Call for Papers for the Special Issue: Journal of Networking and Telecommunications

The Introduction of the Special Issue

Today’s telecommunication networks have become complex interacting systems which involve cloud operations, core and metro transport, mobile connectivity, and video streaming. The Optical Networks (OTNs) are regarded as the cornerstone of the modern society since such networks can provision large capacity required by the enormous amount of heterogeneous data at low costs. However, as a reaction to any upgrades or new deployments, currently, the OTNs incur large amount of time (from few days to weeks) for such changes to be effective. Also, the OTNs are complex in nature due to the existence of large number of adjustable and interdependent system parameters which are in turn enabled by the use of coherent transmission and reception technologies, and advanced digital signal processing techniques. Further, due to the availability of large amount of data, the modern OTNs are required to find out, if any, hidden relations which might exist between the varied data. Hence, it becomes logical to automate the complex tasks in the OTNs so that the network design and operation is handled by the machines.

Artificial intelligence (AI) is a branch of science known to create intelligent machines which have the capability to self-learn and autonomously undertake decisions based on their perceived environment. Further, Machine learning (ML), which is a branch of AI, is known to enable such a learning paradigm. The aforementioned is possible since ML is based on the concept of providing access to correct data to the machines through which the machines can solve complex problems themselves. Through the use of complex mathematical and statistical tools, ML helps the machines to perform complex tasks independently which previously required human intervention. The research community has already considered ML as a paradigm shift for the design of future OTNs and systems. In specific, ML has been identified as one of the most promising mathematical methods which is capable of (i) performing data analysis for the OTNs, and (ii) enabling the automation of self-configuration and fault management in the OTNs.

Even though the application of ML in the OTNs is in its infancy, the research community has already understood that the ML enabled algorithms will provide a promising platform for end-to-end network automation. Hence, it can be inferred that application of ML in the OTNs is a fast-growing research topic, which has already seen an increasingly strong participation from both, the industry and the academic researchers.

However, despite the promise of the ML paradigms, it is extremely challenging to consider all the services, infrastructure and operational requirements of the network while aiming to achieve the goal of learning based optimization. In this special issue, research articles, and surveys and tutorials are sought out from researchers working in the domain of OTNs. The aim and scope of this special issue is to find solutions to the following envisaged open problems (but not limited to):

1: Creating a basis for the design and operation of the learning enabled OTNs at large scale.

2: Formulation of efficient ML enabled algorithms in the context of OTNs.

3:The collection, processing and transferring of large amount of data required for the accurate operation of the ML enabled algorithms.

4: Creating a tool-chain which will integrate the ML frameworks with the OTN orchestration in regard to network control, management integration, and evaluations.

5: Determining the accurate timescale to monitor the parameters which are required to form the input to the ML algorithms to ensure the accuracy of the algorithm.

The Research Scope of the Special Issue

·Machine Learning

·Communications and Networking

·Autonomous Networking

·Artificial Intelligence

·Data Analytics

·Network Automation

Submission guidelines

All papers should be submitted via the Journal of Networking and Telecommunications submission system: http://ojs.piscomed.com/index.php/JNT

Submitted articles should not be published or under review elsewhere. All submissions will be subject to the journal’s standard peer review process. Criteria for acceptance include originality, contribution, scientific merit and relevance to the field of interest of the Special Issue.

Important Dates

Paper Submission Due: September , 2019

The Lead Guest Editor

Sridhar Iyer

Dr. Sridhar Iyer received the B.E. degree in Electronics and Telecommunications Engineering from Mumbai University, India in 2005, M.S degree in Electrical and Communication Engineering from New Mexico State University, U.S.A in 2008, and the Ph.D. degree from Delhi University, India in 2017. He worked as an Assistant Professor in the Department of ECE at NIIT University, and Christ University, India between 2012 - 2016. He received the young scientist award from the Department of Science and Technology, Government of India in the year 2013. Currently he is an Associate Professor in the Department of ECE, Jain College of Engineering, India. His research interests include the architectural, algorithmic, and performance aspects of the optical networks, with current emphasis on efficient design and resource optimization in the Space Division Multiplexing enabled flexi-grid Elastic optical networks using Machine Learning. Dr. Iyer has published over 60 peer-reviewed articles in the aforementioned areas.

Guest Editor

Dr. Rahul Pandya, Assistant Professor, Dept. of ECE, NIT-Warangal,Telangana, India.

Dr. Tanmay De, Associate Professor, Dept. of CS, NIT-Durgapur, WestBengal, India.

Dr. Shree Prakash Singh, Professor, Division of ECE, NSIT, New Delhi,India.