Integration of Cloud Computing with Internet of Things

1. Das, S.K. and Palo, H.K., Internet of Things (IoT) Application in Green Computing: An Overview, in: Advances in Greener Energy Technologies , pp. 85–102, Springer, Singapore, 2020.

2. Evans, D., The internet of things: How the next evolution of the internet is changing everything , vol. 1, pp. 1–11, CISCO white paper, USA, 2011.

3. Philip, V., Suman, V.K., Menon, V.G., Dhanya, K.A., A review on latest internet of things based healthcare applications. Int. J. Inf. Secur ., 15, 1, 248, 2017.

4. Palo, H.K. and Mohanty, M.N., Wavelet-based feature combination for recognition of emotions. Ain Shams Eng. J ., 9, 1799–1806, 2018.

5. Palo, H.K. and Behera, D., Analysis of Speaker’s Age Using Clustering Approaches with Emotionally Dependent Speech Features, in: Critical Approaches to Information Retrieval Research , pp. 172–197, IGI Global, USA, 2020.

6. Palo, H.K. and Sagar, S., Characterization and Classification of Speech Emotion with Spectrograms, in: 2018 IEEE 8th International Advance Computing Conference (IACC) , IEEE, pp. 309–313, 2018.

7. Palo, H.K., Mohanty, J., Mohanty, M.N., Suresh, L.P., Comparison of similarity among sub-categories of angry speech emotion, in: 2016 International Conference on Emerging Technological Trends (ICETT) , IEEE, pp. 1–6, 2016.

8. Palo, H.K., Spectral prosodic and hybrid features for emotion recognition , PhD thesis, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India, 2018.

9. Palo, H.K., Pattanaik, N., Sahu, B.N., Real-Time Detection of Human Speech Emotion Using ATMEGA. Int. J. Adv. Sci. Technol ., 29, 12s, 1995–1301, 2020.

10. Chui, M., Löffler, M., Roberts, R., The internet of things. McKinsey Q ., 2, 1–9, 2010.

11. He, W., Yan, G., Da Xu, L., Developing vehicular data cloud services in the IoT environment. IEEE Trans. Industr. Inform ., 10, 2, 1587–1595, 2014.

12. Keerti Kumar, M., Shubham, M., Banakar, R.M., Evolution of IoT in smart vehicles: An overview, in: IEEE International Conference on Green Computing and Internet of Things (ICGCIoT) , pp. 804–809, 2015.

13. Galinina, O., Mikhaylov, K., Andreev, S., Turlikov, A., Koucheryavy, Y., Smart home gateway system over Bluetooth low energy with wireless energy transfer capability. EURASIP J. Wirel. Comm ., 178, 1–18, 2015.

14. Nandyala., C.S. and Kim., H.K., Green IoT agriculture and healthcare application (GAHA). Int. J. Smart Home , 10, 4, 289–300, 2016.

15. Wang, K., Wang, Y., Sun, Y., Guo, S., Wu, J., Green industrial internet of things architecture: An energy-efficient perspective. IEEE Commun. Mag ., 54, 12, 48–54, 2016.

16. Gou, Q., Yan, L., Liu, Y., Li, Y., Construction and Strategies in IoT Security System, in: IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing , pp. 1129–1132, 2013.

17. Palo, H.K. and Sarangi, L., Overview of Machine Learners in Classifying of Speech Signals, in: Handbook of Research on Emerging Trends and Applications of Machine Learning , pp. 461–489, IGI Global, USA, 2020.

18. Wang, H.I., Constructing the green campus within the internet of things architecture. Int. J. Distrib. Sens. N ., 10, 3, 1–8, 2014.

19. Curry, E., Guyon, B., Sheridan, C., Donnellan, B., Developing a sustainable IT capability: Lessons from Intel’s Journey. MIS Q. Exec ., 11, 2, 61–74, 2012.

20. Shi, W., Cao, J., Zhang, Q., Li, Y., Xu, L., EC: Vision and Challenges. IEEE Internet Things J ., 3, 5, 637–646, 2016.

