Fer Andez-Moctezuma, Reuven Lax, Sam Mcveety, Daniel Mills, Frances Perry, Eric Schmidt, and Sam Whittle Google. 2015. The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. VLDB 8, 12 (2015), 1792--1803.
[2]
Furqan Alam, Rashid Mehmood, and Iyad Katib. 2017. D2TFRS: An object recognition method for autonomous vehicles based on RGB and spatial values of pixels. In International Conference on Smart Cities, Infrastructure, Technologies and Applications. Springer, 155--168.
[3]
Apache. 2020. Storm. Retrieved November 12, 2020 from http://storm.apache.org/
[4]
Girija V Attigeri, Manohara Pai MM, Radhika M Pai, and Aparna Nayak. 2015. Stock market prediction: A big data approach. In TENCON 2015-2015 IEEE Region 10 Conference. IEEE, 1--5.
[5]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877--1901.
[6]
Paris Carbone, Asterios Katsifodimos, Stephan Ewen, Volker Markl, Seif Haridi, and Kostas Tzoumas. 2015. Apache flink: Stream and batch processing in a single engine. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering 36, 4 (2015).
[7]
François Chollet et al. 2015. Keras. https://keras.io.
[8]
Sonal Doomra, Naman Kohli, and Shounak Athavale. 2020. Turn Signal Prediction: A Federated Learning Case Study. arXiv preprint arXiv:2012.12401 (2020).
[9]
Romaric Duvignau, Vincenzo Gulisano, Marina Papatriantafilou, and Vladimir Savic. 2019. Streaming Piecewise Linear Approximation for Efficient Data Management in Edge Computing. In 34th ACM/SIGAPP Symposium On Applied Computing SAC'19. 593--596.
[10]
Romaric Duvignau, Bastian Havers, Vincenzo Gulisano, and Marina Papatriantafilou. 2019. Querying large vehicular networks: How to balance on-board workload and queries response time?. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC). IEEE, 2604--2611.
[11]
Romaric Duvignau, Bastian Havers, Vincenzo Gulisano, and Marina Papatriantafilou. 2021. Time-and Computation-Efficient Data Localization at Vehicular Networks' Edge. IEEE Access 9 (2021), 137714--137732.
[12]
Di Feng, Christian Haase-Schütz, Lars Rosenbaum, Heinz Hertlein, Claudius Glaeser, Fabian Timm, Werner Wiesbeck, and Klaus Dietmayer. 2020. Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges. IEEE Transactions on Intelligent Transportation Systems 22, 3 (2020), 1341--1360.
[13]
Boris Glavic et al. 2021. Data provenance. Foundations and Trends® in Databases 9, 3-4 (2021), 209--441.
[14]
Boris Glavic, Kyumars Sheykh Esmaili, Peter Michael Fischer, and Nesime Tatbul. 2013. Ariadne: Managing Fine-Grained Provenance on Data Streams. In Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (DEBS '13). Association for Computing Machinery, New York, NY, USA, 39--50.
[15]
Florian Grützmacher, Benjamin Beichler, Albert Hein, Thomas Kirste, and Christian Haubelt. 2018. Time and Memory Efficient Online Piecewise Linear Approximation of Sensor Signals. Sensors 18, 6 (2018), 1672.
[16]
Vincenzo Gulisano, Dimitris Palyvos-Giannas, Bastian Havers, and Marina Papatriantafilou. 2020. The role of event-time order in data streaming analysis. In Proceedings of the 14th ACM International Conference on Distributed and Event-based Systems. 214--217.
[17]
Vincenzo Gulisano, Marina Papatriantafilou, Zhuoer Chen, Eduard Hryha, and Lars Nyborg. 2022. Towards data-driven additive manufacturing processes. In Proceedings of the 23rd International Middleware Conference Industrial Track. 43--49.
[18]
Bastian Havers, Romaric Duvignau, Hannaneh Najdataei, Vincenzo Gulisano, Ashok Chaitanya Koppisetty, and Marina Papatriantafilou. 2019. Driven: a framework for efficient data retrieval and clustering in vehicular networks. In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 1850--1861.
[19]
Bastian Havers, Romaric Duvignau, Hannaneh Najdataei, Vincenzo Gulisano, Marina Papatriantafilou, and Ashok Chaitanya Koppisetty. 2020. DRIVEN: A framework for efficient Data Retrieval and clustering in Vehicular Networks. Future Generation Computer Systems 107 (2020), 1--17.
[20]
Bastian Havers, Marina Papatriantafilou, Ashok Koppisetty, and Vincenzo Gulisano. 2022. Proposing a framework for evaluating learning strategies in vehicular CPSs. In Proceedings of the 23rd International Middleware Conference Industrial Track. 22--28.
