Deep Learning based Payload Optimization for Image Transmission over LoRa with HARQ

IF 7.6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Khondoker Ziaul Islam , David Murray , Dean Diepeveen , Michael G.K. Jones , Ferdous Sohel
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引用次数: 0

Abstract

LoRa is a wireless technology suited for long-range IoT applications. Leveraging LoRa technology for image transmission could revolutionize many applications, such as surveillance and monitoring, at low costs. However, transmitting images, through LoRa is challenging due to LoRa’s limited data rate and bandwidth. To address this, we propose a pipeline to prepare a reduced image payload for transmission captured by a camera in a reasonably static background, which is common in surveillance settings. The main goal is to minimize the uplink payload while maintaining image quality. We use a selective transmission approach where dissimilar images are divided into patches, and a deep learning Siamese network determines if an image or patch has new content compared to previously transmitted ones. The data is then compressed and sent in constant packets via HARQ to reduce downlink requirements. Enhanced super-resolution generative adversarial networks and principal component analysis are used to reconstruct the images/patches. We tested our approach with two surveillance videos at two sites using LoRaWAN gateways, end devices, and a ChirpStack server. Assuming no duty cycle restrictions, our pipeline can transmit videos—converted to 1616 and 584 frames—in 7 and 26 min, respectively. Increased duty cycle restrictions and significant image changes extend the transmission time. At Murdoch Oval, we achieved 100% throughput with no retransmissions required for both sets. At Whitby Falls Farm, throughput was 98.3%, with approximately 71 and 266 packets needing retransmission for Sets 1 and 2, respectively.
基于深度学习的基于HARQ的LoRa图像传输有效载荷优化
LoRa是一种适合远程物联网应用的无线技术。利用LoRa技术进行图像传输可以以低成本彻底改变许多应用程序,例如监视和监视。然而,由于LoRa有限的数据速率和带宽,通过LoRa传输图像具有挑战性。为了解决这个问题,我们提出了一个管道来准备一个减少的图像有效载荷,以便在一个合理的静态背景下由摄像机捕获的传输,这在监视设置中很常见。主要目标是在保持图像质量的同时最小化上行负载。我们使用选择性传输方法,将不同的图像分成小块,深度学习Siamese网络确定图像或小块与先前传输的图像或小块相比是否有新的内容。然后,数据被压缩并通过HARQ以恒定的数据包发送,以减少下行链路的需求。使用增强的超分辨率生成对抗网络和主成分分析来重建图像/补丁。我们使用LoRaWAN网关、终端设备和ChirpStack服务器在两个站点的两个监控视频中测试了我们的方法。假设没有占空比限制,我们的管道可以分别在7分钟和26分钟内传输视频(转换为1616帧和584帧)。增加的占空比限制和显著的图像变化延长了传输时间。在默多克椭圆形,我们实现了100%的吞吐量,没有重传需要两组。在Whitby Falls Farm,吞吐量为98.3%,第1组和第2组分别有大约71和266个数据包需要重传。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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