iosr-JCE   Volume-25 ~ Issue-3 ~ May - June 2023

Abstract: In developing world, virtual network plays an important role where A virtual network connects devices and virtual machines, irrespective of the location of the user is, using software. Virtual networks form their connections through internet. Virtual network has grown massively in recent years and it involves researchers from different fields. This chapter classifies these methods into two categories. The first category concerns strategies used for the network data collection, while the second category deals with strategies used for network data visualization

[1]. Article about Virtual Network Computing which was published on February 1998, Source: IEEE Xplore
[2]. By the reference of Virtual networks Isolations, Performances, and Trends project.
[3]. By the reference of Network functions virtualization on laptop installation and survey by Alireza PiranKashani,
[4]. By the reference of Computer Networks Textbook.
[5]. Technikum-wien UAS; Telecommunication and Internet Technology-vienna, Austria.

Abstract:Smart home automation leveraging Internet of Things (IoT) technologies has gained significant attention in recent years due to its potential to enhance convenience, comfort, and energy efficiency in residential environments. This paper aims to explore the challenges associated with IoT device connectivity in smart home systems and propose effective solutions to mitigate such issues. The implementation of smart home automation relies on interconnected IoT devices, which enable seamless communication and control within the ecosystem. However, the connectivity of these devices can be hindered by various factors, including wireless interference, device compatibility, network congestion, and inadequate network coverage......

[1]. Jayawardana Gubbi, Rajkumar Buyya, Slaven Mantsic, Marimuthu Palaniswami, "Internet of Things (IOT): A vision, architectural elements, and future directions", Future Generation Computer Systems (Elsevier), 2013, pp. 1645-1660.
[2]. Boban Davidovic, Aleksandm Labus, "A SMART HOME SYSTEM BASED ON SENSOR TECHNOLOGY", Electronics and Energetics vol. 29, No 3, September 2016, pp. 451 —460.
[3]. Jayashri Bangali, Arvind Shaligmm, "Energy efficient Smart home based on Wireless Sensor Network using LabVIEW', IJER, vol. 2, Issue 12, 2013, pp. 409-413.
https://iot-analytics.com/number-connected-iot-devices/
[4]. Ahamed, S., Rehman, S. U., & Xhafa, F. (2020). Smart Homes: Architectures, Technologies, and Applications. John Wiley & Sons.
[5]. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.

Abstract: Smart Transportation Systems Have Emerged As A Promising Solution For Managing Urban Traffic Congestion And Improving Overall Transportation Efficiency. One Critical Aspect Of Such Systems Is Accurate Traffic Prediction, Which Enables Proactive Traffic Management Strategies And Enhances User Experience. This Paper Proposes An Approach To Unleash The Power Of Machine Learning Techniques For Enhancing Traffic Prediction In Smart Transportation Systems. This Involves Making Predictions Based On Data From The Previous Year And Data From The Most Recent Year, Which Eventually Yields Accuracy And Mean Square Error. This Involves Making Predictions Based On Data From The Previous Year And Data From The Most Recent Year, Which Eventually Yields Accuracy And Mean Square Error......

Keywords: Intelligent Transport System (ITS), Regression, Traffic, Machine Learning, And Prediction

[1]. Vlahogianni, E. I., M. G. Karlaftis, and J. C. Golias. Optimized and Meta-Optimized Neural Networks for Short Term Traffic Flow Prediction: A Genetic Approach. Transportation Research Part C: Emerging Technologies, Vol. 13, No. 3, 2005, pp. 211–234.
[2]. Machine Learning Approach to Short-Term Traffic Congestion Prediction in a Connected Environment AmrElfar1, Alireza Talebpour2, and Hani S. Mahmassani1, National Academy of Sciences: Transportation Research Board 2018.
[3]. Big data-driven machine learning-enabled traffic flow prediction Fanhui Kong1 Jian Li1 Bin Jiang2 Tianyuan Zhang3 Houbing Song3, 2018.
[4]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845248/
[5]. Hong, J. S., and Kim, Y. J. (2015). Using a multi factor pattern recognition paradigm, the urban TFP system. 16(5),2744–2755, IEEE Transactions on ITS.