Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks

Zhu, Anqi; Zeng, Zhiwen; Guo, Songtao; Lu, Huimin; Ma, Mingfang; Zhou, Zongtan

doi:10.1016/j.compag.2023.108017

Computers and Electronics in Agriculture

2023

DOI: 10.1016/j.compag.2023.108017

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Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks

Anqi Zhu

Zhiwen Zeng

Songtao Guo

et al.

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Cited by 5 publications

(1 citation statement)

References 36 publications

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“…In the meantime, UAV and LAPS platforms tend to be much more flexible when deployed in challenging terrains, adapting to ground users to provide ultra-reliable and low-latency communication links for time-sensitive UGV tasks. In the future, 6G network ultra-reliable and low-latency communication (URLLC) will be essential for UGVs, which are indispensable for real-time applications such as smart agriculture [113][114][115], intelligent industry, and various other applications. To accurately control the UGV, tactile feedback and interactive virtual reality (VR) can stimulate the human brain, aiding users in adjusting operation time, pressure, and gestures.…”

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confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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mentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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Cloud–Edge Framework for AoI-Efficient Data Processing in Multi-UAV-Assisted Sensor Networks

Ma,

Wang,

Guo

et al. 2024

IEEE Internet Things J.

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DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks

Zhu,

Lu,

et al. 2023

Drones

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With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.

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Game-theoretic robotic offloading via multi-agent learning for agricultural applications in heterogeneous networks

Cited by 5 publications

References 36 publications

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Cloud–Edge Framework for AoI-Efficient Data Processing in Multi-UAV-Assisted Sensor Networks

DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks

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