Integration of Cloud Computing with Internet of Things. Группа авторов
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make policies about IoT security regarding the locations of cameras, conceivable devices, sensors, measuring instruments, or meters to make it effective.
1.5.4 Semantic IoT vs Machine Learning
The Machine Learners (MLs) are essential components in the field of pattern recognition, classification, and regression analysis. Over the years, several MLs have been efficiently applied in the field of power management, speech, and speaker identification, emotion recognition, etc. [28–32]. The integration and coordination of SIoT with MLs has been often discussed in the literature involving pervasive and ubiquitous computing, ambient intelligence, wireless sensor networks, artificial intelligence, human–computer interaction, cognitive science, etc. The multi-layered back-propagating Neural Networks have been effectively utilized to identify human movements such as sitting, walking, running, etc. in smart home applications. Similarly, identification ML models such as the Naive Base Classifiers, Bayesian networks, Support Vector Machines, K-Nearest Neighbor, Hidden Markov Model, etc. have been efficiently applied in the field of context-aware search systems, home automation, navigation systems, etc. in IoT domains.
The Integration and coordination of SIoT and MLs arguably increase the financial health of an industry or business. It requires the choice of specific words or vocabularies to suitably represent a set of concepts. The choice aims to bridge the semantic gap that exists among machines in IoT platform. However, many industries in the existing structure act superficially, thus unable to transform the company into a true profit-oriented entity in reality. For example, the inclusion of Artificial Intelligence in a fast-food chain allows the planning of the diet charts based on the recommendation of user habits. It helps to suggest add-on items based on the current selection, the restaurant traffic, or environmental conditions, weather, or time of a day. The integration of artificial intelligence, SIoT, machine learning, modern analytic models, etc. requires to be embedded with the lifecycle of a customer frequently and completely. For example, the satisfaction level of a customer can be enhanced by displaying his or her name, preferences, frequent visits, etc. on the menu chart makes the client feel proud and special. Efficient handling of a customer’s behaviors, interests, and future intentions can provide many intelligent inputs to the food industry in real-time. Similarly, the AI-powered chat-bouts help the customers with the user-friendly experience to boost revenue due to personalization. It has been observed that most of the consumers are motivated to choose a product of a company that recognizes, remembers, and values his or her association with the product. This way, it is possible to predict a customer’s next move, by upgrading and feeding SI data consistently in an IoT platform. This feedback provides the simulating engines or MLs an edge for new developments in this field with better outcomes. Ultimately, the revenue increases, productivity improves, operational expenses reduced, personalization enhanced at a large scale that leads to better customer experiences. The SI automated IoT embedded machines with intelligence assists millions of smart models functioning concurrently that benefits both the customers and service providers.
1.6 Conclusions
Internet of Things (IoT) application in smart homes or cities, workplace, agriculture, transportation, healthcare, artificial intelligence, Cognitive Science, Blockchain, Micro-service Architecture, Robotic Process, Automation, Quantum Computing are all concepts gaining attention in recent years in public, private, and corporate worlds due to media publicity and efficacy. With its growing interest on everybody and everyday applications, it helps people enjoy self-driving cars, use wearables for efficiency and timely assistance, plan taxi services or business meetings, and so on. The IoT application domains have covered all sectors, industries, and every sphere of life today, thus thrive to boost the financial health of the world. It has begun to shape the future world with a unique perspective never seen before in the history of humanity. With these intuitions, this paper elaborates several factors concerned to IoT world that rule and dominate the world today in the current scenario.
References
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