Collaborative WSN-UAV Data Collection in Smart Agriculture: A Bi-objective Optimization Scheme-Reference-Cited by-同舟云学术

Collaborative WSN-UAV Data Collection in Smart Agriculture: A Bi-objective Optimization Scheme

Published:2023-05-11 Issue: Volume: Page:
ISSN:1550-4859
Container-title:ACM Transactions on Sensor Networks
language:en
Short-container-title:ACM Trans. Sen. Netw.

Author:

Sun Geng¹,Zheng Xiaoya²,Li Jiahui²,Kang Hui²,Liang Shuang³

Affiliation:

1. 1) College of Computer Science and Technology, Jilin University; 2) Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China

2. College of Computer Science and Technology, Jilin University, China

3. College of Information Science and Technology, Northeast Normal University, China

Abstract

Smart agriculture which integrates the agriculture with Internet of Things (IoT) has attracted attention since it contributes to increase the productivity and quality of crops, reduce energy consumption and facilitate the farmers. Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) are two most commonly deployed devices that are used for enabling the smart agriculture. In this paper, we design a collaborative WSN-UAV system, wherein different clusters of sensor nodes form different sensor-based virtual antenna arrays (SVAAs) to transmit the collected data towards different receiver UAVs via adopting collaborative beamforming (CB), then the receiver UAVs will take the collected data back to the ground control station (GCS). We formulate a transmission rate and battery energy bi-objective optimization problem (TRBEBOP) to simultaneously maximize the total transmission rate of the sensor-based CB clusters and the total remaining battery energy of the selected sensor nodes, by selecting appropriate sensor nodes in each cluster that can form a predominant SVAA, determining suitable receiver UAVs and optimizing the excitation current weights of the selected sensor nodes. To handle the formulated TRBEBOP that is demonstrated to be non-convex and NP-hard, an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) with several specific designs is presented. Simulation results validate the effectiveness of the proposed ENSGA-II for solving the formulated TRBEBOP. Moreover, compared with other benchmark algorithms, the superiority of the proposed ENSGA-II is demonstrated. In addition, the impacts of several fortuitous circumstances on the system are estimated, and the results illustrate the robustness of the proposed scheme. Finally, the discussion about several mechanisms to deal with the interference induced by the sidelobe levels and the impact of UAV movement on receiving rate are provided.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Link

https://dl.acm.org/doi/pdf/10.1145/3597025

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