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3. Swarm Intelligence and Its Impact on Data Mining and Knowledge Discovery

Author

Murthy, Mantripragada Yaswanth Bhanu, Prasanthi, Katragadda, Kilaru, Mrudula, Kakarla, Sandhya Rani, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Kumar, Amit, editor, Senatore, Sabrina, editor, and Gunjan, Vinit Kumar, editor

Published
2023
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14. Applications, merits and demerits of WSN with IoT: a detailed review

Author

Murthy, Mantripragada Yaswanth Bhanu and Koteswararao, Anne

Abstract

This paper provides an in-depth survey of wireless sensor networks (WSNs) and the Internet of Things (IoT). It explores the diverse applications of IoT and WSN in healthcare, agriculture, transportation, automation, etc. The paper provides the various merits and demerits of IoT and WSN technologies. It also investigates the research work exploiting both IoT and WSN technologies for distinct applications and describes the various advantages of integrating these technologies. The paper provides a comparative study of explored applications based on common performance metrics, publication year, technologies used and results achieved. Through exploring the diverse applications, strengths and weaknesses of IoT and WSN systems, this paper offers thorough knowledge on IoT and WSN technologies to readers, encouraging better and more applications exploiting WSN with IoT.

Published
2024
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16. Deep Learning with Game Theory Assisted Vertical Handover Optimization in a Heterogeneous Network.

Author

Kayikci, Safak, Unnisa, Nazeer, Das, Anupam, Kanna, S. K. Rajesh, Murthy, Mantripragada Yaswanth Bhanu, Preetha, N. S. Ninu, and Brammya, G.

Subjects
DEEP learning, GAME theory, EDUCATIONAL games, RECURRENT neural networks, NEXT generation networks, ENERGY consumption
Abstract

Problem: In next-generation networks, users can optimize or tune their preferences with a seamless transfer of diverse access methodologies for maximizing the Quality of Service (QoS) and cost savings. In these heterogeneous wireless environments, users are prepared with several multimode wireless devices for maximizing media services through several access networks. Such networks may vary regarding energy usage, available bandwidth, technology, coverage range, monetary cost, etc. In recent days, vertical handover has attained higher performance owing to the improvements in mobility models through adopting the Fourth Generation (4G) technologies. On the other hand, these improvements are restricted to some cases, so, it does not offer support for generic mobility. Consequently, diverse strategies were implemented by considering these mobility models. However, it suffers from improper network selection, late and too-early handovers, repeated handovers, high packet loss, etc. Aim: This paper tackles the problem of vertical handover problem in the heterogeneous network using deep learning with game theory. Methods: The proposed model develops a non-cooperative game approach, in which all base stations compete selfishly to transmit at higher power. The overall performance in terms of throughput, handover, energy consumption, and load balancing is attained by optimizing the transmission power by the game theory. For performing this model, the required data like path loss, SINR, data rate, load, etc are generated by the deep learning called Recurrent Neural Network (RNN). Results: From the simulation findings, the handoff probability of the recommended RNN+Game Theory is correspondingly secured at 6.9%, 22.6%, and 8.2% superior to TOPSIS, ABC-PSO, and game theory when taking the time like 5 secs for user velocity as 30 km/h. Conclusion: Results show that the proposed game theoretical approach with deep learning provides a throughput enhancement while reducing the power consumption, in addition, to minimizing the unnecessary handover and balancing the load between base stations. [ABSTRACT FROM AUTHOR]

Published
2023
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