scholarly journals Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics

2018 ◽  
Vol 32 (3) ◽  
pp. 315-339 ◽  
Author(s):  
Susan L. Erikson
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>


Author(s):  
Vivekanadam B

Use of automation and intelligence in smart grids has led to implementation in a number of applications. When internet of things is incorporated it will result in the significant improvement a number of factors such as fault recovery, energy delivery efficiency, demand response and reliability. However, the collaboration of internet of things and smart grid gives rise to a number of security issues and threats. This is especially the case when using internet based protocols and public communication infrastructure. To address these issues we should ensure that the data stored is secure and critical information from the data is extracted in a careful manner. If any threat to its security is detective an early blackout warning should be issued immediately. In this paper we have proposed a geometric view point for big data attacks which is capable of bypassing bad data detection. We have created an environment where replay scheme is used launch blind energy big data attack. The defence mechanism of our proposed work is studied and found to be efficient. Experimental evidence supports our theory and we have found our methodology to efficiently improve error detection rate.


Big data isgenerated from a variety of sources.Data sets can grow rapidly: for instance, social media such as Facebook, Instagram, and Twitter generate terabytes of data every day. Desktop computers andlaptopsgenerate tremendous amountsof data. Geospatial data aregenerated by cell phones and even from satellites. IoT (Internet of Things) devices like sensorsandpocket computers are also generating a massive amount of data. “Big data”generatestremendous attention worldwide. This paper aims to provide a more comprehensive description of big data that captures its other specific and distinguishing characteristics, which metrics describe the size and other characteristics of big data, and which tools and technologies exist to leverage the potential of big data


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80122-80132 ◽  
Author(s):  
Jian Wan ◽  
Piaopiao Zheng ◽  
Huayou Si ◽  
Neal N. Xiong ◽  
Wei Zhang ◽  
...  

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 186304-186322
Author(s):  
Hameeza Ahmed ◽  
Muhammad Ali Ismail
Keyword(s):  
Big Data ◽  

Author(s):  
Peige Ren ◽  
Xiaofeng Wang ◽  
Hao Sun ◽  
Fen Xu ◽  
Baokang Zhao ◽  
...  

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