Multi-objective Optimization for Data Collection in UAV-assisted Agricultural IoT
<|reference_start|>Multi-objective Optimization for Data Collection in UAV-assisted Agricultural IoT: The ground fixed base stations (BSs) are often deployed inflexibly, and have high overheads, as well as are susceptible to the damage from natural disasters, making it impractical for them to continuously collect data from sensor devices. To improve the network coverage and performance of wireless communication, unmanned aerial vehicles (UAVs) have been introduced in diverse wireless networks, therefore in this work we consider employing a UAV as an aerial BS to acquire data of agricultural Internet of Things (IoT) devices. To this end, we first formulate a UAV-assisted data collection multi-objective optimization problem (UDCMOP) to efficiently collect the data from agricultural sensing devices. Specifically, we aim to collaboratively optimize the hovering positions of UAV, visit sequence of UAV, speed of UAV, in addition to the transmit power of devices, to simultaneously achieve the maximization of minimum transmit rate of devices, the minimization of total energy consumption of devices, and the minimization of total energy consumption of UAV. Second, the proposed UDCMOP is a non-convex mixed integer nonlinear optimization problem, which indicates that it includes continuous and discrete solutions, making it intractable to be solved. Therefore, we solve it by proposing an improved multi-objective artificial hummingbird algorithm (IMOAHA) with several specific improvement factors, that are the hybrid initialization operator, Cauchy mutation foraging operator, in addition to the discrete mutation operator. Finally, simulations are carried out to testify that the proposed IMOAHA can effectively improve the system performance comparing to other benchmarks.<|reference_end|>
@article{liu2024multi-objective,
    title={Multi-objective Optimization for Data Collection in UAV-assisted
  Agricultural IoT},
    author={Lingling Liu, Aimin Wang, Geng Sun, Jiahui Li, Hongyang Pan, Tony Q.
  S. Quek},
    journal={arXiv preprint arXiv:2403.12985},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12985},
    primaryClass={cs.IT eess.SP math.IT}
}