Machine Learning Techniques and Analytics for Cloud Security. Группа авторов
Чтение книги онлайн.
Читать онлайн книгу Machine Learning Techniques and Analytics for Cloud Security - Группа авторов страница 38
Название: Machine Learning Techniques and Analytics for Cloud Security
Автор: Группа авторов
Издательство: John Wiley & Sons Limited
Жанр: Программы
isbn: 9781119764090
isbn:
4.9.3 API Data
At first, we analyze the results obtained from Google Cloud Console and provided them sequentially (Figures 4.10, 4.11, 4.12, and 4.13) below. Figure 4.10 exhibits the operational workbook with API keys, whereas in Figure 4.11, corresponding graphs are provided in different days, as obtained from cloud console. Traffic variations, corresponding error, and median latency for the same days are provided in that plot.
Figure 4.10 API keys operational workbook.
Figure 4.11 API graphs from Google Cloud Console.
Figure 4.12 API data call counter log.
Figure 4.13 API data push and pull traffic data graph.
For different APIs, call log is extremely important in order to note down the busy schedule. Therefore, important APIs like Google Assistance, JavaScript, Directions, YouTube, and custom search are noted. This is shown in Figure 4.12. In this case, it is also important to get view about the variations of traffic, as shown in Figure 4.13.
4.10 Conclusion
The smart real-time prototype developed as explained in this manuscript is really interesting and efficient in terms of the ease of response and accuracy, precisely for old age people, and also for people who needs special care. Total informative interface is designed using Google Cloud Console so that the operation can be possible for the android users. In absence of any help, people can operate basic electrical appliances using the interface, which is precisely voice-controlled. Moreover, it can search the web for your query and read out the results or inform you about the weather when you ask for it. It also smartly helps you to reduce the energy consumption by switching off the device when not needed. At extreme urgent condition, people can take help of cab service, which can save time and life. Interface with personal mosquito server can also be possible through IFTTT server, and that makes the system more robust. The most important part is that it can be integrated with the existing electrical circuit of one’s home, and therefore, it makes huge cost saving.
4.11 Future Scope
The present prototype can be augmented in near future to generate a large complex yet compact system with smart incorporation of artificial intelligence and therefore can be made scalable for embedding with future controllers. This adds with the benefit of less power requirement and ideal for modern home automation system. Several new and essential features can be easily ties up with the proposed system architecture like coffee machine operation, speed control of fan, and operation of air-conditioner. If private Mosquitto server can replace the original public server, then obviously faster response can be expected.
References
1. Alcácer, V. and Cruz-Machado, V., Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems. Eng. Sci. Technol., an International J., 22, 3, 899–919, 2019.
2. Al-wswasi, M., Ivanov, A., Makatsoris, H., A survey on smart automated computer-aided process planning (ACAPP) techniques. Int. J. Adv. Manuf. Technol., 97, 809–832, 2018.
3. Tan, Y., Yang, W., Yoshida, K., Takakuwa, S., Application of IoT-Aided Simulation to Manufacturing Systems in Cyber-Physical System, Machines, 7(2), 1-13, 2019.
4. Boyes, H., Hallaq, B., Cunningham, J., Watson, T., The industrial internet of things (IIoT): An analysis framework. Comput. Ind., 101, 1–12, 2018.
5. Saqlain, M., Piao, M., Shim, Y., Lee, J. Y., Framework of an IoT-based Industrial Data Management for Smart Manufacturing, Journal of Sensor and Actuator Networks, 8(25), 1-21, 2019.
6. Xu, H., Yu, W., Griffith, D., Golmie, N., A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective. IEEE Access, 6, 78238–8259, 2018.
7. Kavitha, B.C. and Vallikannu, R., IoT Based Intelligent Industry Monitoring System. 6th International Conference on Signal Processing and Integrated Networks, Noida, India, 7-8 March 2019.
8. Maple, C., Security and privacy in the internet of things. J. Cyber Policy, 2, 2, 155–184, 2017.
9. Sfar, A.R., Natalizio, E., Challal, Y., Chtourou, Z., A roadmap for security challenges in the Internet of Things. Digital Commun. Networks, 4, 2, 118–137, 2018.
10. Ranjan, R., Sharma, A., Tanwar, S., Voice-Controlled IoT Devices Framework for Smart Home, in: Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Lecture Notes in Networks and Systems, vol. 121 Springer, Singapore, pp. 57–67, 2020.
11. Wu, Z., Qiu, K., Zhang, J., A Smart Microcontroller Architecture for the Internet of Things. Sensors (Basel), 20, 7, 1821, 2020.
12. Mendoza, J.F., Ordóñez, H., Ordóñez, A., Jurado, J.L., Architecture for embedded software in microcontrollers for Internet of Things (IoT) in fog water collection. Proc. Comput. Sci., 109, 1092–1097, 2017.
13. Singh, H.K., Verma, S., Pal, S., Pandey, K., A step towards Home Automation using IOT. 12th International Conference on Contemporary Computing, Noida, India, 8-10 Aug. 2019.
14. Singh, U. and Ansari, M.A., Smart Home Automation System using Internet of Things. 2nd International Conference on Power Energy, Environment and Intelligent Control, Greater Noida, India, 18-19 Oct. 2019.
15. Elsokah, M.M., Saleh, H.H., Ze, A.R., Next Generation Home Automation System Based on Voice Recognition. 6th International СКАЧАТЬ