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 videos.However,this inevitably causes IoT ...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 videos.However,this inevitably causes IoT networks to be severely overloaded.In this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object detection.It 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 server.By 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 devices.In 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 rate.Consequently,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.展开更多
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 quickly.In this paper,therefore,we propose...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 quickly.In 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 server.First,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 RCNN.Finally,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 method.Experimental 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.展开更多
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 communication.How...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 communication.However,their practical deployment is hindered by the high cost of complex hardware,such as radio frequency(RF)chains.To this end,operation in the beamspace domain,through beam selection,is a viable solution.Generally,the conventional beam selection schemes focus on the feedback and exhaustive search techniques.In addition,since the same beam in the beamspace may be assigned to a different user,conventional beam selection schemes suffer serious multi-user interference.In addition,some RF chains may be wasted,since they do not contribute to the sum-rate performance.Thus,a fingerprint-based beam selection scheme is proposed to solve these problems.The proposed scheme conducts offline group-based fingerprint database construction and online beam selection to mitigate multi-user interference.In the offline phase,the contributing users with the same best beam are grouped.After grouping,a fingerprint database is created for each group.In the online phase,beam selection is performed for purposes of interference mitigation using the information contained in the group-based fingerprint database.The 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.展开更多
基金This work was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020-0-00959,Fast Intelligence Analysis HW/SW Engine Exploiting IoT Platform for Boosting On-device AI in 5G Environment).
文摘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 videos.However,this inevitably causes IoT networks to be severely overloaded.In this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object detection.It 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 server.By 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 devices.In 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 rate.Consequently,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)Grant funded by the Korea government(MSIT)(No.2018-0-00387Development of ICT based Intelligent Smart Welfare Housing System for the Prevention and Control of Livestock Disease).
文摘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 quickly.In 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 server.First,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 RCNN.Finally,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 method.Experimental 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.
基金The Ministry of Science and ICT(MSIT),Korea,under the Information Technology Research Center(ITRC)support program(IITP-2020-2016-0-00314)supervised by the Institute for Information&communications Technology Planning&Evaluation(IITP)was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT:Ministry of Science and ICT)(2018R1A2B6002255 and 2020R1I1A1A01073948).
文摘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 communication.However,their practical deployment is hindered by the high cost of complex hardware,such as radio frequency(RF)chains.To this end,operation in the beamspace domain,through beam selection,is a viable solution.Generally,the conventional beam selection schemes focus on the feedback and exhaustive search techniques.In addition,since the same beam in the beamspace may be assigned to a different user,conventional beam selection schemes suffer serious multi-user interference.In addition,some RF chains may be wasted,since they do not contribute to the sum-rate performance.Thus,a fingerprint-based beam selection scheme is proposed to solve these problems.The proposed scheme conducts offline group-based fingerprint database construction and online beam selection to mitigate multi-user interference.In the offline phase,the contributing users with the same best beam are grouped.After grouping,a fingerprint database is created for each group.In the online phase,beam selection is performed for purposes of interference mitigation using the information contained in the group-based fingerprint database.The 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.
