scholarly journals Semantic Smart World Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
K. ElDahshan ◽  
E. K. Elsayed ◽  
H. Mancy

This paper presents a general Semantic Smart World framework (SSWF), to cover the Migratory birds’ paths. This framework combines semantic and big data technologies to support meaning for big data. In order to build the proposed smart world framework, technologies such as cloud computing, semantic technology, big data, data visualization, and the Internet of Things are hybrid. We demonstrate the proposed framework through a case study of automatic prediction of air quality index and different weather phenomena in the different locations in the world. We discover the association between air pollution and increasing weather conditions. The experimental results indicate that the framework performance is suitable for heterogeneous big data.

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Swapnil Bandal ◽  
Jakkan D. A. ◽  
Jakkan D. A. ◽  
Jakkan D. A.

The framework proposed in this paper is a cutting-edge method for tracking weather conditions in a specific location and making the data available to everyone, anywhere in the world. The Internet of Things (IoT) is the technology behind this, which is a cutting-edge and cost-effective approach for connecting things to the internet and connecting the whole universe of things in a network. Things could go either way here.


2019 ◽  
Vol 20 (2) ◽  
pp. 365-376 ◽  
Author(s):  
Vivek Kumar Prasad ◽  
Madhuri D Bhavsar ◽  
Sudeep Tanwar

The evolution of the Internet of Things (IoT) has augmented the necessity for Cloud, edge and fog platforms. The chief benefit of cloud-based schemes is they allow data to be collected from numerous services and sites, which is reachable from any place of the world. The organizations will be benefited by merging the cloud platform with the on-site fog networks and edge devices and as result, this will increase the utilization of the IoT devices and end users too. The network traffic will reduce as data will be distributed and this will also improve the operational efficiency. The impact of monitoring in edge and fog computing can play an important role to efficiently utilize the resources available at these layers. This paper discusses various techniques involved for monitoring for edge and fog computing and its advantages. The paper ends with a case study to demonstarte the need of monitoring in fog and edge in the healthcare system.


2021 ◽  
Vol 8 (4) ◽  
pp. 685-733
Author(s):  
Jennifer Zwagerman

Technology advancements make life, work, and play easier and more enjoyable in many ways. Technology issues are also the cause of many headaches and dreams of living out the copier destruction scene from the movie “Office Space.” Whether it be user error or technological error, one key technology issue on many minds right now is how all the data produced every second of every day, in hundreds of different ways, is used by those that collect it. How much data are we talking about here? In 2018, the tech company Domo estimated that by 2020 “1.7 MB of data will be created every second” for every single person on Earth. In 2019, Domo’s annual report noted that “Americans use 4,416,720 GB of internet data including 188,000,000 emails, 18,100,000 texts and 4,497,420 Google searches every single minute.” And this was before the pandemic of 2020, which saw reliance on remote technology and the internet skyrocket. It is not just social media and working from home that generates data—the “Internet of Things” (“IoT”) is expanding exponentially. From our homes (smart appliances and thermostats), to entertainment (smart speakers and tablets), to what we wear (smartwatches and fitness devices), we are producing data constantly. Over 30 billion devices currently make up the IoT, and that number will double by 2025. The IoT is roughly defined as “devices—from simple sensors to smartphones and wearables—connected together.” That connection allows the devices to “talk” to each other across networks that stretch across the world, sharing information that in turn can be analyzed (alone or combined with data from other users) in ways that may be beneficial to the user or the broader economy. The key word in that last sentence is “may.” When it comes to the data that individuals and businesses across the world produce every second of every day, some of it—perhaps most of it—could be used in ways that are not beneficial to the user or the entire economy. Some data types can be used to cause harm in obvious ways, such as personal identifying information in cases of identity theft. While some data types may seem innocuous or harmful when viewed on their own, when combined with other data from the same user or even other users, it can be used in a wide variety of ways. While I find it beneficial to know how many steps I take in a day or how much time I sleep at night, I am not the only individual or entity with access to that information. The company that owns the device I wear also takes that information and uses it in ways that are beyond my control. Why would a company do that? In many instances, “[t]he data generated by the Internet of Things provides businesses with a wealth of information that—when properly collected, stored, and processed—gives businesses a depth of insight into user behavior never before seen.” Data security and privacy in general are issues that all companies manage as they work to protect the data we provide. Some types of data receive heightened protections, as discussed below, because they are viewed as personal, as private, or as potentially dangerous since unauthorized access to them could cause harm to the user/owner. Some states and countries have taken a step further, focusing not on industry-related data that needs particular types of protection, but in-stead looking at an individual’s overall right to privacy, particularly on the internet. Those protections are summarized below. It makes sense, you might say, to worry about financial or healthcare data remaining private and to not want every website you have ever visited to keep a file of information on you. But why might we care about the use of data in agricultural operations? Depending on who you ask, the answer may be that agricultural data needs no more care or concern than any other type of business data. Some argue that the use of “Big Data” in agriculture provides opportunities for smaller operations and shareholders. These opportunities include increased power in a market driven for many years by the mantra “bigger is better” and increased production of food staples across the world—both in a more environmentally-friendly fashion. While the benefits of technology and Big Data in the agricultural sector unarguably exist, questions remain as to how to best manage data privacy concerns in an industry where there is little specific law or regulation tied to collection, use, and ownership of this valuable agricultural production data. In the following pages, this Article discusses what types of data are currently being gathered in the agricultural sector and how some of that data can and is being used. In addition, it focuses on unique considerations tied to the use of agricultural data and why privacy concerns continue to increase for many producers. As the Article looks at potential solutions to privacy concerns, it summarizes privacy-related legislation that currently exists and ends by looking at whether any of the current privacy-related laws might be used or adapted within the agricultural sector to address potential misuse of agricultural data.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hui Deng ◽  
Dongwen Xie

