Special Issue "Energy-Aware Networking and Green Internet"
A special issue of Future Internet (ISSN 1999-5903).
Deadline for manuscript submissions: closed (30 July 2019).
Interests: green networks; traffic optimization; traffic control and monitoring in cellular systems; QoE guarantee for MoIP services; routing in WMN; machine learning algorithms for network functions
Special Issues, Collections and Topics in MDPI journals
Ever-increasing required data rates; a high number of connected devices; and a large variety of functions, such as in-network storage and computing, will characterize the future Internet. As a side effect, the energy consumption of the Internet continues to grow faster than global electricity consumption. Today, Information and Communications Technology (ICT) is considered to be one of the world’s major energy consumers. In the last decade, many research activities have been focused on finding energy-efficient solutions considering specific problems, such as resource allocation, planning methods, the design of green protocols, and routing algorithms.
Few works have analysed the impact and the benefits of new solutions based on the combination of results associated with different energy-efficient problems. Furthermore, some new technologies, such as 5G and Industrial IoT, have introduced new challenges from the energy efficiency perspective that cannot be solved simply by adapting solutions proposed for previous network technologies and scenarios. In the future version of the Internet, wireless communications will represent a key technology for the power savings, given that a very high number of devices will be connected to the Internet by wireless links.
New wireless networks (and, in particular, the 5G systems and IoT-related technologies) will provide ubiquitous connectivity to billions of connected devices. Sensors, vehicles, and medical and wearable devices will be connected with one another, interacting with humans for providing innovative services.
In this future network scenario, the design of new energy-efficient techniques requires the development of statistical models that are able to represent the behaviour of the network evolution. Meantime, some scenarios, such as vehicular networks, are characterized by high dynamics. In these scenarios, classic approaches based on models are unsuitable given the high variability of the network conditions. New approaches based on machine learning need to be developed in order to allow the devices to autonomously learn from past observations of their surroundings and to respond as appropriate in a self-organizing fashion to the variable network conditions. The study of new methods based on machine learning and neural networks is a key point for the development of innovative, energy-efficient network management and optimization in these scenarios.
The purpose of this Special Issue is to present the most recent results on the specific research challenges for the next steps towards energy-aware networking and Internet, taking into account the new scenarios recently introduced by new technologies, such as 5G and mmWave communications, and new service scenarios, such as Industrial IoT, Multi-access Edge Computing (MEC), and vehicular networks.
High-quality research papers or comprehensive reviews of recent advances in green communications on 5G, mmWave, Multi-access Edge Computing (MEC), IoT, Industrial IoT, and V2X scenarios are welcome. We invite original contributions from both academia and industry that are not yet published or that are not currently under review by other journals or peer-reviewed conferences.
Potential topics include, but are not limited to, the following:
- Measurements-based network optimization methods for energy savings in V2X networks;
- Software Defined Network functions for green networking;
- Methods and algorithms for green network function virtualization;
- Energy management for IoT systems;
- Green protocols for V2X scenarios;
- Energy-efficient HetNets and dense networks;
- Energy-efficient network architecture and design;
- Energy savings solutions for Industrial IoT scenarios;
- Energy savings algorithms for 5G;
- Machine learning techniques for green networking;
- Neural networks approaches for green 5G and mmWave communications;
- Statistical models for green networking;
- Green content distribution techniques;
- Fundamental laws and tradeoffs in green communications.
Prof. Rosario Giuseppe Garroppo
Prof. Gianfranco Nencioni
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- traffic management in V2X networks
- SDN functions for green networks
- energy-aware VNF management
- Green 5G
- Energy-aware IoT
- Green Industrial IoT
- mmWave communications
- machine learning
- neural networks