Volume-9 , Issue-4 , Aug 2021, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Smell Print: CMS-SEN Model for Odor Perception

  Prathyusha Kanakam, ASN Chakravarthy

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.1-7, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Smell printing is a novel technical term that integrated with two intra activities - odor analysis and odor encoding. The step of Smell Printing into various applications proves its greater influence towards the solution of many problems like human identification, finding severity of pollutants in the environment, gas leakages, food spoilages and many more. The odor is the unique biological feature that any specimen on the universe holds and they can be identified by their distinctive trademark which has become the core idea behind the work. This work illustrates the study of odor, criteria of odor of a specific object and the process of encoding chemicals structure of odorants. Comparison is made between two traditional olfaction systems of humans and canines based on the receptors they possess individually which gives the clarification of olfaction in the identification of any specimen. This work also projects the Cogno Monitoring System (CMS) by combining the typical traits of both the olfaction systems with Sensing-Encoding-Notifying (SEN) Model.

  Key-Words / Index Term
  Odorants; Odor analysis; Odor encoding; Smell-print; Receptors

  References
  [1] R. Lathwal, V.K. Saroha, "A Study on Biometric Technology and Access Control System: Network Security", International Journal of Computer Sciences and Engineering, Vol.2, Issue.7, pp.31-35, 2014.
  [2] N.Krishnaraj, "Feature Extraction Techniques in Keystroke Dynamics for Securing Personal Devices", International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.98-101, 2015.
  [3] CG Igiri, OE Taylor, Orji Friday, "Human Activity Recognition Using Smartphones Sensors for Ambient Assisted Living", International Journal of Computer Sciences and Engineering, Vol.9, Issue.2, pp.5-11, 2021.
  [4] Bonalle,S. David,G. Salow, "Method and system for smellprint recognition biometrics on a smartcard." U.S. Patent No. 7,314,165. 1 Jan. 2008.
  [5] V. Cuzuel, G. Cognon,I. Rivals, C. Sauleau,F. Heulard,D. Thiébaut, J. Vial, "Origin, analytical characterization, and use of human odor in forensics." Journal of forensic sciences Vol. 62, Issue. 2, pp.330-350., 2017.
  [6] Gardner, W. Julian , Hyun Woo Shin, L. Hines. Evor, "An electronic nose system to diagnose illness." Sensors and Actuators B: Chemical Vol. 70, Issue. (1-3), pp.19-24. 2000.
  [7] Jenkins, Eileen K., Mallory T. DeChant, Erin B Perry, "When the nose doesn’t know: Canine olfactory function associated with health, management, and potential links to microbiota." Frontiers in veterinary science 5, p.56. 2018.
  [8] Kanakam, Prathyusha, S. Mahaboob Hussain, A. S. N. Chakravarthy, "Cogno-Detective System for Body Odor Sensing." In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-3. IEEE, 2017.
  [9] Kanakam, Prathyusha, S. Mahaboob Hussain,A. S. N. Chakravarthy,"Electronic noses: Forestalling fire disasters: A technique to prevent false fire alarms and fatal casualties." In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-6. IEEE, 2015.
  [10] Kanakam, Prathyusha, S. Mahaboob Hussain, A. S. N. Chakravarthy, "Smart-Trace: A Future Security Aid Using Body Odor Sensing of an Individual with IoT." In Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), pp. 26-27. 2018.
  [11] Keller, E. Paul, J. Lars Kangas, H. Lars Liden, Sherif Hashem, T. Richard Kouzes, "Electronic noses and their applications." In World Congress on Neural Networks (WCNN), pp. 928-931. 1995.
  [12] Prathyusha, Kanakam, A. S. N. Chakravarthy, "Chemical Sensing Through Cogno-Monitoring System for Air Quality Evaluation." In Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough, pp. 27-37. Springer, Cham, 2020.
  [13] Seesaard, Thara, S. Seaon, P. Lorwongtragool, T. Kerdcharoen, "On-cloth wearable E-nose for monitoring and discrimination of body odor signature." In 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1-5. IEEE, 2014.
  [14] Smeets, A.M. Monique, A.E. Egge Rosing, M. Doris Jacobs, Ewoud van Velzen, Jean H. Koek, Cor Blonk, Ilse Gortemaker et al. "Chemical fingerprints of emotional body odor." Metabolites Vol. 10,Issue. 3 p. 84. 2020.
  [15] Thomas-Danguin, Thierry, Charlotte Sinding, Sébastien Romagny, Fouzia El Mountassir, Boriana Atanasova, Elodie Le Berre, Anne-Marie Le Bon, Gérard Coureaud, "The perception of odor objects in everyday life: a review on the processing of odor mixtures." Frontiers in psychology 5.,p. 504. 2014 .
  [16] Wongchoosuk, Chatchawal, Taweesak Youngrod, Hirihattaya Phetmung, Mario Lutz, Theeraporn Puntheeranurak, Teerakiat Kerdcharoen, "Identification of people from armpit odor region using networked electronic nose." In 2011 Defense Science Research Conference and Expo (DSR), pp. 1-4. IEEE, 2011.