[21]
Kenneth M Jensen, Ilmar F Santos, and Harry JP Corstens. 2023. Estimation of brake pad wear and remaining useful life from fused sensor system, statistical data processing, and passenger car longitudinal dynamics. Wear (2023), 205220.
[22]
Wei Jiang, Bin Han, Mohammad Asif Habibi, and Hans Dieter Schotten. 2021. The road towards 6G: A comprehensive survey. IEEE Open journal of the Communications Society 2 (2021), 334--366.
[23]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012).
[24]
Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flötteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner. 2018. Microscopic Traffic Simulation using SUMO, In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE Intelligent Transportation Systems Conference (ITSC). https://elib.dlr.de/124092/
[25]
Jojn C. McCallum. 2022. Our World in Data - Historical cost of computer memory and storage, https://ourworldindata.org/grapher/historical-cost-of-computer-memory-and-storage
[26]
D. Milojicic. 2020. The Edge-to-Cloud Continuum. Computer 53, 11 (2020), 16--25.
[27]
Hannaneh Najdataei, Yiannis Nikolakopoulos, Vincenzo Gulisano, and Marina Papatriantafilou. 2018. Continuous and Parallel LiDAR Point-Cloud Clustering. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 671--684.
[28]
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli. 2016. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles. IEEE Transactions on Intelligent Vehicles 1, 1 (2016), 33--55.
[29]
Dimitris Palyvos-Giannas, Vincenzo Gulisano, and Marina Papatriantafilou. 2018. GeneaLog: Fine-Grained Data Streaming Provenance at the Edge. In Proceedings of the 19th International Middleware Conference. ACM, 227--238.
[30]
Dimitris Palyvos-Giannas, Bastian Havers, Marina Papatriantafilou, and Vincenzo Gulisano. 2020. Ananke: a streaming framework for live forward provenance. Proceedings of the VLDB Endowment 14, 3 (2020), 391--403.
[31]
Dimitris Palyvos-Giannas, Katerina Tzompanaki, Marina Papatriantafilou, and Vincenzo Gulisano. 2023. Erebus: Explaining the outputs of data streaming queries. In Very Large Data Base, Vol. 16. 230--242.
[32]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 8024--8035. http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
[33]
Jason Posner, Lewis Tseng, Moayad Aloqaily, and Yaser Jararweh. 2021. Federated learning in vehicular networks: opportunities and solutions. IEEE Network 35, 2 (2021), 152--159.
[34]
A. Rasouli, I. Kotseruba, and J. K. Tsotsos. 2018. Understanding Pedestrian Behavior in Complex Traffic Scenes. IEEE Transactions on Intelligent Vehicles 3, 1 (2018), 61--70.
[35]
Petroc Taylor. 2023. Statista - Cellular Network Average Speed in US 2016-2023. https://www.statista.com/statistics/995096/average-cellular-network-speed-in-the-us/
[36]
Petroc Taylor. 2023. Statista - Data Growth Worldwide. https://www.statista.com/statistics/871513/worldwide-data-created/
[37]
Silke Wagner and Dorothea Wagner. 2007. Comparing clusterings: an overview. Universität Karlsruhe, Fakultät für Informatik Karlsruhe.
[38]
Ingo Wald and Vlastimil Havran. 2006. On building fast kd-trees for ray tracing, and on doing that in O (N log N). In 2006 IEEE Symposium on Interactive Ray Tracing. IEEE, 61--69.
[39]
Qing Xie, Chaoyi Pang, Xiaofang Zhou, Xiangliang Zhang, and Ke Deng. 2014. Maximum error-bounded Piecewise Linear Representation for online stream approximation. The VLDB Journal 23, 6 (2014), 915--937.
[40]
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, and Kazuya Takeda. 2020. A survey of autonomous driving: Common practices and emerging technologies. IEEE access 8 (2020), 58443--58469.
Index Terms
- Research Summary: Enhancing Localization, Selection, and Processing of Data in Vehicular Cyber-Physical Systems
Recommendations
A Survey on Platoon-Based Vehicular Cyber-Physical Systems
Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another vehicle and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, ...
Comments
Information & Contributors
Information
Published In
June 2024
95 pages
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Vinnova
- HORIZON EUROPE Marie Sklodowska-Curie Actions
- Swedish Energy Agency (SESBC)
- Chalmers tekniska högskola
- Wallenberg AI, Autonomous Systems and Software Program and Wallenberg Initiative Materials for Sustainability
Conference
Acceptance Rates
Overall Acceptance Rate 3 of 4 submissions, 75%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 80Total Downloads
- Downloads (Last 12 months)80
- Downloads (Last 6 weeks)16
Reflects downloads up to 25 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in