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Multi-objective Optimization for Data Collection in UAV-assisted Agricultural IoT

Lingling Liu, Aimin Wang, Geng Sun, Jiahui Li, Hongyang Pan, Tony Q. S. Quek

TL;DR

The paper addresses efficient data collection in UAV-assisted agricultural IoT by formulating a three-objective optimization problem that jointly optimizes UAV hovering positions, visit sequence, speed, and device transmit power to maximize the minimum data rate while minimizing device and UAV energy. An improved multi-objective hummingbird algorithm (IMOAHA) with a hybrid initialization, Cauchy mutation foraging, and a discrete mutation operator is proposed to handle the mixed continuous-discrete decision space and NP-hard nature of the problem. Through extensive Matlab simulations, IMOAHA consistently outperforms several baselines, achieving solutions that are closer to the Pareto front and offering better trade-offs among rate and energy metrics. The work advances UAV trajectory and resource allocation in precision agriculture by enabling energy-efficient, high-rate data collection across multiple subareas, with implications for scalable IoT deployments in farming.

Abstract

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.

Multi-objective Optimization for Data Collection in UAV-assisted Agricultural IoT

TL;DR

The paper addresses efficient data collection in UAV-assisted agricultural IoT by formulating a three-objective optimization problem that jointly optimizes UAV hovering positions, visit sequence, speed, and device transmit power to maximize the minimum data rate while minimizing device and UAV energy. An improved multi-objective hummingbird algorithm (IMOAHA) with a hybrid initialization, Cauchy mutation foraging, and a discrete mutation operator is proposed to handle the mixed continuous-discrete decision space and NP-hard nature of the problem. Through extensive Matlab simulations, IMOAHA consistently outperforms several baselines, achieving solutions that are closer to the Pareto front and offering better trade-offs among rate and energy metrics. The work advances UAV trajectory and resource allocation in precision agriculture by enabling energy-efficient, high-rate data collection across multiple subareas, with implications for scalable IoT deployments in farming.

Abstract

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.
Paper Structure (33 sections, 18 equations, 7 figures, 4 tables, 2 algorithms)

This paper contains 33 sections, 18 equations, 7 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature review
  3. Application of UAV in agriculture
  4. UAV-assisted fresh data collection
  5. UAV-assisted energy-efficient data collection
  6. UAV-assisted data collection of rechargeable WSN
  7. Resources allocation of UAV-assisted WSN
  8. System Models and Preliminaries
  9. Scenarios and communication model
  10. Energy consumption model of device
  11. Energy consumption model of UAV
  12. Problem formulation and analysis
  13. Problem formulation
  14. Problem analysis
  15. Trade-offs
  16. ...and 18 more sections

Figures (7)

  • Figure 1: UAV-assisted agricultural IoT model.
  • Figure 2: Solution distributions obtained by different algorithms in Case 1 (6 UAV hovering points).
  • Figure 3: Movement path of 6 UAV hovering points obtained by different algorithms.
  • Figure 4: Transmission power consumed by all devices under different algorithms in Case 1 (6 UAV hovering points).
  • Figure 5: Solution distributions obtained by different algorithms in Case 2 (8 UAV hovering points).
  • ...and 2 more figures