  Citation
  Prathyusha Kanakam, ASN Chakravarthy, "Smell Print: CMS-SEN Model for Odor Perception," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.1-7, 2021

• Open Access   Article

  Evidence Recovery using EnCase and FTK in Forensic Computing Investigation

  Narayan P. Bhosale

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.8-13, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Evidence retrieval from digital devices using tools of forensic computing is always challenging job and an exciting new frontier awaits criminologists in the cyberspace age. The forensic tools such as EnCase and FTK that have been frequently utilized to examine cybercrime are the topic of this research. There are always digital traces of every contact. It is difficult to uncover evidence in cyber-evidence investigations. The recovery of numerous types of files and photos has been achieved after using FTK and EnCase. If AccessData FTK 7.1.0 is used in conjunction with EnCase v8.08, then the accuracy of carving evidence is improved over a demo version of FTK. In this way, both tools are useful to cyber investigators for minimal data acquisition, which is applicable to potential evidence recovery from digital media. Cyber Forensics or Forensic Computing, then, is a combination of methodology and technical knowledge for early bird investigators with legal obligations.

  Key-Words / Index Term
  Forensic Computing, Digital Evidence, FTK, Encase.

  References
  [1] Albert Marcella Jr., Doug Menendez, “Cyber Forensics: A Field Manual for Collecting, Examining, and Preserving Evidence of Computer Crimes,” Auerbach Publications, Taylor & Francis, 2nd Edition, ISBN-10 : 0849383285, pp.1-528, 2007.
  [2] Hanan Hibshi, Timothy Vidas, Lorrie Cranor, “Usability of Forensics Tools: A User Study,” ixth International Conference on IT Security Incident Management and IT Forensics, IEEE, ISBN: 978-0-7695-4403-8, Germany, pp.-81-91, 2011.
  [3] Kambiz Ghazinour et al., “A study on digital forensic tools,” IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), USA, pp.1-7, 2017. DOI:10.1109/ICPCSI.2017.8392304.
  [4] Samuel Norris, “Digital Evidence and Computer Crime,” 3rd edition, Elsevier, pp.-1-244, 2011.
  [5] Vedanta Kapoor et al.,“Digital Forensics Tools,” International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume-9, Issue-2, pp.3965-3969, 2019.
  [6] Hailye Tekleselase Woldemichael, “Emerging Cyber Security Threats in Organization,” International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.6, pp.7-10, 2019.

