Special Issue "Machine Learning Advances Applied to Wireless Multi-hop IoT Networks"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editors

Dr. Vishal Sharma
E-Mail Website
Guest Editor
Information Security Engineering Department, Soonchunhyang University, South Korea,
Interests: UAV communications; 5G networks; drone security; estimation and prediction theory; blockchain; statistics and data analytics
Special Issues and Collections in MDPI journals
Dr. Daniel Gutiérrez Reina
E-Mail Website
Guest Editor
Electronic Engineering Department, University of Seville, Calle San Fernando, 4, 41004 Sevilla, Spain
Interests: multi-hop networks; sensor networks; VANETs; FANETs; evolutionary computation; machine learning; deep learning
Special Issues and Collections in MDPI journals
Dr. Kathiravan Srinivasan
E-Mail Website
Guest Editor
School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Interests: machine learning; communication systems and networks; multimedia and computer vision; artificial intelligence; data science; wireless networks
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless multihop networks have experienced an enormous evolution since their inception back in the 1990s. The classical MANET (mobile ad hoc network) paradigm led to new research directions, more focused on the application scenario, such as WSNs (wireless sensor networks) for monitoring and sensing, VANETs (vehicular ad hoc networks) for vehicular scenarios, DTNs (delay-tolerant networks) for intermittent connectivity, and FANETs for drone-based applications. The multihop paradigm is envisioned to play an essential role in the IoT ecosystem, since ubiquitous devices will interconnect with each other through different wireless technologies, creating intelligent systems like smart cities.

Machine learning techniques have experienced a new flourishing in the last few years due to the availability of massive data and high computational resources even for low-cost and embedded devices like the ones used in multihop networks. Supervised and unsupervised learning techniques are the leading hotlines, including regression, classification, clustering, among other more advanced approaches like reinforcement learning and deep learning. These techniques will allow the improvement of the underlying operational mechanisms of wireless multihop networks throughout all communication layers, such as deployment, connectivity, broadcasting and routing, security, quality of service, power consumption, and mobility, among others. This Special Collection seeks to publish novel approaches of machine learning techniques to improve the performance of wireless multihop networks for the IoT ecosystem. The main topics include but are not limited to:

  • Machine learning techniques for wireless multihop IoT networks;
  • Evolutionary computation for wireless multihop IoT networks;
  • Swarm intelligence for wireless multihop IoT networks;
  • Deep learning for wireless multihop IoT networks;
  • Reinforcement learning and deep reinforcement learning for wireless multihop IoT networks;
  • Bayesian optimization for wireless multihop IoT networks;
  • Game theory for wireless multihop IoT networks;
  • Neural networks for wireless multihop IoT networks;
  • Soft computing approaches for wireless multihop IoT networks.

Dr. Vishal Sharma
Dr. Daniel Reina
Dr. Kathiravan Srinivasan
Guest Editors

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.

Keywords

  • wireless multihop networks
  • machine learning
  • VANETs
  • WSNs
  • deep learning
  • IoT

Published Papers (2 papers)

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Research

Open AccessArticle
The Value of Simple Heuristics for Virtualized Network Function Placement
Future Internet 2020, 12(10), 161; https://doi.org/10.3390/fi12100161 - 25 Sep 2020
Viewed by 610
Abstract
Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in [...] Read more.
Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in the network. There is a wide range of mathematical models proposed to place the Network Functions (NF) optimally. However, the critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks. In wireless networks, we are considering the scarcity of Bandwidth (BW) as another constraint that is due to the presence of interference. While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide results almost as good as optimal solution. Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time. In this paper, in contrast, we focus on designing a simple and scalable heuristic. We propose four heuristics, which are gradually becoming more complex. We compare their performance with each other, a related heuristic proposed in the literature, and a mathematical optimization model. Our results demonstrate that while more complex placement heuristics do not improve the performance of the algorithm in terms of the number of accepted placement requests, they take longer to solve and therefore are not applicable to larger networks.In contrast, a very simple heuristic can find near-optimal solutions much faster than the other more complicated heuristics while keeping the number of accepted requests close to the results achieved with an NP-hard optimization model. Full article
(This article belongs to the Special Issue Machine Learning Advances Applied to Wireless Multi-hop IoT Networks)
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Open AccessArticle
Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks
Future Internet 2020, 12(3), 47; https://doi.org/10.3390/fi12030047 - 09 Mar 2020
Cited by 1 | Viewed by 1915
Abstract
In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to [...] Read more.
In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to address several problems in mobile ad hoc networks like energy efficiency. In this paper, we apply game theory to reduce the control packets in dense networks. We choose a proactive routing protocol, Optimized Link State Routing (OLSR) protocol. We consider two strategies in this method: willingness_always and willingness_never to reduce the multipoint relay (MPR) ratio in dense networks. Thus, nodes with less influence on other nodes are excluded from nomination as MPRs. Simulations were used to confirm the efficiency of using our improved method. The results show that the MPR ratio was significantly reduced, and packet delivery ratio was increased compared to the conventional protocol. Full article
(This article belongs to the Special Issue Machine Learning Advances Applied to Wireless Multi-hop IoT Networks)
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