Cited by 104IoT and Edge/Fog ComputingVehicular Ad Hoc Networks (VANETs)Privacy-Preserving Technologies in Data

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Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

Wu, Xiang, Dong, Jian, Bao, Wei, Zou, Baowen, Wang, Lili, Wang, Huanhuan·2024·IEEE Internet of Things Journal

DOI 10.1109/jiot.2024.3417285

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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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Authors: Wu, X., Dong, J., Bao, W., Zou, B., Wang, L., & Wang, H.
Affiliations: Xuzhou Medical College (CN)
China Electronics Standardization Institute (CN)
ORCID: Wu, Xiang
Dong, Jian
Zou, Baowen
Wang, Lili
Wang, Huanhuan
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-11-15
Volume: 11
Issue: 22
Pages: 36030-36043
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2024.3417285
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading

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