  Citation
  Narayan P. Bhosale, "Evidence Recovery using EnCase and FTK in Forensic Computing Investigation," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.8-13, 2021

• Open Access   Article

  A Prediction Model addressing the Changes and Challenges in Adopting SEaaS Model during COVID-19

  Justus Selwyn, Christy Dharmasingh

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.14-20, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  During this world-wide universal lockdown period, software companies are trying to find a possible solution to keep up their everyday activities. Software Engineering as a Service (SEaaS) will be able to provide support to the processes which they have been doing in the software development lifecycle (SDLC). Elaborately, SEaaS model has the ability to intelligently provision the design, development, testing, deployment and maintenance of the software. Pre-COVID-19 and conventionally, software design and development happens within the corporate’s boundary which always requires up-front investments, planning, procurement of infrastructure, purchase of software tools and space for development. During this COVID-19 lock-down and post this period, SEaaS will take care of the complete SDLC processes, and provision an end-to-end design and development model. This migration of on-campus development to off-campus (SEaaS cloud based) development is not without challenges and the changes that are needed at the SDLC process level. This paper addresses the changes that will be needed and the challenges that need to be addressed when software development enterprises adopt their design and development processes and environment to SEaaS model. Also a machine learning model predicting the scope of SEaaS in the Off-Campus is built, scoring a significant fit in the prediction model.

  Key-Words / Index Term
  Software Engineering, COVID-19, Software as a Service, Prediction Models

  References
  [1] ‘Covid-19 pandemic set to reshape nature of work’, Hindustan Times, https://www.hindustantimes.com/india-news/covid-19-pandemic-set-to-reshape-nature-of-work/story-h5fx3c5WA90xgIcT5rrgAP.html, accessed March, 2020
  [2] Krebs R, Momm C, Konev S., “Architectural concerns in multi-tenant SaaS applications”, In the Proceedings of the 2nd International Conference on Cloud Computing and Service Science, Shanghai, pp. 426–431, 2012
  [3] Nagappan, M., Mirakhorli, M. “Big(ger) Data in Software Engineering”, In the Proceedings of the International Conference Software Engineering, pp. 957-958, 2015
  [4] Bezerra RO, Rabelo RJ, Cancian MH., “Supporting SOA resilience in virtual enterprises, collaborative networks of cognitive systems”, In the Proceedings of the 19th IFIP WG 5.5 working conference on virtual enterprises, Cardiff, UK, 2018.
  [5] Rastogi, V. “Software Development Life Cycle Models-Comparison, Consequences”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 1, pp. 168- 172, 2015
  [6] Neelu Lalband, D. Kavitha, “Software Engineering for Smart Healthcare Applications”, International Journal of Innovative Technology and Exploring Engineering, Vol. 8, No. 6S4, 2019.
  [7] Augustsson NP, Nilsson A, Holmström J, Mathiassen L. “Managing digital infrastructures: negotiating control and drift in service provisioning’, International Journal Business Information System, Vol. 30, No. 1, pp. 51–78, 2019
  [8] Cestari JMAP, Loures EFR, Santos EAP, Panetto H. “A capability model for public administration interoperability”, Enterprise Information Systems, 2019.
  [9] “12 Factors To Help You Evaluate Potential Technical Solutions”, https://www.forbes.com/sites/forbestechcouncil/2017/02/09/12-factors-to-help-you-evaluate-potential-technical-solutions/#52e6ce714f66 accepted June 2020.
  [10] Sandanayake, T. ., & Jayangani, P., “Current Trends in Software as a Service (SaaS)”, International Journal for Innovation Education and Research, Vol. 6, No. 2, pp. 221-234, 2018
  [11] Giuliano Casale , Cristina Chest, et. al. “Current and Future Challenges of Software Engineering for Services and Applications”, In the Proceedings of the Cloud Futures: From Distributed to Complete Computing, Madrid, Spain, Vol. 97, pp. 34 – 42, 2016
  [12] Kneuper, R. “Sixty Years of Software Development Life Cycle Models”, IEEE Annals of the History of Computing, Vol. 39, No. 3, pp. 41-54, 2017
  [13] Liu Y Wang Lunyan, Hu Fangyuan, Yuan Lu., “Security Access Control in SaaS Mode Based on Improved RBAC Model”, Journal of Modern Computer, Vol. 15, pp. 81-84, 2017
  [14] Gorton, I., Bener, A., Mockus, A. “Software Engineering for Big Data Systems”, IEEE Software, Vol. 33, No. 2, pp. 32-35, 2016
  [15] ‘The Coronavirus Impact: How COVID-19 has affected Tech and Software Industry worldwide, A Report from SEASIA’, https://www.seasiainfotech.com/blog/the-coronavirus-impact-how-covid-19-has-affected-tech-and-software-industry-worldwide/ accessed April, 2020
  [16] S. Schneider, A. Sunyaev, “Determinant Factors of Cloud-Sourcing Decisions: Reflecting on the IT Outsourcing Literature in the Era of Cloud Computing”, Journal of Information Technology, Vol. 31, No. 1, pp. 1–31, 2016
  [17] Euripidis L., M. Janssen, Ianislav M. “Determinants of Software-As-A-Service Benefits and Impact on Firm Performance”, Decision Support System, Vol. 117, pp. 38-27, 2019

