Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high pos...Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.展开更多
Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing plat...Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing platform is built to handle such a large amount of data,which is composed of some subsystems such as data transfer,data storage,high throughput computing and metadata management.Results and conclusions The platform was under construction since 2018 and has been working well since 2021.In this paper,the details of the design,implementation and performance of the data processing platform are presented.展开更多
文摘Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.
基金supported by National Nature Science Foundation of China(GrantNos.12075268,12175255,12175258,12105300)the Chinese Academy of Science,Institute of High Energy Physics.
文摘Purpose The LHAASO project collects trillions of cosmic ray events every year,generating about 10 PB of raw data annually,which brings big challenges for data processing platform.Method The LHAASO data processing platform is built to handle such a large amount of data,which is composed of some subsystems such as data transfer,data storage,high throughput computing and metadata management.Results and conclusions The platform was under construction since 2018 and has been working well since 2021.In this paper,the details of the design,implementation and performance of the data processing platform are presented.
