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SafeAssign Originality Report Fall 2021 - Data Science & Big Data Analy (ITS-836-M20) - Full Term • Week 4 Research Paper: Big Data and the Internet of Things

%64Total Score: High riskSharath Kumar Goud Madgula Submission UUID: 35365637-6e4a-cb8e-04ea-562b3891ae67

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Big Data Analytics and Internet of Things Sharath Kumar Goud Madgula

Data Science & Big Data Analytics (ITS-836-M20) - Full Term

Term - Fall 2021

University of the Cumberlands

Big Data Analytics

Introduction The recent information and communication technology advancements, they have played a role in promoting the evolving of conventional manufacturing indus-

try that is computer-aided to manufacturing that is smart-driven. Data analytics within massive manufacturing of data is capable of extracting large business values, however it is also capable of leading to research challenges because of the data types that are heterogeneous, volume that is enormous and actual-time velocity of data manufacturing. In

the process of this evolution, the Internet of Things is known to play a role that is important of linking the physical manufacturing environment to the computing cyberspace plat- forms in addition to algorithms of decision-making, which consequently creates a system of Cyber-Physical. The Industrial IoT that is dedicated to the manufacturing industry, is re- ferred to as a manufacturing IoT (Awotunde, et.al, 2021). Manufacturing IoT mainly comprises of an extensive diversity meant to manufacture sensors, controllers, equipment

in addition to actuators, meters that are smart and RFID that are linked to computing platforms via a wireless or wired links of communication. Then there happens to be a

sudden big surge of data volume traffic that is generated from manufacturing IoT. MIoT data analytics might bring lots of benefits, like enhancing factory production and op-

eration, minimizing machine downtime, enhancing efficiency of supply chain, improving quality of a product in addition to enhancing consumer experience. Nevertheless, there is also lots of challenges within analytics of data in MIoT in the varying stages of the entire data analytics life-cycle. This paper mainly provides an in-depth on the advantages

and complexities of the analytics of big data within the manufacturing Internet of Things. Benefits of big data analytics for MIoT

There happens to be huge data amount generated from the entire chain of manufacturing that comprises of the supply of raw material, manufacturing, distribution of prod-

uct, logistics in addition to consumer support. Big data is supposed to be analyzed thoroughly in order to extract information that is informative and valuable as well.

Therefore, its benefits are: Enhancing factory production and operations – the analytics that are predictive of data manufacturing plus client demand information is capable of helping in enhancing machinery usage consequently improving factory functions. For instance, the demands for a specific commodity are mostly related to seasonal or weather circumstances. Cold wave forecasting might be utilized in making the machinery resources allocation that is pro-active in addition to the prior purchasing of the raw materials in order to achieve the demands that are upsurge

Another benefit is minimizing machinery downtime in that the sensors that are prevalent deployed all through the entire product line are capable of gathering different data re- flecting the status of the machine. For instance, the machinery status analysis of data might assist in identifying the main reason behind a failure which leads to machine down- time reduction. Additionally, sensory data from an assembly line that is automatic might also be utilized in determining excessive machines load in order to have the loads bal- anced amidst multiple machines (Dai, et.al, 2019) Third benefit is enhancing product quality – the market demand analysis as well as consumer necessity analysis might be utilized in enhancing the product design to reflect the product advancements. In the procedure of product manufacturing, the manufacturing data analysis might help in reducing

the defective goods ratio through identification of the root cause. Therefore, this leads to improvement of product quality. Fourth benefit is enhancement of the efficiency of

a supply chain – proliferation of different sensors, tags plus RFID in the process of supplying, manufacturing in addition to transportation ends up generating huge data on the supply chain, which might be utilized in analyzing supply risk, predicting time of delivery and planning optimal route of logistic among others. In addition, the inventory data analy- sis, might minimize the holding expenses and achieve the demands that are dynamic through the establishment of levels of security stock. Big data analytics in the intelligent

manufacturing shops that shops that are IoT enabled might also help in making accurate logistic schedules as well as plans. Through this, the efficiency of the system is capable of being improved greatly (Martinez, 2017). Challenges of big data analytics for manufacturing IoT

MIoT data is known to be having various characteristics which include: heterogeneous sort of data, massive volume, bringing massive social value and business value and gets gen- erated within a real-time fashion.

