Fourth Quarter 2020
Manuscript Submission Deadline
Call for Papers
Internet of Things (IoT) is a great paradigm for continuous research and development. Studies in this area focus on enabling objects to communicate with each other and to send information about their environment, facilitating decision-making and improving the Quality of Service (QoS). Likewise, the disruption of the IoT has led to the search for strategies that would mitigate the data load in complex communications networks, where data are generated, processed and exchanged among millions of sensors and devices. One of those strategies is Edge Computing (EC), which aims to prevent the congestion caused by the lack of computing resources, network or storage. This trend brings computational and service infrastructures closer to the end user by migrating data filtering, processing or storage from the cloud to the edge of the network.
Nevertheless, the number of Internet-connected devices is already huge, and it continues to increase. Moreover, new and more advanced applications are developed every day, which require better QoS and therefore, greater demands in terms of bandwidth, latency and data integrity. This has fostered the emergence of new approaches that optimize the use of the resources of existing networks and make network investments profitable. Network Function Virtualization (NFV) is among these solutions and has been designed to virtualize the different components of the network. NFV is closely associated with and complementary to the concept of Software-Defined Network (SDN), Software-Defined Wireless Networks (SDWNs) and Software-Defined Wireless Sensor Networks (SDWSNs).
The use of Artificial Intelligence (AI) techniques, such as Machine Learning (ML) or Deep Learning (DL), is ideal in SDN and VFN network architectures, since they enable the creation of intelligent algorithms that learn automatically in both Edge and Cloud, allocating network resources according to the needs of the users and the targeted QoS.
This special issue aims to consolidate the current state-of-the art and to promote the exchange of innovative ML concepts and solutions applied to SDN and NFV in EC and IoT scenarios, focusing on the different possibilities offered by ML, such as its ability to allocate resources according to the quality of service.
The topics of interests include, but are not limited to:
- SDN and NFV in IoT and Edge Computing scenarios
- Software-Defined Wireless Networks (SDWNs) and Software-Defined Wireless Sensor Networks (SDWSNs)
- SDN and NFV in Industrial Internet of Things
- SDN and NFV in the Internet of Vehicles
- New architectures in the field of Edge Computing, Fog Computing and Cloud Computing in IoT applications
- Intelligent algorithms for network resource management and orchestration from the control layer
- Intelligent mechanisms for the transfer of models from the Cloud to the Edge, as well as training of these models in the Edge
- Consensus Algorithms for Resource Allocation in Software Defined Networks
- New architectures for the implementation of Edge-IoT solutions with low energy consumption.
- Blockchain and other Distributed Ledger Technologies as NFV
- New case studies and applications of Edge-IoT architectures, including Industry 4.0, Smart Farming, Smart Cities, healthcare, Smart Energy, etc.
- Prototyping, test-beds and case-studies for SDN and NFV
- Reviews and surveys for SDN and NFV
Submit manuscript to: https://mc.manuscriptcentral.com/oj-coms
For information regarding IEEE OJ-COMS including its publication policy and fees, please visit the website https://www.comsoc.org/publications/journals/ieee-ojcoms
Lead Guest Editor
Antonio Skarmeta, University of Murcia, Spain
Joel J. P. C. Rodrigues, Federal University of Piauí (UFPI), Brazil; Instituto de Telecomunicações, Portugal
Sofiène Affes, INRS, Montréal, QC, Canada
Yuan Shen, Tsinghua University, Beijing, China
Ian Oppermann, NSW Data Analytics Centre, Sydney, NSW, Australia
Roberto Casado, University of Salamanca, Spain
Ricardo S. Alonso, University of Salamanca, Spain
For inquiries regarding this Special Issue, please contact the Lead Guest Editor.