Artificial Intelligence-Based Algorithms in Wireless Sensor 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 (31 December 2023) | Viewed by 1945

Special Issue Editor


E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu City 300, Taiwan
Interests: theoretical and fundamental problems in wireless sensor networks; algorithms in wireless sensor networks; graph algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) raise several practical and undiscovered algorithmic and mathematical issues, but traditional techniques are not sufficient to solve these problems. This is due to constrained energy and computation capability, nondeterministic sensor failures, channel impairments, node mobility, hostile and distrusted environments, and even external attackers of nodes in WSNs. In all these issues, WSNs exhibit substantial vulnerability when compared to other wireless and wired networks. It seems challenging to design robust and long-lived WSNs by devising novel algorithms or developing new theories whilst introducing minimal communication overhead and energy consumption. However, Artificial Intelligence (AI) seems to be a very effective technology that can be used to solve the problems faced by WSNs. Usually, there are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. Under those categories, there are dozens of different algorithms. We believe that these AI-based algorithmic and theoretical issues in WSNs have not been fully explored. As a result, the main focus of this Special Issue strives for a deeper understanding of AI-based algorithms and theories which are developed to build up WSNs.

Prof. Dr. Chang Wu Yu
Guest Editor

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 submissions that pass pre-check are 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. Algorithms 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 1600 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 sensor networks
  • AI-based algorithms including machine learning, genetic algorithms, clustering, decision trees, support vector machines, k-nearest neighbor, neural networks, fuzzy theory for wireless sensor networks
  • AI-based data structures for wireless sensor networks
  • AI-based protocols for wireless sensor networks
  • AI-based mathematic models for wireless sensor networks
  • AI-based optimization problems for wireless sensor networks
  • AI-based performance evaluation for wireless sensor networks
  • AI-based system design for wireless sensor networks
  • AI-based simulation tools for wireless sensor networks

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 1147 KiB  
Article
A Narrow-Down Approach Based on Machine Learning for Indoor Localization
by Sahibzada Muhammad Ahmad Umair and Tughrul Arslan
Algorithms 2023, 16(11), 529; https://doi.org/10.3390/a16110529 - 17 Nov 2023
Viewed by 1270
Abstract
Over the past decade, the demand and research for indoor localization have burgeoned and Wi-Fi fingerprinting approach has been widely considered because it is cheap and accessible. However, most existing methods lack in terms of positioning accuracy and high computational complexity. To cope [...] Read more.
Over the past decade, the demand and research for indoor localization have burgeoned and Wi-Fi fingerprinting approach has been widely considered because it is cheap and accessible. However, most existing methods lack in terms of positioning accuracy and high computational complexity. To cope with these issues, we formulate a two-stage, coarse and accurate positioning narrow-down approach (NDA). Furthermore, a three-step source domain refinement (SDR) scheme that involves outlier removal, stable AP’s weight enhancement, and a data averaging technique by applying the K-means clustering algorithm is also proposed. The collaboration of SDR scheme with the training data selection, area division, and overlapping schemes reduces the computational complexity and improves coarse positioning accuracy. The effect of the proposed SDR scheme on the performance of the support vector machine (SVM) and random forest algorithms is also presented. In the final/accurate positioning phase, a set of lightweight neural networks (DNNs), trained on different sub-areas, predict the user’s location. This approach significantly increases positioning accuracy while reducing the online computational complexity at the same time. The experimental results show that the proposed approach outperforms the best solutions presented in the literature. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Algorithms in Wireless Sensor Networks)
Show Figures

Figure 1

Back to TopTop