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These features that are unique, lead to the encountered challenges. These challenges can be summarized as follows: Data acquisition challenges – the acquisition of data tends to address various issues inclusive of data transmission and data gathering which leads to some challenges; Data representation difficulties in that MIoT data is in varying types,

different dimensions and heterogeneous structures. For instance, the manufacturing data might be classified into semi-structured data, unstructured as well as structured

data. The appropriate means of having these data represented is what becomes a core challenge when it comes to MIoT big data analytics. Another challenge is efficient transmis- sion of data in that transmitting the massive data volumes into a storage infrastructure of data within an efficient manner is a challenge because of, bandwidth consumption that is high since big data transmission tends to be a key wireless communication systems bottleneck. There is also energy efficiency which is a core constraints within most wire-

less industrial systems like industrial sensor networks that are wireless. Second challenge is in storage and preprocessing of data. The MIoT generated data results into vari-

ous research complexities within the pre-processing of data. Data Integration- it is of importance to integrate different forms of data in order to have the efficient schemes of

data analytics implemented. Nevertheless, it is a challenge to integrate the different MIoT data types. There is also the issue of redundancy reduction in that, the generated

raw data from MIoT tends to be characterized by spatial and temporal redundancy, which in most occasions leads to data inconsistency impacting the subsequent analysis of data. Mitigating the redundancy of the data present in MIoT data can also be classified as a complexity. Compressing of data as well as data cleaning in that, on top of data redundancy, the MIoT data tends to be noisy as well as erroneous as a result of the defected machinery and mistakes of the sensors. Nevertheless, the massive data volumes makes the

data cleaning process to become more challenging. As a result, it is important to design schemes that are effective in compressing data as well as cleaning the MIoT data errors. Storage of data has an essential role within the analysis of data as well as extraction of value. However, designing of a scalable as well as efficient storage system of data is

also a challenge. Another challenge is persistency as well as reliability of storage of data. The storage systems of data are supposed to make sure there is MIoT data persis-

tency as well as reliability. It becomes a challenge to have the said big data analytics necessities fulfilled while at the same time having the cost balanced mainly because of the massive amounts of data. There is also another challenge which is the issue of scalability – in that apart from the issue of storage reliability, there is also the complexities that are linked to scalability of big data analytics storage systems. The different types of data, the structures that are basically heterogeneous as well as the huge volumes of data sets that belong to the MIoT, results into in-feasibility of the databases that are conventional in the analytics of big data. Therefore, it is important that the paradigms of storage should be adapted so as to backup storage systems of massive scales of data (Zafar, et.al, 2019). Additionally, there is the challenge on its efficiency whereby, to enable the backing up of the extensive figures of queries that are concurrent or accesses that are implemented when in the stage of data analytics, data storage is supposed to achieve the reliability and scala- bility alongside the efficiency necessities all together, which happens to be a real challenge. It is necessary to have the data errors mitigated in addition bro uncertainty because of the features that are erroneous of the MIoT data. Another issue is security in addition to privacy – it is quite a challenge to have privacy and at the same time ensure there is

data security during the process of data analytics. As a result, new data mining schemes of privacy-preserving are supposed to be proposed so as to handle these challenges.

Conclusion

In a situation whereby the entire system of IoT plays the role of a data generated source, then it means that the big data role within IoT tends to be important. The analytics of big data is a tool that is emerging for analyzing the data that is generated by the linked-device within IoT which helps in taking the lead to enhance the making of decisions.

References

Awotunde, J. B., Adeniyi, A. E., Ogundokun, R. O., Ajamu, G. J., & Adebayo, P. O. (2021). MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for

Enhanced Telemedicine Systems. Enhanced Telemedicine and e-Health: Advanced IoT Enabled Soft Computing Framework, 199-220. https://link.springer.com/content/pdf/10.1007/978-3-030-70111-6_10.pdf Dai, H.-N., Wang, H., Xu, G., Wan, J., & Imran, M.

