Special Issue


[Submission Due Date - 2019-01-20 ]

Machine Learning in IoT and Edge Computing

KIPS Transactions on Computer and Communication Systems

Description

The Internet of Things (IoT) advances and promotes a variety of wired and wireless technologies through embedded devices, smart objects, and smartphones. The integration of IoT along with Wireless Sensor Networks (WSN) based on new Bluetooth standards, such as Bluetooth Low Energy (BLE) 4.0 and ZigBee, unlocks new IoT applications and services in the future generation of networks. IoT devices generate a massive volume of data continuously. In general, cloud systems are used to store and compute such a large volume of IoT sensor data. This creates even more challenges in data storage and the integration between IoT devices. In order to overcome this problem, edge computing technology moves the collection, process, and analysis of data into the point of origin, rather than to a cloud-based data center, and it enables data processing and analytics as well as knowledge generation to occur in the vicinity of the source of the data. Thus, edge computing technology is becoming a significant platform for enabling consumer-centric IoT services and applications that demand real-world applications. In the meantime, machine learning is a branch of artificial intelligence (AI) that focuses on enabling machines to learn for themselves without the need for human intervention or to be explicitly programmed to do so. Machine learning on IoT devices allows for learning models directly on the devices themselves, removing the need to externalize data in any way. Machine learning algorithms have a capability to process and analyze a kind of streaming data generated at the edge of the network and IoT devices. This special issue focuses on challenges and problems in machine learning algorithms for processing, learning and discovering knowledge from IoT devices and edge computing technology.

Scope
Topics of interest include, but are not limited to:
 
• Supervised, unsupervised and reinforcement learning in IoT and edge computing
• Big data processing in edge computing for IoT
• Emerging machine learning technologies in IoT and edge computing
• Smart homes, smart traffic monitoring, smart health, smart education and smart manufacturing in IoT and edge computing
• Machine learning algorithms for memory management in IoT and edge computing
• Dimension reduction and Information fusion in IoT and edge computing
• Scalable, online, and decentralized deep learning in IoT and edge computing
• Machine learning algorithms for intrusion and threat detection in IoT and edge computing
• Deep learning for energy efficient data sensing and processing in IoT and edge computing
 
Submission Guideline
• Submission: Online Manuscript Submission and Review System - Select 'Special Issue Track' - Select 'Machine Learning in IoT and Edge Computing'
• Page Limit: 20 pages
• Format: Your submission is subject to the 'Manuscript Preparation' of 'Instruction for Authors' in the Journal homepage      (http://ktccs.kips.or.kr)
• Review: Final decision will be made at the 1st round without revision process
• Sample Manuscript Format: You can download in the KTCCS homepage (http://ktccs.kips.or.kr)
- Submission Fee: USD 100 or KRW 100,000
- Contact: Ms. Young-Suk Yun (02-2077-1414, Ext. 3) 
 
Schedule
• Submission Due Date: 2019-01-20 
• Expected Publication Date: Vol.8, No.4, April 2019
Guest Editors

Prof. Kyungbaek Kim (Chonnam National University)

 
 
 

[Submission Due Date - 2016-10-30 ]

Android Security

KIPS Transactions on Computer and Communication Systems

 
Description

We plan to publish a new special issue. We are soliciting high quality manuscripts presenting original contributions for its special issue, and the manuscripts should be submitted by our online submission and review system. When submitting a new manuscript, please select 'Special Issue' track and then select 'Android Security' special issue, too.

Scope

Topics of interest include, but are not limited to:

  • - Android system security
  • - Android app security
  • - Android hardware security
  • - Android cryptography
  • - Applying machine learning to android security
  • - Android security in IoT Environment
  • - Other android security study
Submission Guideline

- Submission: Online Manuscript Submission and Review System - Select 'Special Issue Track' - Select 'Android Security'
- Page Limit: 20 pages following the 'Manuscript Preparation' of 'Instruction for Authors'
- Format: Your submission is subject to the 'Manuscript Preparation' of 'Instruction for Authors'
- Review: Final decision will be made at the 1st round without revision process
- Sample Manuscript Format: Society homepage (http://www.kips.or.kr -> Notifications/Events)
- Submission Fee: 100,000 (KRW)
- Contact: Ms. Yong-Sook Yoon (02-2077-1414, Ext. 3)

 
Schedule
- Submission Due Date: 2016-10-30
- Expected Publication Date: Vol.5, No.12, Dec. 2016
Guest Editors
Prof. Jun-Won Ho
Seoul Women's University