Algorithms in Wireless and Mobile Networks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 3000

Special Issue Editors


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Guest Editor
Department of Information Technology, Jadavpur University Salt Lake Campus, Kolkata, India
Interests: wireless and mobile networks

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Guest Editor
Department of Computer Science and Engineering, Techno International New Town (Techno India Group), Newtown, Kolkata, India
Interests: wireless and mobile networks; machine learning; network security

Special Issue Information

Dear Colleagues,

Due to recent advances in wireless communication and networking technologies, a surge of ubiquitous infrastructure and high-performance wireless networks have been observed, leading to a wide variety of applications ranging from environment monitoring to health care, as well as from critical infrastructure protection to wireless security. The number of users and variety of services have grown not only in number, but also in complexity, intensifying the interest in developing principles, algorithms, design methodologies, and systematic evaluation frameworks for next-generation wireless networks.

Wireless networks pose many algorithmic challenges, such as modeling realistic wireless signal propagation, interference at the lower level, and dynamics and mobility at the higher level being complex problems. Additionally, emerging technologies in this domain make this task even more difficult; thus, using these models in rigorous algorithmic research is very challenging. In this context, analyzing protocols for wireless networks in a more realistic set up is essential to demonstrate that these protocols would actually work in practice.

Another aspect of this problem is that next-generation wireless and mobile networks are going to be more complex due to the integration of artificial intelligence (AI), big data, the Internet of Things (IoT), edge computing, etc. Since the modeling of wireless networks is going to become increasingly more difficult, the complexity of solutions is going to increase exponentially, leading to a higher network operation and maintenance costs.

The incorporation of AI into the future wireless network architecture has become a technological trend in research and exploration, contributing to the future wireless network transmission framework. Moreover, there is a need for further research discussing AI combined with communication network channel modelling, network operation and maintenance, network security audits, and other fields.

The focus of this Special Issue is (i) models and algorithmic approaches to better understand the capabilities and limitations of modern wireless networks and (ii) the application of intelligent algorithms in next-generation wireless networks.

Prof. Dr. Samiran Chattopadhyay
Dr. Raja Karmakar
Guest Editors

Manuscript Submission Information

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Keywords

  • communication protocols
  • complexity and computability
  • application of deep learning in wireless and mobile networks
  • dynamic networks
  • temporal graphs
  • edge intelligence
  • energy management
  • power-saving schemes
  • game theoretic aspects
  • infrastructure discovery
  • Internet of Things
  • Internet of Vehicles
  • intelligent localization algorithms
  • machine intelligence-enabled wireless and mobile networks
  • medium access control
  • mobility and dynamics
  • multimedia transmission
  • performance evaluation
  • experimental analysis
  • population protocols
  • swarm computing
  • resource efficiency
  • routing and data propagation
  • security in wireless and mobile networks
  • self-stabilization
  • self-properties
  • software-defined networking
  • systems and testbeds
  • time synchronization
  • topology control
  • tracking
  • virtual infrastructures
  • wireless networking for metaverse
  • wireless networking for ubiquitous computing

Published Papers (1 paper)

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Research

22 pages, 9854 KiB  
Article
Visual Assessment of Cluster Tendency with Variations of Distance Measures
by Guzel Shkaberina, Natalia Rezova, Elena Tovbis and Lev Kazakovtsev
Algorithms 2023, 16(1), 5; https://doi.org/10.3390/a16010005 - 21 Dec 2022
Cited by 1 | Viewed by 1977
Abstract
Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the [...] Read more.
Finding the cluster structure is essential for analyzing self-organized networking structures, such as social networks. In such problems, a wide variety of distance measures can be used. Common clustering methods often require the number of clusters to be explicitly indicated before starting the process of clustering. A preliminary step to clustering is deciding, firstly, whether the data contain any clusters and, secondly, how many clusters the dataset contains. To highlight the internal structure of data, several methods for visual assessment of clustering tendency (VAT family of methods) have been developed. The vast majority of these methods use the Euclidean distance or cosine similarity measure. In our study, we modified the VAT and iVAT algorithms for visual assessment of the clustering tendency with a wide variety of distance measures. We compared the results of our algorithms obtained from both samples from repositories and data from applied problems. Full article
(This article belongs to the Special Issue Algorithms in Wireless and Mobile Networks)
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