  Citation
  Justus Selwyn, Christy Dharmasingh, "A Prediction Model addressing the Changes and Challenges in Adopting SEaaS Model during COVID-19," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.14-20, 2021

• Open Access   Article

  Intelligent Chatbot for Solving Mathematical Problems: MAC

  S. Korouhanba Khuman, Gayatri Kalita, Basab Nath

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.21-24, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  With the growing internet and online resources, e-learning is the new era in today`s education system, and many students are nowadays engaging themselves in a more online course and browsing the internet to find answers to their respective queries, but as we all know, the internet is a vast place where numerous amount of resources are present, and sometimes you don`t obtain a peculiar explanation to your queries, and due to which sometimes people get tired of searching, and they tend to prefer a developed application where they can solve their respective queries effectively, so in this paperwork, we developed a standalone chatbot application designed for a singular subject which is mathematics where students sometimes get confused and even when searching online they don`t acquire any decent answers, so by using C programming, natural language, and string compare feature we developed our intelligent chatbot which will help the students to learn and solve their complex queries more efficiently and effectively.

  Key-Words / Index Term
  Chatbot, E-learning, C Programming, Mathematics, CLI

  References
  [1] G. Vladova, J. Haase, L. S. Rüdian, N. Pinkwart, “Educational Chatbot with Learning Avatar for Personalization,” In 25th Americas Conference on Information System, Mexico, pp.1-5, 2019.
  [2] G. Hiremath, A. Hajare, P. Bhosale, R. Nanaware, Dr. K. S. Wagh, “Chatbot for education system,” International Journal of Advance Research, Ideas and Innovations in Technology, Vol. 4, Issue. 3, pp.37-43, 2018.
  [3] F. Colace, M. D. Santo, M. Lombardi, F. Pascale, A. Pietrosanto, “Chatbot for E-Learning: A Case of Study,” International Journal of Mechanical Engineering and Robotics Research, Vol. 7, Issue. 5, pp.528-533, 2018.
  [4] A. O. Priadko, K. P. Osadch , V. S. Kruhlyk, V. A. Rakovych, “Development of a chatbot for informing students of the schedule,” CS&SE@SW 2019 2nd Student Workshop on Computer Science & Software Engineering, Ukraine, pp.128-137, 2020.
  [5] A. Mondal, M. Dey, D. Das, S. Nagpal and K. Garda, “Chatbot: An automated conversation system for the educational domain,” International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Thailand, pp.1-5, 2018.
  [6] G. Molnár, Z. Sz?ts, “The Role of Chatbots in Formal Education,” IEEE 16th International Symposium on Intelligent Systems and Informatics, Serbia, pp.000197-000202, 2018.
  [7] M. Dahiya, “A Tool of Conversation: Chatbot,” International Journal of Computer Sciences and Engineering, Vol. 5, Issue. 5, pp.158-161, 2017.
  [8] B. Setiaji, F. W. Wibowo, “Chatbot Using A Knowledge in Database,” IEEE 7th International Conference on Intelligent Systems, Modelling and Simulation, Thailand, pp. 72-77, 2016.

