Traffic prediction for diverse edge IoT data using graph network
Authors: 
Tao Shen, Lu Zhang, Renkang Geng, Shuai Li, Bin SunAuthors Info & Claims
Abstract
More researchers are proposing artificial intelligence algorithms for Internet of Things (IoT) devices and applying them to themes such as smart cities and smart transportation. In recent years, relevant research has mainly focused on data processing and algorithm modeling, and most have shown good prediction results. However, many algorithmic models often adjust parameters for the corresponding datasets, so the robustness of the models is weak. When different types of data face other model parameters, the prediction performance often varies a lot. Thus, this work starts from the perspective of data processing and algorithm models. Taking traffic data as an example, we first propose a new data processing method that processes traffic data with different attributes and characteristics into a dataset that is more common for most models. Then we will compare different types of datasets from the perspective of multiple model parameters, and further analyze the precautions and changing trends of different traffic data in machine learning. Finally, different types of data and ranges of model parameters are explored, together with possible reasons for fluctuations in forecast results when data parameters change.
References
[1]
Yan S, Shao H, Min Z, Peng J, Cai B, Liu B (2023) Fgdae: a new machinery anomaly detection method towards complex operating conditions. Reliab Eng Syst Saf 236:109319., https://www.sciencedirect.com/science/article/pii/S0951832023002338
[2]
Chen M, Shao H, Dou H, Li W, and Liu B Data augmentation and intelligent fault diagnosis of planetary gearbox using ILoFGAN under extremely limited samples IEEE Trans Reliab 2023 72 3 1029-1037
[3]
Chen X, Shao H, Xiao Y, Yan S, Cai B, Liu B (2023) Collaborative fault diagnosis of rotating machinery via dual adversarial guided unsupervised multi-domain adaptation network. Mech Syst Signal Process 198:110427., https://www.sciencedirect.com/science/article/pii/S0888327023003357
[4]
Vermesan O, Friess P (2013) Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers
[5]
Huang J, Lv B, Wu Y, Chen Y, and Shen X Dynamic admission control and resource allocation for mobile edge computing enabled small cell network IEEE Trans Veh Technol 2021 71 2 1964-1973
[6]
Qureshi KN and Abdullah AH A survey on intelligent transportation systems Middle-East J Sci Res 2013 15 5 629-642
[7]
Shao H, Li W, Cai B, Wan J, Xiao Y, and Yan S Dual-threshold attention-guided GAN and limited infrared thermal images for rotating machinery fault diagnosis under speed fluctuation IEEE Trans Ind Inform 2023 19 9 9933-9942
[8]
Huang J, Gao H, Wan S, and Chen Y AoI-aware energy control and computation offloading for industrial IoT Future Gener Comput Syst 2023 139 29-37
[9]
Jamson AH, Merat N, Carsten OM, and Lai FC Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions Transp Res Part C Emerg Technol 2013 30 116-125
[10]
Darwish TS and Bakar KA Fog based intelligent transportation big data analytics in the internet of vehicles environment: Motivations, architecture, challenges, and critical issues IEEE Access 2018 6 15679-15701
[11]
Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, and Sheth AP Machine learning for Internet of Things data analysis: a survey Digit Commun Netw 2018 4 3 161-175
[12]
Chen Y, Zhao J, Zhou X, et al. A distributed game theoretical approach for credibility-guaranteed multimedia data offloading in MEC Inf Sci 2023
[13]
Li D, Deng L, and Cai Z Intelligent vehicle network system and smart city management based on genetic algorithms and image perception Mech Syst Signal Process 2020 141 106623
[14]
Yang F, Wang S, Li J, Liu Z, and Sun Q An overview of internet of vehicles China Commun 2014 11 10 1-15
[15]
Chen C, Li H, Li H, Fu R, Liu Y, Wan S (2022) Efficiency and fairness oriented dynamic task offloading in internet of vehicles. IEEE Trans Green Commun 6(3):1481-93.
[16]
Huang J, Wan J, Lv B, Ye Q, et al. Joint computation offloading and resource allocation for edge-cloud collaboration in internet of vehicles via deep reinforcement learning IEEE Syst J 2023 17 2 2500-2511
[17]
Chen C, Zeng Y, Li H, Liu Y, Wan S (2023) A multihop task offloading decision model in MEC-enabled internet of vehicles. IEEE Internet Things J 10(4):3215-30.
[18]
Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y (2003) Review of road traffic control strategies. Proceedings of the IEEE 91(12):2043-67.
[19]
Chen Y, Zhao J, Hu J, et al. Distributed task offloading and resource purchasing in NOMA-enabled mobile edge computing: Hierarchical game theoretical approaches ACM Trans Embed Comput Syst 2023
[20]
Srinivasan D, Choy MC, and Cheu RL Neural networks for real-time traffic signal control IEEE Trans Intell Transp Syst 2006 7 3 261-272
[21]
Wen L, Kenworthy J, Guo X, and Marinova D Solving traffic congestion through street renaissance: A perspective from dense Asian cities Urban Sci 2019 3 1 18
[22]
Nicopolitidis P, Obaidat MS, Papadimitriou GI, and Pomportsis AS Wireless Networks 2003 John Wiley & Sons
[23]
Chen Y, Zhao J, Wu Y et al (2022) Qoe-aware decentralized task offloading and resource allocation for end-edge-cloud systems: a game-theoretical approach. IEEE Trans Mob Comput.
