Special Issue "Emerging Trends in Software-Defined Networking for Industry 4.0"

Submission Deadline: 31 July 2021 (closed)
Guest Editors
Dr. Thippa Reddy Gadekallu, Vellore Institute of Technology, India.
Dr. Rutvij H. Jhaveri, Pandit Deendayal Petroleum University, India.
Dr. Praveen Kumar Reddy M, Vellore Institute of Technology, India.


Cyber-Physical systems (CPSs), Internet-of-Things (IoT) and Machine-to-Machine (M2M) communication form foundation for the implementation of Industry 4.0. In this new era, the key foundation for success for manufacturing enterprises will be provided by efficient network management. In recent years, Software-Defined Networking (SDN) is envisioned as a promising technology in enabling efficient management and configuration of large and complex industrial networks. As SDN separates control plane from data plane, it improves the agility and scalability as compared to the traditional networks. Due to the abstraction of the network infrastructure from the applications, it provides an efficient and effective solution for centralizing the control and intelligence of a network. SDN has proven its potential in industrial communication between heterogenous devices due to its flexibility and capability. It paves the way in achieving efficient collection, transfer and analysis of huge amount of data in extremely efficient manner for smart factories. SDN provides global visibility in industrial communication networks which can fit numerous challenges of Industry 4.0. In addition, it has enormous potential to face upcoming challenges by providing better resource management, resilience management, improved security, better traffic management, improved scheduling and efficient routing in critical industrial applications. Moreover, SDN provides an efficient solution for achieving targeted quality-of-service (QoS) and for automating decision making process, with Artificial Intelligence (AI) techniques and Machine Learning (ML) algorithms.


This Special Issue on “Emerging Trends in Software-Defined Networking for Industry 4.0” aims to reflect recent advancements in software-defined networks for the fourth industrial revolution. This will provide platform for researchers to submit original manuscripts showcasing findings and exploring emerging trends in SDN for Industry 4.0.


Aims and Scopes:

Topics of interest include, but are not limited to:

• Innovative architecture, infrastructure, networking protocol design and testbeds for SDN-based smart manufacturing

• Machine learning, novel algorithms and methods for assisting SDN-based industrial networks

• Resource management with SDN

• Traffic management in SDN-based Industrial IoT

• Clustering of SDN Networks

• Novel fault-resilience framework with SDN

• SDN algorithms for network security

• Artificial Intelligence for assisting SDN networks

• Performance optimization of SDN-based industrial networks

• Novel SDN controller placement and allocation strategies

• Scheduling and routing challenges for SDN-based smart manufacturing

Industry 4.0, Industrial IoT, Machine Learning, Software Defined Networks

Published Papers
  • Deep Q-Learning Based Optimal Query Routing Approach for Unstructured P2P Network
  • Abstract Deep Reinforcement Learning (DRL) is a class of Machine Learning (ML) that combines Deep Learning with Reinforcement Learning and provides a framework by which a system can learn from its previous actions in an environment to select its efforts in the future efficiently. DRL has been used in many application fields, including games, robots, networks, etc. for creating autonomous systems that improve themselves with experience. It is well acknowledged that DRL is well suited to solve optimization problems in distributed systems in general and network routing especially. Therefore, a novel query routing approach called Deep Reinforcement Learning based Route Selection… More
  •   Views:901       Downloads:839        Download PDF

  • Flow Management Mechanism in Software-Defined Network
  • Abstract Software-defined networking (SDN) is a paradigm shift in modern networking. However, centralised controller architecture in SDN imposed flow setup overhead issue as the control plane handles all flows regardless of size and priority. Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself. Furthermore, existing frameworks do not focus on managing elephant flows like RTSP. Thus, the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a pre-defined threshold. This mechanism will ensure that the control plane works at… More
  •   Views:845       Downloads:842        Download PDF

  • Centralized QoS Routing Model for Delay/Loss Sensitive Flows at the SDN-IoT Infrastructure
  • Abstract The rapidly increasing number of Internet of Things (IoT) devices and Quality of Service (QoS) requirements have made the provisioning of network solutions to meet this demand a major research topic. Providing fast and reliable routing paths based on the QoS requirements of IoT devices is very important task for Industry 4.0. The software-defined network is one of the most current interesting research developments, offering an efficient and effective solution for centralized control and network intelligence. A new SDN-IoT paradigm has been proposed to improve network QoS, taking advantage of SDN architecture in IoT networks. At the present time, most… More
  •   Views:838       Downloads:698        Download PDF

  • FogQSYM: An Industry 4.0 Analytical Model for Fog Applications
  • Abstract Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps… More
  •   Views:905       Downloads:783       Cited by:1        Download PDF

  • Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm
  • Abstract The evolution of Industry 4.0 made it essential to adopt the Internet of Things (IoT) and Cloud Computing (CC) technologies to perform activities in the new age of manufacturing. These technologies enable collecting, storing, and retrieving essential information from the manufacturing stage. Data collected at sites are shared with others where execution automatedly occurs. The obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing process. However, information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major concern. The current research validates the information optimally… More
  •   Views:12658       Downloads:6969       Cited by:2        Download PDF