  Citation
  S. Korouhanba Khuman, Gayatri Kalita, Basab Nath, "Intelligent Chatbot for Solving Mathematical Problems: MAC," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.21-24, 2021

• Open Access   Article

  Mobilegeddon: Why Google Forcing Websites to Go Mobile-Friendly

  Ajay D. Sirsat

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.25-28, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Search improvement is that the technique by that we tend to get a higher page ranking for your websites in search result pages. several of the users use Google to seem for one thing, and if they’re lucky they’re progressing to catch on at intervals the best of few results came back by the Google search. With the increase in mobile devices, the search on mobile devices needs to be relevant and in a very timely manner. throughout this analysis, an effort has been created to understand the results of the mobilegeddon’s logic of mobile-friendliness for search ranking, which changes the strategy your website is stratified in mobile search. throughout this study, an in-depth understanding of the search analytics report that was found at intervals the Google search console and wholly completely different web surveys taken by organizations taken into thought to understand the changes in mobile search ranking drastically changed due to the advancement at intervals the mobilegeddon rule. This analysis helped to get utterly completely different aspects of Mobilegeddon’s rules that junction rectifier to large updates of mobile search rankings and results, and formally made-up the approach for the mobile-first era.

  Key-Words / Index Term
  Mobilegeddon, Mobile First-Era, Google’s plan to go mobile-friendly, Why Google forcing websites to go mobile-friendly, Mobile Ad Market, Conversion rate, PPC Revenue

  References
  [1] G. Sterling, “It’s Official: Google says more searches now on Mobile than on Desktop”, Search Engine Land, 5-5, 2015.
  [2] R. Cellan-Jones, “Google’s Mobilegeddon”, BBC News, 21-04, 2015.
  [3] S. Rajan, “Google’s Mobilegeddon and Its Effects”, Suyati Technologies Blog, 08-07, 2016.
  [4] D.P. McCleskey, “Mobilegeddon: Government Edition``, Digital.gov, 15-4, 2015.
  [5] P. Bilic, “Search Algorithms, hidden labor, and information control”, International Journal of Big Data and Society, pp.1-9, 2016.
  [6] P. Reinhart, “Mobilegeddon: A Complete Guide to Google’s Mobile-Friendly Update”, Search Engine Journal, 1-5, 2015.
  [7] Dr. P.J. Meyers, “7 Days After Mobilegeddon: How Far Did the Sky Fall”, Moz Blog, 22-04, 2015.
  [8] M. Graham, J. Elias, “How Google’s $150 billion advertising business works”, CNBC Tech, 18-05, 2021.
  [9] J. Clement, “Global Mobile Data Traffic from 2017 to 2022” Statista Internet Reach and Traffic Survey, 18-2, 2020.
  [10] E. Parisar, “The Filter Bubble - What The Internet Is Hiding From You”, Penguin Books Ltd., Ebook 12-05, 2011
  [11] R. Winkler, “Google Gives Boost to Mobile-Friendly Sites”, The Wall Street Journal, .21-04, 2015.
  [12] M.J. Hiemstra, “The Aftermath of Mobilegeddon and What it Means for Marketers”, Landing Page Optimization, Unbounce, 23-07, 2015
  [13] eMarketer, “Driven by Facebook and Google, Mobile Ad Market Soars 105% in 2013 ”, Insider Intelligence Technology Blog, 19-04, 2014.
  [14] Statista Research Department, “Google Global Mobile Advertising Revenues 2014-2018”, Statista, 26-10, 2016.

  Citation
  Ajay D. Sirsat, "Mobilegeddon: Why Google Forcing Websites to Go Mobile-Friendly," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.25-28, 2021

• Open Access   Article

  Youtube Comment Analyzer

  Mohammed Arsalan Khan, Sumit Baraskar, Anshul Garg, Shineyu Khanna, Asha M.Pawar

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.29-31, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  The goal of this study article is to assist a content creator or anyone who wants to know about the audience`s thoughts, emotions, on a particular video. We have studied the literature papers first and then identified the basic functionality of it and then we get to know about its dimensions from the paper.

