Special Issue: "Machine Learning Algorithms for Self-organizing Peer-to-Peer (P2P) Network"

Introduction/Call for Papers

In recent years, the internet has been growing rapidly as new services have been implemented in the market every day. However, network traffic generated from the network protocols and applications needs to be categorized which is considered as a critical task in day-to-day network management. Especially, Peer-to-Peer (P2P) network has the largest share of the bandwidth. This large amount of bandwidth has improved the prominence of network traffic engineering. The applications of P2P is growing dramatically, which results in many complex issues such as network congestion and traffic hindrance. More commonly, the effective process of a peer-to-peer network depends on the flexibility of its peer’s communications. However, NATs and traffic filtering have restrict the direct connections between peers on the internet. Modern P2P networks use various methods to solve the issue of restricted connectivity on the Internet. One interesting development is that various overlay networks now support multiple communication protocols to improve resilience and counteract service degradation. On the other hand, various self-reorganization algorithms are recently identified based on the machine learning algorithms to improve the performance, scalability, resilience and reduce the communication cost thereby providing the basis of an efficient neighbor selection scheme for P2P overlays. Moreover, the benefit of reorganization is often lower than the cost incurred in probing and adapting the overlay.

Indicative Topics/Areas

This special issue focus on various machine learning based self-reorganization algorithms to reduce the network traffic, cost and improve the performance, scalability, resilience in P2P networks. The topics include, but not limited to, the following:

Machine learning for resource allocation in P2P networks

Machine learning for neighbor selection in P2P networks

Machine learning for self-organizing P2P networks

Machine learning for processing huge volumes of network-structured data generated from P2P networks

Information retrieval algorithms in P2P networks

Machine learning for Hybrid P2P communication network

Applications of machine learning in P2P networks

Machine learning for P2P network security and privacy

Machine learning for network traffic management in P2P network

Machine learning for mobility management in P2P network

Personalized and Private Peer-to-Peer Machine Learning for P2P network

Reinforcement Learning for Vulnerability Assessment in P2P Networks.

Machine learning for network behavior analysis in P2P network

Machine learning for communication overhead calculation in P2P network

 

Important Dates

Manuscript submission deadline: November 1, 2018

Notification of Acceptance/Rejection/Revision: May 1, 2019

Submission of final revised paper: June 1, 2019

Publication of special issue (tentative):3rd Quarter, 2020


Submission

Manuscripts for a Special Issue should NOT be submitted in duplication to any other Journals. All papers submitted to a Special Issue will undergo the same review process as regular papers. Authors should follow the format of JIT(http://jit.ndhu.edu.tw/ojs/index.php/jit/about/submissions#authorGuidelines) and manuscript should be submitted via online system (https://mc04.manuscriptcentral.com/internet-tech). When submitting papers, authors should specify that the manuscript is for Special Issue on “MLASP2PN 2018”. A copy of the manuscript should also be emailed to the following email: gmanogaran@ucdavis.edu  


Guest Editors:

Dr. Gunasekaran Manogaran is currently working as a Big Data Scientist in University of California, Davis, USA. He received his PhD from the Vellore Institute of Technology University, India. He received his Bachelor of Engineering and Master of Technology from Anna University and Vellore Institute of Technology University respectively. He has worked as a Research Assistant for a project on spatial data mining funded by Indian Council of Medical Research, Government of India. His current research interests include data mining, big data analytics and soft computing. He is the author/co-author of papers in conferences, book chapters and journals. He got an award for young investigator from India and Southeast Asia by Bill and Melinda Gates Foundation, USA. He is a member of International Society for Infectious Diseases and Machine Intelligence Research labs. He is on the reviewer board of several international journals and has been a member of the program committee for several international/national conferences and workshops. He currently serves on Technical Program Committee for 2018 IEEE International Conference on Consumer Electronics (ICCE) in Las Vegas, USA. He is the guest editor for various international journals including IEEE, Springer, Elsevier, Inderscience, IGI, Taylor&Francis and Emerald publishing. He is a Co-Investigator for the project entitled “Agent Based Modeling of HIV epidemic in state of Telangana, India” funded by Pitt Public Health, Pittsburgh University, USA.

Dr. Naveen Chilamkurti is the Editor-in-Chief for International Journal of Wireless Networks and Broadband Technologies, IGI Global. He is currently working as a Senior Lecturer at Department of Computer Science and Computer Engineering, La Trobe University, Australia. He received his PhD from La Trobe University. He has published about 125 journal and conference papers. His current research areas include intelligent transport systems (ITS), wireless multimedia, wireless sensor networks, vehicle to infrastructure, vehicle to vehicle communications, health informatics, mobile communications, WiMAX, mobile security, mobile handover, and RFID. He currently serves on editorial boards of several international journals. He is a senior member of IEEE. He is also an Associate Editor for Wiley IJCS, SCN, Inderscience JETWI, and IJIPT.

Dr. Ching-Hsien Hsu is the Editor-in-Chief of International Journal of Grid and High Performance Computing and International Journal of Big Data Intelligence. He is a professor and the chairman in the CSIE department at National Chung Cheng University, Taiwan; He was distinguished chair professor at Tianjin University of Technology, China, during 2012-2016.  His research includes high performance computing, cloud computing, parallel and distributed systems, big data analytics, ubiquitous/pervasive computing and intelligence. He has published 100 papers in top journals such as IEEE TPDS, IEEE TSC, IEEE TCC, IEEE TETC, IEEE System, IEEE Network, ACM TOMM and book chapters in these areas. Dr. Hsu is serving as editorial board for a number of prestigious journals, including IEEE TSC, IEEE TCC.  He has been acting as an author/co-author or an editor/co-editor of 10 books from Elsevier, Springer, IGI Global, World Scientific and McGraw-Hill.  Dr. Hsu was awarded nine times distinguished award for excellence in research from Chung Hua University.  He is vice chair of IEEE TCCLD, executive committee of IEEE TCSC, Taiwan Association of Cloud Computing and an IEEE senior member.