[24]
Schlomer GL, Bauman S, and Card NA Best practices for missing data management in counseling psychology J Couns Psychol 2010 57 1 1
[25]
Ozbayoglu M, Kucukayan G, Dogdu E (2016) A real-time autonomous highway accident detection model based on big data processing and computational intelligence. In: 2016 IEEE International Conference on Big Data (Big Data), IEEE, pp 1807–1813
[26]
Wang L, etc XD, (2023) A review of urban air mobility-enabled intelligent transportation systems: Mechanisms, applications and challenges. J Syst Archit 141:102902., www.sciencedirect.com/science/article/pii/S1383762123000814
[27]
Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, and Monfardini G The graph neural network model IEEE Trans Neural Netw 2008 20 1 61-80
[28]
Chen Y, Hu J, Zhao J, Min G (2023) Qos-aware computation offloading in leo satellite edge computing for IoT: a game-theoretical approach. Chin J Electron
[29]
Wu Z, Pan S, Chen F, Long G, Zhang C, and Philip SY A comprehensive survey on graph neural networks IEEE Trans Neural Netw Learn Syst 2020 32 1 4-24
[30]
Zhang S, Tong H, Xu J, and Maciejewski R Graph convolutional networks: a comprehensive review Comput Soc Netw 2019 6 1 1-23
[31]
Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, and Bengio Y Graph attention networks Stat 2017 1050 20
[32]
Hamilton WL, Ying R, Leskovec J (2017) Inductive Representation Learning on Large Graphs. arXiv:1706.02216
[33]
Atluri G, Karpatne A, and Kumar V Spatio-temporal data mining: a survey of problems and methods ACM Comput Surv CSUR 2018 51 4 1-41
[34]
Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. ArXiv Prepr arXiv:1409.2329
[35]
Liu F, Huang J, and Wang X Joint task offloading and resource allocation for device-edge-cloud collaboration with subtask dependencies IEEE Trans Cloud Comput 2023 11 3 3027-3039
[36]
Zhao L, Zhang E, Wan S, Hawbani A, Al-Dubai AY, Min G, Zomaya AY Meson: a mobility-aware dependent task offloading scheme for urban vehicular edge computing.
[37]
Sak H, Senior AW, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Interspeech. p. 338-342
[38]
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. ArXiv Prepr arXiv:1406.1078
[39]
Saad W, Han Z, Debbah M, Hjorungnes A, and Basar T Coalitional game theory for communication networks IEEE Signal Process Mag 2009 26 5 77-97
[40]
Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks. ArXiv Prepr arXiv:1511.05493
[41]
Ang LM and Seng KP Big sensor data applications in urban environments Big Data Res 2016 4 1-12
[42]
Chen PH, Lin CJ, Schölkopf B (2005) A tutorial on -support vector machines. Appl Stoch Models Bus Ind 21(2):111–136
[43]
Miettinen P (2009) Matrix decomposition methods for data mining: computational complexity and algorithms. University of Helsinki, Helsinki. 170 p. (Series of publications / Department of Computer Science, University of HelsinkiA; 2009-4)
[44]
Yu H, Rao NS, Dhillon IS (2016) Temporal regularized matrix factorization for high-dimensional time series prediction. Neural Information Processing Systems
[45]
Merris R Laplacian matrices of graphs: a survey Linear Algebra Appl 1994 197 143-176
[46]
Yang JM, Peng ZR, and Lin L Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization Transp Res Part C Emerg Technol 2021 129 103228
Recommendations
Anomaly traffic detection in IoT security using graph neural networks
AbstractThe number of Internet of Things (IoT) devices is expanding quickly as IoT gradually spreads to all aspects of life. At the same time, IoT devices have emerged as a new attack medium for attack groups, and IoT security becomes an ...
IoT devices discovery and identification using network traffic data: poster
WiSec '19: Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile NetworksThe Internet of Things (IoT) has been erupting world widely over the decade. However, the security and privacy leakage issues from IoT devices are surfaced to a major flaw for IoT device operators. An attacker may use network traffic data to identify ...
The rise of traffic classification in IoT networks: A survey
AbstractWith the proliferation of the Internet of Things (IoT), the integration and communication of various objects have become a prevalent practice. The huge growth of IoT devices and different characteristics in the IoT traffic patterns ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© The Author(s) 2024.
Publisher
Hindawi Limited
London, United Kingdom
Publication History
Published: 08 April 2024
Accepted: 05 November 2023
Received: 13 December 2022
Author Tags
Qualifiers
- Research-article
Funding Sources
- Shandong Key Technology R&D Program
- Natural Science Foundation of Shandong, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 25 Nov 2024
Other Metrics
Citations
View Options
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