  Key-Words / Index Term
  Sentiment Analysis; YouTube comment

  References
  [1] Shi Yuan, Junjie Wu, Lihong Wang and Qing Wang, "A Hybrid Method for Multi-class Sentiment Analysis of Micro-blogs", ISBN- 978-1-5090-2842-9, 2016.
  [2] Neethu M S and Rajasree R, “Sentiment Analysis in Youtube using Machine Learning Techniques”
  [3] Aliza Sarlan, Chayanit Nadam, and Shuib Basri, "Youtube Sentiment Analysis", 2014 International Conference on Information Technology and Multimedia (ICIMU), Putrajaya, Malaysia November 18 – 20, 2014.
  [4] B. Gupta, M. Negi, K. Vishwakarma, G. Rawat, and P. Bandhani, "Study of Youtube Sentiment Analysis using Machine Learning Algorithms on Python," Int. J. Comput. Appl., vol. 165, no. 9, pp. 29–34, May 2017.
  [5] “Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing," 2019 IEEE Comput. Soc. Annu. Symp. On, p. 134, 2019. Opinion Mining”, Kluwer Academic Publishers. Printed in the Netherlands, 2006.
  [6]Hearst, M., “Direction-based text interpretation as an information access refinement”, In Paul Jacobs, editor, Text-Based Intelligent Systems. Lawrence Erlbaum Associates, 1992.
  [7]Das, S., and Chen, M., “Yahoo! for Amazon: Extracting market sentiment from stock message boards”, In Proc. of the 8th Asia Pacific Finance Association Annual Conference (APFA 2001), 2001. unsupervised classification of reviews”. In Proc. of the ACL, 2002.
  [8]Argamon-Engelson, S., Koppel, M., and Avneri, G., “Style-based text categorization: What newspaper am I reading? ”, In Proc. of the AAAI Workshop on Text Categorization, pages 1–4, 1998.
  [9]Pang, B. & Lee, L., “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts”, Association of Computational Linguistics (ACL), 2004.
  [10]Jin, W., & HO H. H., “A novel lexicalized HMM-based learning framework for web opinion mining”, Proceedings of the 26th Annual International Conference on Machine Learning. Montreal, Quebec, Canada, ACM: 465-472, 2009.
  [11]Brody, S., & Elhadad, N., “An unsupervised aspect-sentiment model for online reviews”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Los Angeles, California, Association for Computational Linguistics: 804-812, 2010.
  [12]Wiebe, J., Wilson, T., and Cardie, C., “Annotating expressions of opinions and emotions in language”. Language Resources and Evaluation, 2005.

  Citation
  Mohammed Arsalan Khan, Sumit Baraskar, Anshul Garg, Shineyu Khanna, Asha M.Pawar, "Youtube Comment Analyzer," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.29-31, 2021

• Open Access   Article

  IoT and Personal Data Protection

  Jhanvi Zala

  Review Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.32-35, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  IoT is the most evolving technology ever. In short period of time, there are so many devices related to IoT have come into market. Devices like smart watches, lenses, fitness brand, microchips and other more. IoT means a physical device powered by the Internet with an embedded system of sensors, actuators and a networking device that exchanges and collects data. All the devices need different application to run. Any user who uses this application has to enter the data to enhance service of the device. Similarly, to see the security and nodes of IoT devices that have been compromised, collect all this information of malicious activity, it has become a top priority of the IoT network. If we insert data from any application into the device, the user shares this data. The user`s privacy is at risk whenever the user shares data. So this paper presents how you can protect a user`s personal data. Inside this paper we will look at different methods of how to protect personal data. Personal data administrators are being used to monitor and handle user data exchange, as well as inference prevention techniques. It can also equate the PDPA to its counterparts in other countries using a comparative approach.