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(2019). Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies. https://arxiv.org/abs/1909.00413? There is a link to

the PDF of this article in the right column of this record under Download. Martinez, R. M. (2017). The Internet of Things: Privacy Issues in a Connected World Remarks

Given at Protecting Virtual You: Individual and Informational Privacy in the Age of Big Data. University of St. Thomas Journal of Law and Public Policy (Minnesota), 11(1), 63–71. Zafar, S., Hussain, R., Hussain, F., & Jangsher, S. (2019). Interplay between Big Spectrum Data and mobile Internet of Things: Current solutions and future chal-

lenges. Computer Networks, 163, 106879. https://www.sciencedirect.com/science/article/pii/S1389128619307376

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Student paper 64%

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Big Data Analytics and Internet of Things Sharath Kumar Goud Madgula

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Big Data Analytics in Internet of Things

Student paper

Data Science & Big Data Analytics (ITS-836-M20) - Full Term

Original source

Data Science & Big Data Analytics (ITS-836-02)

Student paper

University of the Cumberlands

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University of Cumberlands

Student paper

Big Data Analytics

Original source

Big Data Analytics

Student paper

Introduction The recent information and communication technology advancements, they have played a role in promoting the evolving of conventional manufacturing industry that is computer-aided to manufacturing that is smart-driven. Data analytics within massive manufacturing of data is capable of extracting large business values, however it is also capable of leading to research challenges because of the data types that are heterogeneous, volume that is enormous and actual- time velocity of data manufacturing.

Original source

The advances that are recent within information as well as communication technology are known to have played a great role in promoting the evolution of the conventional manufacturing industry that is computer-aided into smart manufac- turing that is data driven Data analytics within massive data manufacturing is capable of extracting business values that are huge in addition it is also capable of resulting into research challenges mainly because of the data types that are het- erogeneous, volume that is enormous as well as velocity that is real-time of data manufacturing

Student paper

In the process of this evolution, the Internet of Things is known to play a role that is important of linking the physical man- ufacturing environment to the computing cyberspace platforms in addition to algorithms of decision-making, which con- sequently creates a system of Cyber-Physical.

Original source

During this evolution, Internet of Things plays an important role in connecting the physical environment of manufacturing to the cyberspace of computing platforms and decision-making algorithms, consequently forming a Cyber-Physical System

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Student paper 82%

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Manufacturing IoT mainly comprises of an extensive diversity meant to manufacture sensors, controllers, equipment in addition to actuators, meters that are smart and RFID that are linked to computing platforms via a wireless or wired links of communication.

Original source

MIoT includes a variety of manufacturing equipment, sensors, actuators, controllers, RFID tags, and smart meters that are connected to computing platforms via wired or wireless communication links

Student paper

Then there happens to be a sudden big surge of data volume traffic that is generated from manufacturing IoT.

Original source

There is a surge of big volume of data traffic generated from Manufacturing of IoT

Student paper

MIoT data analytics might bring lots of benefits, like enhancing factory production and operation, minimizing machine downtime, enhancing efficiency of supply chain, improving quality of a product in addition to enhancing consumer experience.

Original source

The MIoT data analytics comes with various benefits such as improving the factory operation, reducing machine down- time, production, improving product quality, improving customer experience, enhancing supply chain efficiency

Student paper

This paper mainly provides an in-depth on the advantages and complexities of the analytics of big data within the manu- facturing Internet of Things.

Original source

This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT)

Student paper

Benefits of big data analytics for MIoT

Original source

Benefits of Big Data Analytics for MIoT

Student paper

There happens to be huge data amount generated from the entire chain of manufacturing that comprises of the supply of raw material, manufacturing, distribution of product, logistics in addition to consumer support.

Original source

There is an enormous amount of data generated from the whole manufacturing chain consisting of raw material supply, manufacturing, product distribution, logistics, and customer support

Student paper

Big data is supposed to be analyzed thoroughly in order to extract information that is informative and valuable as well.

Original source

The big data has to be analyzed to extract the informative and valuable information

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In the procedure of product manufacturing, the manufacturing data analysis might help in reducing the defective goods ratio through identification of the root cause.

Original source

During the product manufacturing procedure, the analysis of manufacturing data can help to reduce the ratio of defective goods by identifying the root cause

Student paper

Therefore, this leads to improvement of product quality.

Original source

Product quality improvement

Student paper

Big data analytics in the intelligent manufacturing shops that shops that are IoT enabled might also help in making accu- rate logistic schedules as well as plans.

