Blockchain-Enabled Self-Autonomous Intelligent Transport System for Drone Task Workflow in Edge Cloud Networks
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| Publicado no: | Algorithms vol. 18, no. 8 (2025), p. 530-555 |
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| Autor principal: | |
| Outros Autores: | , , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Pattaraporn, Khuwuthyakorn |u Innovative Research and Computational Science Lab, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand | |
| 245 | 1 | |a Blockchain-Enabled Self-Autonomous Intelligent Transport System for Drone Task Workflow in Edge Cloud Networks | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. | |
| 653 | |a Agents (artificial intelligence) | ||
| 653 | |a Security | ||
| 653 | |a Communication | ||
| 653 | |a Fault tolerance | ||
| 653 | |a Blockchain | ||
| 653 | |a Transportation networks | ||
| 653 | |a Aircraft performance | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Transportation applications | ||
| 653 | |a Localization | ||
| 653 | |a Intelligent transportation systems | ||
| 653 | |a Deadlines | ||
| 653 | |a Internet of Things | ||
| 653 | |a Heterogeneity | ||
| 653 | |a Scheduling | ||
| 653 | |a Machine learning | ||
| 653 | |a Costs | ||
| 653 | |a Autonomous vehicles | ||
| 653 | |a Sensors | ||
| 653 | |a Privacy | ||
| 653 | |a Vehicles | ||
| 653 | |a Quality of service | ||
| 653 | |a Drones | ||
| 653 | |a Workflow software | ||
| 653 | |a Algorithms | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Deep learning | ||
| 653 | |a Client server systems | ||
| 653 | |a Cloud computing | ||
| 653 | |a Drone aircraft | ||
| 700 | 1 | |a Abdullah, Lakhan |u School of Economics, Innovations and Technology, Kristiania University College, 1190 Sentrum, 0107 Oslo, Norway | |
| 700 | 1 | |a Majumdar Arnab |u Transport Risk Management Centre, Imperial College London, London SW7 2AZ, UK | |
| 700 | 1 | |a Orawit, Thinnukool |u Innovative Research and Computational Science Lab, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 8 (2025), p. 530-555 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3243965809/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3243965809/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3243965809/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |