Blockchain-Empowered UAV-Assisted Computation Resource Allocation in Smart Farm Services Platform - Sida Huang; Matilda Isaac; Yuan Gao; Yuji Dong; Jie Zhang; Bintao Hu (IEEE Access)
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.