Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform
Unmanned aerial vehicle-to-everything (U2X) communications have become increasingly essential technologies to support multiple delay and computation-sensitive services, including augmented reality (AR), Virtual Reality (VR), digital twins, smart farms, unknown geological exploration, smart manufactu...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11009159/ |
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| author | Sida Huang Matilda Isaac Yuan Gao Yuji Dong Jie Zhang Bintao Hu |
| author_facet | Sida Huang Matilda Isaac Yuan Gao Yuji Dong Jie Zhang Bintao Hu |
| author_sort | Sida Huang |
| collection | DOAJ |
| description | Unmanned aerial vehicle-to-everything (U2X) communications have become increasingly essential technologies to support multiple delay and computation-sensitive services, including augmented reality (AR), Virtual Reality (VR), digital twins, smart farms, unknown geological exploration, smart manufacturing, etc. Therefore, it is necessary to propose effective privacy-preserving communication and computation optimisation algorithm schemes to improve the quality of service (QoS) of multiple UAV-assisted Internet of Things (IoT) systems. In recent years, blockchain, U2X communications, and mobile edge computing will gradually become the main technologies of future academia and industry to overcome the aforementioned challenges for the UAV-assisted IoT system networks, while guaranteeing user data collection, data processing, and privacy-preserving requirements. In this paper, we consider a UAV-assisted IoT smart farm network systems, where blockchain technology is applied at each UAV server to guarantee real-time user data collection, data processing, data encryption, system access authorisation, and immutability. To minimise the total service delay (which includes the transmission delay and processing delay) among the user equipment (UEs) and UAVs, we propose jointly optimising the communication resource allocation for all UEs and UAVs, the computation resource allocation at each UAV, and the transmission power. This is achieved by devising a Q-learning-based optimisation algorithm, which is called a Q-learning-based latency reduction optimisation algorithm (QLROA). Simulation results illustrate that our proposed algorithm outperforms the benchmarks in terms of the long-term average delay among all the UEs. |
| format | Article |
| id | doaj-art-24a4ac24d75d4f4291ff31dbfd988a39 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-24a4ac24d75d4f4291ff31dbfd988a392025-08-20T02:19:31ZengIEEEIEEE Access2169-35362025-01-0113926759268910.1109/ACCESS.2025.357251911009159Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services PlatformSida Huang0https://orcid.org/0009-0004-8456-4678Matilda Isaac1https://orcid.org/0009-0004-2115-628XYuan Gao2https://orcid.org/0000-0003-1014-9725Yuji Dong3https://orcid.org/0000-0001-6715-7588Jie Zhang4https://orcid.org/0000-0001-7415-7869Bintao Hu5https://orcid.org/0000-0003-4821-0448School of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai, ChinaSchool of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaSchool of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, ChinaUnmanned aerial vehicle-to-everything (U2X) communications have become increasingly essential technologies to support multiple delay and computation-sensitive services, including augmented reality (AR), Virtual Reality (VR), digital twins, smart farms, unknown geological exploration, smart manufacturing, etc. Therefore, it is necessary to propose effective privacy-preserving communication and computation optimisation algorithm schemes to improve the quality of service (QoS) of multiple UAV-assisted Internet of Things (IoT) systems. In recent years, blockchain, U2X communications, and mobile edge computing will gradually become the main technologies of future academia and industry to overcome the aforementioned challenges for the UAV-assisted IoT system networks, while guaranteeing user data collection, data processing, and privacy-preserving requirements. In this paper, we consider a UAV-assisted IoT smart farm network systems, where blockchain technology is applied at each UAV server to guarantee real-time user data collection, data processing, data encryption, system access authorisation, and immutability. To minimise the total service delay (which includes the transmission delay and processing delay) among the user equipment (UEs) and UAVs, we propose jointly optimising the communication resource allocation for all UEs and UAVs, the computation resource allocation at each UAV, and the transmission power. This is achieved by devising a Q-learning-based optimisation algorithm, which is called a Q-learning-based latency reduction optimisation algorithm (QLROA). Simulation results illustrate that our proposed algorithm outperforms the benchmarks in terms of the long-term average delay among all the UEs.https://ieeexplore.ieee.org/document/11009159/Mobile edge computingblockchainreinforcement learningunmanned aerial vehicles (UAV)Internet of Things (IoT)smart farms |
| spellingShingle | Sida Huang Matilda Isaac Yuan Gao Yuji Dong Jie Zhang Bintao Hu Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform IEEE Access Mobile edge computing blockchain reinforcement learning unmanned aerial vehicles (UAV) Internet of Things (IoT) smart farms |
| title | Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform |
| title_full | Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform |
| title_fullStr | Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform |
| title_full_unstemmed | Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform |
| title_short | Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform |
| title_sort | blockchain empowered uav assisted computation resource allocation in smart farm services platform |
| topic | Mobile edge computing blockchain reinforcement learning unmanned aerial vehicles (UAV) Internet of Things (IoT) smart farms |
| url | https://ieeexplore.ieee.org/document/11009159/ |
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