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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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Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers

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Global Energy Interconnection 2020年第3期3卷 272-282页

作者： Yanan Liu Xiaoxia Wei Jinyu Xiao Zhijie Liu Yang Xu Yun Tian School of Artificial Intelligence Beijing Normal UniversityHaidian DistrictBeijng 100875China Global Energy Interconnection Development and Cooperation Organization Xicheng DistrictBeijing 100031China

With the rapid development of technologies such as big data and cloud computing,data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data ***,data centers will become the world’s largest users of energy consumption,with the ratio rising from 3%in 2017 to 4.5%in *** to its unique climate and energy-saving advantages,the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent *** order to predict and analyze the future energy consumption and carbon emissions of global data centers,this paper presents a new method based on global data center traffic and power usage effectiveness(PUE)for energy consumption ***,global data center traffic growth is predicted based on the Cisco’s ***,the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained,and then global data center energy consumption with two different scenarios,the decentralized scenario and the centralized scenario,is analyzed quantitatively via the polynomial fitting *** simulation results show that,in 2030,the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario,which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy *** study provides support for global energy consumption prediction,and guidance for the layout of future global data centers from the perspective of energy ***,it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

关键词： data center Pan-Arctic Energy consumption carbon emission data traffic PUE Global Energy Interconnection

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