  Key-Words / Index Term
  personal data, privacy, PDPA

  References
  [1] SN. Friedman, J. B. Rowe, D. J. Reinkensmeyer, M Bachman,The manumeter: A wearable device for monitoring daily use of the wrist and fingers. IEEE journal of biomedical and health informatics, 18(6), 1804-1812, 2014.
  [2] Department of computer science, Bioengineering, Robotics and systems Engineering University of Genoa, Italy. A Framework for Personal Data Protection in IoT.
  [3] Z. Huo,M. Xiaofeng, Z. Rui, "Feel free to check-in: Privacy alert against hidden location inference attacks in GeoSNs.", International Conference on Database Systems for Advanced Applications, Springer Berlin Heidelberg, 2013.
  [4] Department of Computing and Cyber Security, Texas A&M University, San Antonio, TX 78224, USA. IoT Privacy and Security: Challenges and Solutions.
  [5] Meng, Y.; Zhang, W.; Zhu, H.; Shen, X.S. Securing consumer IoT in the smart home: Architecture, challenges, and countermeasures. IEEE Wirel. Commun. 25, 53–59, 2018.
  [6] Siby, S.; Maiti, R.R.; Tippenhauer, N.O. Iotscanner: Detecting privacy threats in IoT neighborhoods. In Proceedings of the 3rd ACM International Workshop on IoT Privacy, Trust, and Security, Abu Dhabi United Arab Emirates, pp. 23–30, 2 April 2017.
  [7] Hassan, W.H. Current research on Internet of Things (IoT) security: A survey. Comput. Netw. 148,283–294, 2019.
  [8] Leloglu, E.Areviewof security concerns in Internet of Things. J. Comput. Commun. 5, 121–136, 2016.
  [9] Sadeghi, A.-R.; Wachsmann, C.; Waidner, M. Security and privacy challenges in industrial internet of things. In Proceedings of the 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 8–12 pp. 1–6, June 2015.
  [10] Bertino, E., Choo, K.-K. R., Georgakopolous, D., & Nepal, S. (2016). Internet of Things (IoT): Smart and Secure Service Delovery. ACM Transactions on Internet Technology, 16(4), 1–7. http://doi.org/10.1145/3013520
  [11] Raza, S., Helgason, T., Papadimitratos, P., & Voigt, T. (2017). SecureSense: End-to-end secure communication architecture for the cloud-connected internet of things. Future Generation Computer Systems. http://doi.org/10.1016/j.future.2017.06.008
  [12] Z. Cai, Z. He, X. Guan, Y. Li, "Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks", IEEE Transactions on Dependable and Secure Computing, 5971(c), 2016.
  [13] Y. Sun, L. Yin, L. Liu, S. Xin, "Toward inference attacks for kanonymity", Personal and Ubiquitous Computing, 18(8), 1871- 1880, 2014
  [14] S. Chakraborty, C. Shen, K. R. Raghavan, Y. Shoukry, M. Millar, M. Srivastava, "ipShield : A Framework For Enforcing Context-Aware Privacy", In 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), pp. 143-156. 2014.
  [15] The Influence of Privacy and Security on the Future of IoT Mihaela M?d?lina ANGHEL, Petru IANC, Marian ILEANA, Laura Iulia MODI Bucharest University of Economic Studis, Romania Informatica Economic? vol. 24, no. 2/202
  [16] H. Orman, Blockchain: The emperors new pki?, IEEE Internet Computing, vol. 22, no. 2, pp. 23–28, 2018.