21. Sun, X. and Ansari, N., Edge IoT: Mobile EC for the Internet of Things. IEEE Commun. Mag ., 54, 12, 22–29, 2016.

22. EPA Office Building Energy Use Profile (PDF), National Action Plan for Energy Efficiency Sector Collaborative on Energy Efficiency Office Building Energy Use Profile, http://www.epa.gov/cleanenergy/documents/sector-meeting/4bi_officebuilding.pdf, pp. 1-4, August 15, 2007, archived from the original (PDF) on 6 Mar 2009.

23. Stojmenovic, I. and Wen, S., The FC paradigm: Scenarios and security issues, in: 2014 IEEE Federated Conference on Computer Science and Information Systems , pp. 1–8, 2014.

24. Visalakshi, P., Paul, S., Mandal, M., Green Computing, International Journal of Modern Engineering Research (IJMER) , in: Proceedings of the National Conference on Architecture, Software systems and Green computing (NCASG), Paiyanoor (India) , pp. 63–69, 2013.

25. Arshad, R., Zahoor, S., Shah, M.A., Wahid, A., Yu, H., Green IoT: An investigation on energy saving practices for 2020 and beyond. IEEE Access , 5, 667–681, 2017.

26. Ganzha, M., Paprzycki, M., Pawłowski, W., Szmeja, P., Wasielewska, K., Alignment-based semantic translation of geospatial data, in: 3rd IEEE International Conference on Advances in Computing, Communication & Automation (ICACCA)(Fall) , pp. 1–8, 2017.

27. Gyrard, A. and Serrano, M., Connected smart cities: Interoperability with seg 3.0 for the internet of things, in: 30th IEEE International Conference on Advanced Information Networking and Applications Workshops (WAINA) , pp. 796–802, 2016.

28. Palo, H.K. and Mohanty, M.N., Analysis of Speech Emotions Using Dynamics of Prosodic Parameters, in: Cognitive Informatics and Soft Computing , pp. 333–340, Springer, Singapore, 2020.

29. Palo, H.K., Behera, D., Rout, B.C., Comparison of Classifiers for Speech Emotion Recognition (SER) with Discriminative Spectral Features, in: Advances in Intelligent Computing and Communication , pp. 78–85, Springer, Singapore, 2020.

30. Palo, H.K., Chandra, M., Mohanty, M.N., Recognition of Human Speech Emotion Using Variants of Mel-Frequency Cepstral Coefficients, in: Advances in Systems, Control and Automation, Lecture Notes in Electrical Engineering , vol. 442, pp. 491–498, Springer Nature Singapore, 2018.

31. Palo, H.K., Kumar, P., Mohanty, M.N., Emotional Speech Recognition using Optimized Features. Int. J. Res. Electr. Comput. Eng ., 5, 4, 4–9, 2017.

32. Mohanty, M.N. and Palo, H.K., Child emotion recognition using probabilistic neural network with effective features. Measurement , Elsevier, 152, 107369, 2020.

1 *Corresponding author: hemantapalo@soa.ac.in

Richa Goel1*, Seema Sahai1, Gurinder Singh1 and Saurav Lall2

1Amity International Business School, Noida, India

2Azure IoT, Microsoft Seattle, Seattle, Washington, USA

Abstract

In today’s world of Digital Transformation, IoT banking/online transaction is a major point of concern for the user. IoT security plays a vital role to develop the trust of end users for making frequent use of the same. There have been IoT security breaches in recent years. As a result, immoral hackers have ample opportunities to intercept and change or misuse the information. Through this paper we are going to discuss various issuse/problems faced in terms of IoT security along with the measure to be chosen to achieve higher security while going for online transactions.

Keywords:IoT security, digital transformation, breach of trust

Through the World Wide Web, we’ve entered into a brand new era of connectivity. “Things have identities and virtual personalities operating in smart places using intelligent interfaces to connect and communicate inside social, environmental and user contexts [7].” More than twenty billion devices were connected with each other in 2017. This means that the potential risk of cyber-attacks is going on increasingly, equally.