Original source

Also, big data analytics in intelligent manufacturing shops that are IoT-enabled can assist in making accurate logistics plans

Student paper

Challenges of big data analytics for manufacturing IoT

Original source

Challenges of Big Data Analytics for Manufacturing IoT

Student paper

Data representation difficulties in that MIoT data is in varying types, different dimensions and heterogeneous structures.

Original source

However, MIoT data has different types, heterogeneous structures, and various dimensions

Student paper

For instance, the manufacturing data might be classified into semi-structured data, unstructured as well as structured data.

Original source

For example, manufacturing data can be categorized into structured data, semi-structured and unstructured data

Student paper

There is also energy efficiency which is a core constraints within most wireless industrial systems like industrial sensor networks that are wireless.

Original source

And energy efficiency is a critical restriction in many wireless industrial systems like industrial wireless sensor networks

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Second challenge is in storage and preprocessing of data.

Original source

Data storage and data preprocessing challenge

Student paper

Data Integration- it is of importance to integrate different forms of data in order to have the efficient schemes of data an- alytics implemented.

Original source

It is essential to merge different forms of data in order to execute effective data analytics schemes

Student paper

There is also the issue of redundancy reduction in that, the generated raw data from MIoT tends to be characterized by spatial and temporal redundancy, which in most occasions leads to data inconsistency impacting the subsequent analysis of data.

Original source

The raw data generated from MIoT is characterized by temporal and spatial redundancy, which often results in the data inconsistency, consequently affecting the subsequent data analysis

Student paper

Nevertheless, the massive data volumes makes the data cleaning process to become more challenging.

Original source

However, a large amount of data makes the data cleaning process even more challenging

Student paper

Storage of data has an essential role within the analysis of data as well as extraction of value. However, designing of a scalable as well as efficient storage system of data is also a challenge.

Original source

Data storage plays an essential role in data analysis and value extraction However, implementing an efficient and scalable data storage system is a challenge in MIoT

Student paper

Another challenge is persistency as well as reliability of storage of data.

Original source

Data Storage persistency and reliability

Student paper

Another issue is security in addition to privacy – it is quite a challenge to have privacy and at the same time ensure there is data security during the process of data analytics.

Original source

This leads to a lack of privacy and security issues it is difficult to enhance privacy and ensure the security of data during the analytics process

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B., Adeniyi, A. E., Ogundokun, R. O., Ajamu, G. J., & Adebayo, P.

Original source

B., Adeniyi, A E., Ogundokun, R O., Ajamu, G J., & Adebayo, P

Student paper

MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for Enhanced Telemedicine Systems. Enhanced Telemedicine and e-Health: Advanced IoT Enabled Soft Computing Framework, 199-220.

Original source

MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for Enhanced Telemedicine Systems Enhanced Telemedicine and e-Health Advanced IoT Enabled Soft Computing Framework, 199-220

Student paper

Big Data Analytics for Manufacturing Internet of Things: Opportunities, Challenges and Enabling Technologies.

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Big Data Analytics for Manufacturing Internet of Things opportunities, challenges and enabling technologies

Student paper

https://arxiv.org/abs/1909.00413?

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https://arxiv.org/pdf/1909.00413.pdf

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The Internet of Things:

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"The Internet of Things

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Privacy Issues in a Connected World Remarks Given at Protecting Virtual You: Individual and Informational Privacy in the Age of Big Data. University of St. Thomas Journal of Law and Public Policy (Minnesota), 11(1), 63–71.

Original source

Privacy Issues in a Connected World Remarks Given at Protecting Virtual You Individual and Informational Privacy in the Age of Big Data University of St Thomas Journal of Law and Public Policy (Minnesota), 11(1), 63–71

Student paper

Zafar, S., Hussain, R., Hussain, F., & Jangsher, S.

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Zafar, S., Hussain, R., Hussain, F., & Jangsher, S

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Interplay between Big Spectrum Data and mobile Internet of Things:

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Interplay between Big Spectrum Data and Mobile Internet of Things

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Current solutions and future challenges. Computer Networks, 163, 106879.

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Current solutions and future challenges Computer Networks, 163, 106879

Student paper

https://www.sciencedirect.com/science/article/pii/S1389128619307376

Original source

https://www.sciencedirect.com/science/article/pii/S1514032616300125]