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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.
“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.”
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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