Wu, Xiang and Dong, Jian and Bao, Wei and Zou, Baowen and Wang, Lili and Wang, Huanhuan (2024) Augmented Intelligence of Things for Emergency Vehicle Secure Trajectory Prediction and Task Offloading. IEEE Internet of Things Journal, 11 (22). pp. 36030-36043. ISSN 2327-4662, DOI https://doi.org/10.1109/JIOT.2024.3417285.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Key Project of Basic Science Research in Colleges, Universities of Jiangsu Province (22KJA120002), Unveiling and Leading Project of XZHMU (JBGS202204) |
| Uncontrolled Keywords: | Task analysis; Trajectory; Internet of Things; Delays; Privacy; Differential privacy; Sensitivity; Augmented Intelligence of Things (AIoT); edge computing (EC); emergency rescue; task offloading; trajectory prediction |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 21 Feb 2025 03:45 |
| Last Modified: | 21 Feb 2025 03:45 |
| URI: | http://eprints.um.edu.my/id/eprint/47310 |
Actions (login required)
 |
View Item |
