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Data Traffic Reduction with Compressed Sensing in an AIoT System

Computers, Materials & Continua 2022年第1期70卷 1769-1780页

作者： Hye-Min Kwon Seng-Phil Hong Mingoo Kang Jeongwook Seo Hanshin University Osan-si18101Korea hancom with inc. Seongnam-si13493Korea

To provide Artificial Intelligence(AI)services such as object detection,Internet of Things(IoT)sensor devices should be able to send a large amount of data such as images and ***,this inevitably causes IoT networks to be severely *** this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object *** consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing(CS)rate,an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing,and a YOLOv5 deep learning function for object detection,and an IoT *** analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device,we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor *** addition,we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall,precision,mAP50,and mAP,and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing ***,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

关键词： 5G Internet of Things data traffic compressed sensing YOLOv5

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Fingerprint-Based Millimeter-Wave Beam Selection for Interference Mitigation in Beamspace Multi-User MIMO Communications

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Computers, Materials & Continua 2021年第1期66卷 59-70页

作者： Sangmi MoonHyeonsung Kim Seng-Phil Hong Mingoo Kang Intae Hwang Department of Electronic Engineering Chonnam National UniversityGwangju61186Korea hancom with inc. Seongnam-si13493Korea Division of Information&Telecommunication Hanshin UniversityOsan-si18101Korea

Millimeter-wave communications are suitable for application to massive multiple-input multiple-output systems in order to satisfy the ever-growing data traffic demands of the next-generation wireless ***,their practical deployment is hindered by the high cost of complex hardware,such as radio frequency(RF)*** this end,operation in the beamspace domain,through beam selection,is a viable ***,the conventional beam selection schemes focus on the feedback and exhaustive search *** addition,sinc. the same beam in the beamspace may be assigned to a different user,conventional beam selection schemes suffer serious multi-user *** addition,some RF chains may be wasted,sinc. they do not contribute to the sum-rate ***,a fingerprint-based beam selection scheme is proposed to solve these *** proposed scheme conducts offline group-based fingerprint database construction and online beam selection to mitigate multi-user *** the offline phase,the contributing users with the same best beam are *** grouping,a fingerprint database is created for each *** the online phase,beam selection is performed for purposes of interference mitigation using the information contained in the group-based fingerprint *** simulation results confirm that the proposed beam selection scheme can achieve a signal-to-interference-plus-noise ratio and sum-rate performance which is close to those of a fully digital system,and having much higher energy efficiency.

关键词： Beam selection beamspace group-based fingerprint interference mitigation millimeter-wave

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An AIoT Monitoring System for Multi-Object Tracking and Alerting

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Computers, Materials & Continua 2021年第4期67卷 337-348页

作者： Wonseok Jung Se-Han Kim Seng-Phil Hong Jeongwook Seo Korea Electronics Technology Institute Seongnam13509Korea Electronics and Telecommunications Research Institute Daejeon34129Korea hancom with inc. Seongnam13493Korea Hanshin University Osan-si18101Korea

Pig farmers want to have an effective solution for automatically detecting and tracking multiple pigs and alerting their conditions in order to recognize disease risk factors *** this paper,therefore,we propose a novel monitoring system using an Artificial Intelligence of Things(AIoT)technique combining artificial intelligence and Internet of Things(IoT).The proposed system consists of AIoT edge devices and a central monitoring ***,an AIoT edge device extracts video frame images from a CCTV camera installed in a pig pen by a frame extraction method,detects multiple pigs in the images by a faster region-based convolutional neural network(RCNN)model,and tracks them by an object center-point tracking algorithm(OCTA)based on bounding box regression outputs of the faster ***,it sends multi-pig tracking images to the central monitoring server,which alerts them to pig farmers through a social networking service(SNS)agent in cooperation with an oneM2M-compliant IoT alerting *** results showed that the multi-pig tracking method achieved the multi-object tracking accuracy performance of about 77%.In addition,we verified alerting operation by confirming the images received in the SNS smartphone application.

关键词： Internet of Things multi-object tracking pig pen social network

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