With the development of the Internet of Things and big data technologies and the rise of smart cities, more and more Internet of Things and big data technologies are applied in the economic field, and the construction of the Shanghai Pilot Free-Trade Zone will be important for my country’s follow-up construction of free-trade zones and the whole country. Its economic development strategy has a significant impact, and it has also become a hot spot for domestic and foreign research. Among them, the use of big data and the Internet of Things application of the free-trade zone tax policy system for research cannot be ignored. This article puts forward the literature in the process of writing research method, quantitative research method, and case research method. We used the literature research method to study the theory of big data and tax informatization, used the quantitative research method to conduct data statistics on the tax system of the free-trade zone to understand the information construction of the tax authority, and used the case study method to realize the “data quality monitoring platform.” Taxation informatization management in turn brings enlightenment and suggestions for big data to promote the taxation informatization construction of the free-trade zone. A total of 2,854 samples of tax data were extracted from the system of the State Taxation Bureau of a certain district using SQL statements, when the tax burden rate threshold value selected by the nodes of the tax burden discrimination decision tree for all industries is 2.23%, through experiments, various tax collection, and management. The work has achieved ideal results, providing a more scientific and persuasive argument for the proposal of countermeasure research.


Nanoscale ◽  
2020 ◽  
Vol 12 (39) ◽  
pp. 20118-20130 ◽  
Author(s):  
Yike Liu ◽  
Chenguo Hu

New technologies such as the Internet of Things and big data have become the strategic focus of national development in the world.


2020 ◽  
Vol 7 (2) ◽  
pp. 205395172095035 ◽  
Author(s):  
Hugo Jeanningros ◽  
Liz McFall

As Big Data, the Internet of Things and insurance collide, so too, do the best and the worst of our futures. Insurance is summoned as an example of the interference in our private lives that is already underway everywhere. In this paper, we pause to reflect on this argument. Can changes in the way insurance measures the value of behaviour really serve as an example of the individual and social harms of datafication? How do we know? Insurance is a mathematical relationship staged between individuals and groups, between risk and uncertainty, between distribution and assessment, between the value of sharing and the sharing of value. We use the case study of Discovery International, owner of Vitality, the market leading brand in behavioural insurance to consider how behaviour is being branded and how the brand behaves.


10.6036/10342 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 561-562
Author(s):  
MIKEL NIÑO

The Smart Industry has been developing has been developing at an accelerated pace since the beginning of the last decade, driven by of the last decade, driven by the by the emergence of technologies such as the Internet of Things, Compute of Things, Cloud Computing and Big Data Cloud Computing and Big Data technologies, as well as their connection and Big Data technologies, as well as their connection with machine learning algorithms for predictive data analysis [1] of data [1].


Author(s):  
Dhai Eddine Salhi ◽  
Abelkamel Tari ◽  
Mohand Tahar Kechadi

In the world of the internet of things (IoT), many connected objects generate an enormous amount of data. This data is used to analyze and make decisions about specific phenomena. If an object generates wrong data, it will influence the analysis of this collected data and the decision later. A forensics analysis is necessary to detect IoT nodes that are failing. This paper deals with a problem: the detection of these nodes, which generate erroneous data. The study starts to collect in a cloud computing server temperature measurements (the case study); using temperature sensors, the communication of the nodes is based on the HIP (host identity protocol). The detection is made using a data mining classification technique, in order to group the connected objects according to the collected measurements. At the end of the study, very good results were found, which opens the door to further studies.


2020 ◽  
Vol 10 (2) ◽  
pp. 106-112
Author(s):  
Ahmed Burhan Mohammed

    One of the most important topics in the last decade is the Big Data (BD) and how to link it and benefit from its consumption in different fields, included as the introduction in this research analysis of the BD belonging to devices of the Internet of Things. The concept of managing objects and exploring devices is connected to the Internet and sensors deployed in the world, all these devices are pumping a lot of data through the Internet of Things (IoT) into the world. In order to make the right decisions for people and things, BD using data mining techniques and machine language algorithms help make decisions. The Internet of Things that insert large amounts of data need to be studied, analysed and disseminated in order to access valuable, useful and bug-free information for the purpose of making the right decision and avoiding problems. In this paper, two clustering algorithms simple K-means and self-organising map (SOM) in IoT are presented. Next, comparing the clustering models’ output in the IoT data set that improved the SOM is better than K-means, but it is slower in creating the model.   Keywords: Internet of things (IoT), big data, machine learning, filtered cluster, K-means, SOM.    


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