The DOI matches its Crossref record; 50 of the 51 references it cites were found in a registry or an index.
Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading
Abstract
Augmented Intelligence of Things (AIoT) combines augmented intelligence algorithms with the massive data collected by IoT devices, enabling more advanced decision-making. The typical application of AIoT is edge computing (EC), which provides computational and storage resources at the edge to support vehicle decision making for computation tasks. With the development of EC, task offloading has become a hopeful paradigm for assisting the time-sensitive tasks of resource-limited vehicles, such as emergency rescue vehicles by deploying at roadside units (RSUs). However, the effectiveness of task offloading for emergency vehicles is hindered by the timeliness of trajectory data and the concern regarding vehicle location. Therefore, this study introduces a secure task offloading scheme relying on the real-time trajectory prediction, named STODRL. First, this study proposes a temporal differential privacy method to disturb vehicular location information to avoid suffering from malicious stealing. Second, a vehicular trajectory prediction method using the temporal convolutional network (TCN) is designed to improve the task offloading precision by offering supplemental trajectory information. Moreover, the scheme employs a reinforcement learning method to offload computational requests effectively and avoid dimensional disasters. Simulated results validate that the STODRL outperforms the existing methods, significantly reducing task completion delays and ensuring the security of location information.
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“Memory replay strategies effectively preserve historical distributions but face practical constraints in storage and privacy, particularly in real-world deployments where sensitive data must be protected51,52.”
Citations
Cites
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
Savitzky, A., & Golay, M. J. E. (1964) · Analytical Chemistry · doi:10.1021/ac60214a047 · Cited by 21,904
Convolutional Social Pooling for Vehicle Trajectory Prediction
Deo, N., & Trivedi, M. M. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00196 · Cited by 1,085
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
Edge Computing for the Internet of Things: A Case Study
Premsankar, G., Francesco, M. D., & Taleb, T. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2805263 · Cited by 720
Vehicle Trajectory Prediction Using LSTMs With Spatial–Temporal Attention Mechanisms
Lin, L., Li, W., Bi, H., & Qin, L. (2021) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2021.3049404 · Cited by 285
Making NGSIM Data Usable for Studies on Traffic Flow Theory
Montanino, M., & Punzo, V. (2013) · Transportation Research Record Journal of the Transportation Research Board · doi:10.3141/2390-11 · Cited by 197
PiP: Planning-Informed Trajectory Prediction for Autonomous Driving
Song, H., Ding, W., Chen, Y., Shen, S., Wang, M. Y., & Chen, Q. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58589-1_36 · Cited by 188
AI augmented Edge and Fog computing: Trends and challenges
Tuli, S., Mirhakimi, F., Pallewatta, S., Zawad, S., Casale, G., Javadi, B., Yan, F., Buyya, R., & Jennings, N. R. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103648 · Cited by 147
Dependency-Aware Task Offloading and Service Caching in Vehicular Edge Computing
Shen, Q., Hu, B., & Xia, E. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3196544 · Cited by 136
Parking Edge Computing: Parked-Vehicle-Assisted Task Offloading for Urban VANETs
Ma, C., Zhu, J., Liu, M., Zhao, H., Liu, N., & Zou, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3056396 · Cited by 116
Research works printed but not linked: 2
- 2008, NGSIM: Next Generation Simulation (Lankershim Boulevard Dataset)
- 10.3390/e23080961 · DOI
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Chen, J., Jin, S., Cui, Y., Wei, C., Polat, K., & Alenezi, F. S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115532 · Cited by 31
Augmented Multiagent DRL for Multi-Incentive Task Prioritization in Vehicular Crowdsensing
Singh, P., Hazarika, B., Singh, K. K., Huang, W.-J., & Li, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3446954 · Cited by 20
DCAM-DETR: Dual Cross-Attention Mamba Detection Transformer for RGB–Infrared Anti-UAV Detection
Qin, Z., & Li, Y. (2026) · Information · doi:10.3390/info17010103 · Cited by 7
A Cloud–Edge–Vehicle Framework for Task Offloading With Trajectory Prediction Information
Xi, C., Dai, L., Zhao, J.-X., Chen, H., Ma, Y., & Xia, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3596187 · Cited by 6
Artificial Intelligence and Carbon Emissions of Manufacturing Enterprises in China
Huang, L., Sun, G., & Zhang, J. (2026) · Sustainability · doi:10.3390/su18157645 · Cited by 6
Metadata
- Authors
- Wu, X., Dong, J., Bao, W., Zou, B., Wang, L., & Wang, H.
- Affiliations
- Xuzhou Medical College (CN)
- China Electronics Standardization Institute (CN)
- Venue
- IEEE Internet of Things Journal
- Published
- 2024-11-15
- Volume
- 11
- Issue
- 22
- Pages
- 36030-36043
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4400111381
- Cited by
- 95
- References
- 51
- Citations by year
- 2026: 81 · 2025: 12 · 2024: 2
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- Funders
- Key Project of Basic Science Research in Colleges and Universities of Jiangsu Province (22KJA120002)
- Unveiling and Leading Project of XZHMU (JBGS202204)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- emergency vehicles, task offloading, trajectory prediction, edge computing, differential privacy, temporal convolutional network, reinforcement learning, roadside units, vehicular networks
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