Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These...Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.展开更多
Biological data,represented by the data from omics platforms,are accumulating exponentially.As some other data-intensive scientific disciplines such as high-energy physics,climatology,meteorology,geology,geography and...Biological data,represented by the data from omics platforms,are accumulating exponentially.As some other data-intensive scientific disciplines such as high-energy physics,climatology,meteorology,geology,geography and environmental sciences,modern life sciences have entered the information-rich era,the era of the 4th paradigm.The creation of Chinese information engineering infrastructure for pan-omics studies(CIEIPOS) has been long overdue as part of national scientific infrastructure,in accelerating the further development of Chinese life sciences,and translating rich data into knowledge and medical applications.By gathering facts of current status of international and Chinese bioinformatics communities in collecting,managing and utilizing biological data,the essay stresses the significance and urgency to create a 'data hub' in CIEIPOS,discusses challenges and possible solutions to integrate,query and visualize these data.Another important component of CIEIPOS,which is not part of traditional biological data centers such as NCBI and EBI,is omics informatics.Mass spectroscopy platform was taken as an example to illustrate the complexity of omics informatics.Its heavy dependency on computational power is highlighted.The demand for such power in omics studies is argued as the fundamental function to meet for CIEIPOS.Implementation outlook of CIEIPOS in hardware and network is discussed.展开更多
文摘Existing Internet of Things(IoT)systems that rely on Amazon Web Services(AWS)often encounter inefficiencies in data retrieval and high operational costs,especially when using DynamoDB for large-scale sensor data.These limitations hinder the scalability and responsiveness of applications such as remote energy monitoring systems.This research focuses on designing and developing an Arduino-based IoT system aimed at optimizing data transmission costs by concentrating on these services.The proposed method employs AWS Lambda functions with Amazon Relational Database Service(RDS)to facilitate the transmission of data collected from temperature and humidity sensors to the RDS database.In contrast,the conventional method utilizes AmazonDynamoDB for storing the same sensor data.Data were collected from 01 April 2022,to 26 August 2022,in Tokyo,Japan,focusing on temperature and relative humiditywitha resolutionof oneminute.The efficiency of the twomethods—conventional andproposed—was assessed in terms of both time and cost metrics,with a particular focus on data retrieval.The conventional method exhibited linear time complexity,leading to longer data retrieval times as the dataset grew,mainly due to DynamoDB’s pagination requirements and the parsing of payload data during the reading process.In contrast,the proposed method significantly reduced retrieval times for larger datasets by parsing payload data before writing it to the RDS database.Cost analysis revealed a savings of$1.56 per month with the adoption of the proposed approach for a 20-gigabyte database.
基金financial support of Taicang government,Suzhou,China
文摘Biological data,represented by the data from omics platforms,are accumulating exponentially.As some other data-intensive scientific disciplines such as high-energy physics,climatology,meteorology,geology,geography and environmental sciences,modern life sciences have entered the information-rich era,the era of the 4th paradigm.The creation of Chinese information engineering infrastructure for pan-omics studies(CIEIPOS) has been long overdue as part of national scientific infrastructure,in accelerating the further development of Chinese life sciences,and translating rich data into knowledge and medical applications.By gathering facts of current status of international and Chinese bioinformatics communities in collecting,managing and utilizing biological data,the essay stresses the significance and urgency to create a 'data hub' in CIEIPOS,discusses challenges and possible solutions to integrate,query and visualize these data.Another important component of CIEIPOS,which is not part of traditional biological data centers such as NCBI and EBI,is omics informatics.Mass spectroscopy platform was taken as an example to illustrate the complexity of omics informatics.Its heavy dependency on computational power is highlighted.The demand for such power in omics studies is argued as the fundamental function to meet for CIEIPOS.Implementation outlook of CIEIPOS in hardware and network is discussed.