  Citation
  Jhanvi Zala, "IoT and Personal Data Protection," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.32-35, 2021

• Open Access   Article

  Exploiting Packet Travel Time and Energy factor in delay sensitive application of MANET

  Bekan Kitaw

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.9 , Issue.4 , pp.36-43, Aug-2021

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  There are various threats in mobile ad-hoc networks. Among these threats packet drop and delays are serious threats that have to be controlled or reduced if not. Delay sensitive applications are typical applications of mobile ad-hoc network. These applications are sensitive to delay (requires low end-to-end delay) as their name suggests and else results in the data that they carry being meaningless and less effective. Apart from this, since they hold sensitive information there should be no or low packet loss rate. Typical examples of these applications are live video streaming, voice over IP, emergency applications, and multimedia teleconferencing. In this paper, we are trying to minimize the delay and packet drop caused by the routing protocols used in this application. For our simulation work, we uses the multiple-path selection protocol called AOMDV, and then we compare it with the two most recent works using the NS2 network simulator and the Xgraph for evaluation purposes.

  Key-Words / Index Term
  AOMDV, Delay, Disjoint, Teleconferencing, NS2, Xgraph

  References
  [1]. Bulent, T. & Wendi. H., “Mobile Ad hoc Networks: Energy-Efficiency Real-Time Data Communication”, Springer Netherlands, 2006.
  [2]. Chen, L. & Heinzelman, W. B., “A Survey of Routing Protocols that Support QoS in Mobile Ad Hoc Networks”, IEEE Network, Issue 21(6), Pp. 30-38, 2007, doi: 10.1109/MNET.2007.4395108.
  [3]. Garnepudi, P., Damarla, T., Gaddipati, J., & Veeraiah, D., “Proactive, reactive and hybrid multicast routing protocols for Wireless Mesh Networks”, IEEE International Conference on Computational Intelligence and Computing Research, 2013 doi:10.1109/iccic.2013.6724255
  [4]. Abdulkader, M. & S. Abdulkader, “Load Aware and Energy Efficient Approach to Improve Network Connectivity in MANET”, Sam Higginbottom University of agriculture. Technology and sciences. Allahabad, 2017.
  [5]. Sharma, M. Brar, A., “Enhancement in AOMDV Protocol to Reduce Chances of Link Failure in Mobile Adhoc Network”. International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), 4(6), 2015.
  [6]. Singh, B., Baghla, S. & Monga, H., “Mobility models based performance evaluation of AOMDV routing protocol of MANET”, International journal of applied research, 3, Pp. 82-86, 2017.
  [7]. Sivaraman, T. & Karthikeyan, E., “EE-BWA-AOMDV: Energy Efficient and Bandwidth Aware On-demand Multipath Routing protocol for Mobile Ad hoc Networks”, Inter-national Journal of Computer Application, 6(2), Pp. 2250-1797, 2016.
  [8]. Jayalakshmi, V. & Dr. kumar, R., “Multipath fault tolerant routing protocol In MANET”, International Journal on Adhoc Networking System, Issue 2(1), 2012.
  [9]. Ryan, M. A., Nouh, S., Salem, T. M. & Naguib, A. M., “EDA-AODV:Energy and Distance Aware AODV Routing Protocol”, International Journal of Computer Networks and Applications, 5(5), 1, 2018, https://doi.org/10.22247/IJCNA/2018/49518.
  [10]. Perkins, C., Belding-Royer, E., Das, S., “RFC-3561, Ad Hoc on-Demand Distance Vector Routing”, 2003, http://www.faqs.org/rfcs/rfc3561.html.
  [11]. Mahesh K. M. & Samir R. Das., “Ad hoc on-demand multipath distance vector routing. Wireless communications and mobile computing”, Volume 6, Pp. 969–988, 2006.
  [12]. Sayed, E., Ahmed, A., & Saeed, R., “Review of Gateway Selection Schemes for MANET to Internet Connectivity”, 2014.
  [13]. Farooq, H. & Tang Jung, L., “Energy, Traffic Load, and Link Quality Aware Ad-Hoc Routing Protocol for Wireless Sensor Network Based Smart Metering Infrastructure”, International Journal of Distributed Sensor Networks, Article ID 597582, 2013.

  Citation
  Bekan Kitaw, "Exploiting Packet Travel Time and Energy factor in delay sensitive application of MANET," International Journal of Scientific Research in Computer Science and Engineering, Vol.9, Issue.4, pp.36-43, 2021