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.


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


• 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

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