Leveraging Augmented Intelligence of Things to Enhance Lifetime of UAV-Enabled Aerial Networks

被引:4
|
作者
Mishra, Rahul [1 ]
Gupta, Hari Prabhat [1 ]
Kumar, Ramakant [1 ]
Dutta, Tanima [1 ]
机构
[1] BHU Varanasi, Dept Comp Sci & Engn, Indian Inst Technol, Varanasi 221005, Uttar Pradesh, India
关键词
Autonomous aerial vehicles; Training; Task analysis; Logic gates; Internet of Things; Monitoring; Artificial intelligence; Augmented intelligence (AI) of things; deep neural networks; knowledge distillation (KD); unmanned aerial vehicles (UAVs); DRIVEN;
D O I
10.1109/TII.2022.3197410
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Augmented intelligence is an innovative amplification of artificial intelligence that allows human experts to take over the autonomous decision of machines. It also facilitates human-intelligence-based decisions on the network edge using low-cost and small-sized devices. Augmented intelligence and the Internet of Things collectively create augmented intelligence of things. It logically and effortlessly interrelates human intelligence to articulate smart decisions. Unmanned aerial vehicles find various applications ranging from search operations during disasters to intruders identification; thus, they are suitable for aerial networks, where connections between base stations and servers are extinct. This article presents an approach to enhance the lifetime of unmanned-aerial-vehicles-enabled aerial networks via augmented intelligence. It first considers the available battery power to transform a large-size deep neural network into a lightweight. We next present a knowledge-distillation-based approach, which reduces training time and enhances accuracy. Finally, we evaluate the approach on the existing dataset.
引用
收藏
页码:586 / 593
页数:8
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