Special Issue "Intelligent techniques for energy efficient service management in Edge computing"

Submission Deadline: 10 September 2020 (closed)
Guest Editors
Dr. Alireza Souri, Islamic Azad University, Iran.
Prof. Mu-Yen Chen, National Taichung University of Science and Technology, Taiwan.
Prof. Amir Masoud Rahmani, Khazar University, Azerbaijan.

Summary

Nowadays, the Internet of Things (IoT) has been one of the most popular technologies that facilitate new interactions among things and humans to enhance the quality of life. With the rapid development of IoT, Edge computing paradigm is emerging as an attractive solution for processing the data of IoT applications. In the Edge environment, IoT applications are executed by the intermediate computing nodes, as well as the physical servers in cloud data centers. On the other hand, due to the energy efficient service management limitations, service heterogeneity, dynamic nature, and unpredictability of Edge environment, it necessitates the energy-aware service management issues as one of the challenging problems to be considered in the Edge landscape. Despite the importance of energy efficient service management issues, this special issue invites researchers to publish selected original articles presenting intelligent trends to solve new challenges of intelligent services and system management problems. We also are interested in review articles as the state-of-the-art of this topic, showing recent major advances and discoveries, significant gaps in the research and new future issues.

 

Topics are as below but are not limited to:

Methodologies, and Techniques

• New methods for Artificial Neural Networks and MLP techniques

• New learning methods for established intelligent architectures

• Methods for non-established deep learning models (deep SVMs, deep fuzzy models, deep clustering techniques, …)

• Complexity Reduction in and Transformation of Deep Learning Models

• Interoperability Aspects for a better Understanding of Deep Learning Models

• Reasoning of Input-Output Behavior of Deep Learning Models (toward Understanding their Predictions)

• Deep Learning Classifiers combined with Active Learning

• Evolutionary–based optimization

• Hybrid learning schemes (deterministic with heuristics-based, mimetic)

• Incremental learning methods for self-adaptive deep models

• Evolving techniques for deep learning systems (expanding and pruning layers, components etc. on the fly)

• Transfer learning for deep learning systems

Applications

• Service management in fog computing

• Task scheduling and composition in edge computing

• Service offloading and placement in IoT environments

• Service oriented architecture for IoT applications in edge computing

• Service negotiation and communication for vehicular networks in IoT

• Medical service orchestration in fog-based healthcare IoT systems

• Agriculture-based service management in IoT

• Industrial service discovery in IoT


Keywords
Machine learning, Intelligent systems, meta-heuristic algorithms, Deep learning, Fog Computing, Internet of Things (IoT), Cloud computing, Service management, Energy efficiency, healthcare and medical systems, Industrial systems, Blockchain technology

Published Papers
  • Enhanced KOCED Routing Protocol with K-means Algorithm
  • Abstract Replacing or recharging batteries in the sensor nodes of a wireless sensor network (WSN) is a significant challenge. Therefore, efficient power utilization by sensors is a critical requirement, and it is closely related to the life span of the network. Once a sensor node consumes all its energy, it will no longer function properly. Therefore, various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation. Recently, clustering algorithms combined with artificial intelligence have been proposed for this purpose. In particular, various protocols employ the K-means clustering algorithm, which is a machine learning… More
  •   Views:47       Downloads:34        Download PDF

  • COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries
  • Abstract Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge the feelings of Gulf countries… More
  •   Views:177       Downloads:95        Download PDF

  • OTS Scheme Based Secure Architecture for Energy-Efficient IoT in Edge Infrastructure
  • Abstract For the past few decades, the Internet of Things (IoT) has been one of the main pillars wielding significant impact on various advanced industrial applications, including smart energy, smart manufacturing, and others. These applications are related to industrial plants, automation, and e-healthcare fields. IoT applications have several issues related to developing, planning, and managing the system. Therefore, IoT is transforming into G-IoT (Green Internet of Things), which realizes energy efficiency. It provides high power efficiency, enhances communication and networking. Nonetheless, this paradigm did not resolve all smart applications’ challenges in edge infrastructure, such as communication bandwidth, centralization, security, and privacy.… More
  •   Views:285       Downloads:221        Download PDF

  • Predicting the Type of Crime: Intelligence Gathering and Crime Analysis
  • Abstract Crimes are expected to rise with an increase in population and the rising gap between society’s income levels. Crimes contribute to a significant portion of the socioeconomic loss to any society, not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy, social parameters, and reputation of a nation. Policing and other preventive resources are limited and have to be utilized. The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are… More
  •   Views:315       Downloads:233        Download PDF

  • A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing
  • Abstract In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required… More
  •   Views:494       Downloads:287        Download PDF