scholarly journals Synthetic data generation for the internet of things

Author(s):  
Jason W. Anderson ◽  
K. E. Kennedy ◽  
Linh B. Ngo ◽  
Andre Luckow ◽  
Amy W. Apon
Biotechnology ◽  
2019 ◽  
pp. 1967-1984
Author(s):  
Dharmendra Trikamlal Patel

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.


2017 ◽  
Author(s):  
Ivan Zyrianoff ◽  
Fabrizio Borelli ◽  
Alexandre Heideker ◽  
Gabriela Biondi ◽  
Carlos Kamienski

Context-Aware Management Systems have been proposed in the last years to perform automatic decision making for the Internet of Things. Although scalability is an indispensable feature for those systems, there are no comprehensive results reporting their performance. This paper shows results of a performance analysis study of different context-aware architectures and introduces the SenSE platform for generating sensor synthetic data. Results show that different architectural choices impact system scalability and that automatic real time decision-making is feasible in an environment composed of dozens of thousands of sensors that continuously transmit data.


Author(s):  
Dharmendra Trikamlal Patel

Voluminous data are being generated by various means. The Internet of Things (IoT) has emerged recently to group all manmade artificial things around us. Due to intelligent devices, the annual growth of data generation has increased rapidly, and it is expected that by 2020, it will reach more than 40 trillion GB. Data generated through devices are in unstructured form. Traditional techniques of descriptive and predictive analysis are not enough for that. Big Data Analytics have emerged to perform descriptive and predictive analysis on such voluminous data. This chapter first deals with the introduction to Big Data Analytics. Big Data Analytics is very essential in Bioinformatics field as the size of human genome sometimes reaches 200 GB. The chapter next deals with different types of big data in Bioinformatics. The chapter describes several problems and challenges based on big data in Bioinformatics. Finally, the chapter deals with techniques of Big Data Analytics in the Bioinformatics field.


2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Liane Colonna

This paper explores a specific risk-mitigation strategy to reduce privacy concerns in the Internet of Health Things (IoHT): data anonymization. It contributes to the current academic debate surrounding the role of anonymization in the IoHT by evaluating how data controllers can balance privacy risks against the quality of output data and select the appropriate privacy model that achieves the aims underlying the concept of Privacy by Design. It sets forth several approaches for identifying the risk of re-identification in the IoHT as well as explores the potential for synthetic data generation to be used as an alternative method to anonymization for data sharing.


2020 ◽  
pp. 1-12
Author(s):  
Zhang Caiqian ◽  
Zhang Xincheng

The existing stand-alone multimedia machines and online multimedia machines in the market have certain deficiencies, so they cannot meet the actual needs. Based on this, this research combines the actual needs to design and implement a multi-media system based on the Internet of Things and cloud service platform. Moreover, through in-depth research on the MQTT protocol, this study proposes a message encryption verification scheme for the MQTT protocol, which can solve the problem of low message security in the Internet of Things communication to a certain extent. In addition, through research on the fusion technology of the Internet of Things and artificial intelligence, this research designs scheme to provide a LightGBM intelligent prediction module interface, MQTT message middleware, device management system, intelligent prediction and push interface for the cloud platform. Finally, this research completes the design and implementation of the cloud platform and tests the function and performance of the built multimedia system database. The research results show that the multimedia database constructed in this paper has good performance.


2019 ◽  
pp. 4-44 ◽  
Author(s):  
Peter Thorns

This paper discusses the organisations involved in the development of application standards, European regulations and best practice guides, their scope of work and internal structures. It considers their respective visions for the requirements for future standardisation work and considers in more detail those areas where these overlap, namely human centric or integrative lighting, connectivity and the Internet of Things, inclusivity and sustainability.


2019 ◽  
Vol 14 (5) ◽  
pp. 375
Author(s):  
Vladimir P. Zhalnin ◽  
Anna S. Zakharova ◽  
Demid A. Uzenkov ◽  
Andrey I. Vlasov ◽  
Alexey I. Krivoshein ◽  
...  

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