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AUTOSOFT JOURNAL
Intelligent Automation & Soft Computing
Call for Papers
Special Issue on “Impact of Machine Learning in Internet of Things (IoT) Paradigm”
The objective of this special issue is According to ABI Research’s research analysis Edge Analytics in Internet of Things (IoT), the total volume of data produced annually by IoT-connected devices is estimated to reach almost 130 million yottabytes (YB) in 2020. With such huge numbers in play, managing the large volume, variety, and velocity of big data becomes one of the major challenges the Internet of Things (IoT) industry has to face today. With the number of IoT-connected devices estimated to grow to tens of billions by the end of the decade, the volume of the data generated by these devices is poised to reach unprecedented proportions.
Now, as we collect massive amounts of Internet of Things (IoT) data, our ability as humans to make sense of it becomes quite the challenge. To be more efficient, a process is needed that will automatically and in real time collect data, make predictions, and react. Machine learning and a complete tool chain that supports this model are required. Internet of Things (IoT) is not only about collecting the data, but it’s also focused on obtaining value from the data after we’ve acquired it. Attaching sensors to everything only becomes worthwhile when we can predict, control, and make decisions in response to the data.
One of the greatest boons that machine learning and its algorithms have delivered to the Internet of Things (IoT) is how easily it integrates into the IoT’s platforms. Most of the leading Internet of Things (IoT) platforms now offer machine learning capabilities. This enables the Internet of Things (IoT) system to analyze sensor data, look for correlations and determine the best response to take. The system continuously checks to see how well its predictions are working and keeps refining its own algorithm. The rapid proliferation of mobile devices around the globe, for instance, is one of the key drivers of the Internet of Things (IoT), and machine learning often fits neatly into the world of mobile device development, programming, and maintenance. Companies who want to succeed in today’s marketplace understand the valuable potential hidden in machine learning, and are starting to justifiably treat their algorithms as valued parts of their workforce.
As billions of more devices spread across the world in the next one or two decades alone, these algorithms and the cost-cutting advances they bring to businesses and consumers alike will only grow more indispensable. As more people sign up on social media platforms, buy smart devices and commute with autonomous vehicles, the vice grip of the Internet of Things (IoT) on society will only grow stronger, powered to a large extend by the wondrous world of machine learning. This huge increase in data will drive great improvements in machine learning, opening countless opportunities for us to reap the benefits. With the right algorithms, the system can be gradually taught to recognize any internal and external production-related factors, optimize the use of consumables, and improve the efficiency of the entire production process.
Topics include, but are not limited to, the following:
• Design and Evaluation of Energy Efficient Networks and Services in IoT
• Algorithms for Time Series Data and IoT
• IoT system architecture and Enabling technologies
• Intelligent interfaces for Internet of Things
• IoT Sensing Things Technology and Applications
• Efficient Resource Management Based on IoT
• Knowledge-Based Discovery of Devices in the IoT
• Localization in IoT
• Crowd-Sourcing and Opportunistic IoT
• Security, Trust, Privacy and Identity in the IoT
• Performance Evaluation of IoT Technologies
• Classification and interpretation of images, text, video
• Deep learning and latent variable models
• Bayesian machine learning
• Classification, regression and prediction
• Machine Learning for Web Navigation and Mining
• Machine Learning for Information Retrieval
• Neural Network Learning
• Distributed and Parallel Learning Algorithms and Applications
• Structured prediction, relational learning, logic and probability
• Machine learning for network slicing optimization
• Reinforcement Learning and Planning
• Fault-tolerant network protocols using machine learning
• Machine learning and big data analytics for network management
• State-of-practice, experience reports, industrial experiments, and case studies in the IoT
Submission Details
Authors should follow the manuscript format and the submission procedure of Intelligent Automation & Soft Computing Journal manuscript format described below at the Journal site:
http://wacong.org/autosoft/auto/index.php
The submission must include the title, abstract of your paper, and the corresponding author's name and affiliation. All papers will be rigorously reviewed based on the quality: originality, high scientific quality, well organized and clearly written, sufficient support for assertions and conclusions.
Schedule
Manuscript due: June 25th, 2019
Acceptance/Rejection notification: August 15th, 2019
Final manuscript due: October 25th, 2019
Submission
Use the following link
http://wacong.org/autosoft/auto/index.php
Create Author's Login
Inside the Author's Login click Journal Submission
Under the Name of the Special Issue choose " Impact of Machine Learning in Internet of Things(IoT) Paradigm
Guest Editor(s)
Prof.D.Ganesh Gopal, Galgotias University, India, [email protected]
Prof. Victor Chang, Xi’an Jiaotong-Liverpool University (XJTLU), China, [email protected]
Prof. Bharat S. Rawal Kshatriya, Penn State Abington University, USA, [email protected]
Prof. PELUSI Danilo, UNIVERSITA’ DEGLI STUDI DI TERAMO, University of Teramo, Italy, [email protected]
For enquiries, please contact (Corresponding GE)[email protected]
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