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Meta-Heuristic Optimizations for the Security and Energy Efficiency of Wireless Sensor Networks

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 7951

Special Issue Editors

Prof. Dr. Jeng-Shyang Pan

E-Mail Website
Guest Editor
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Interests: information hiding; image signal processing; big data processing; computational intelligence; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vaclav Snasel

E-Mail Website
Guest Editor
VSB, Technical University of Ostrava, 708 00 Ostrava, Czech Republic
Interests: artificial intelligence; deep learning; information retrieval; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Chin-Shiuh Shieh

E-Mail Website
Guest Editor
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Interests: computer networking; information security; computational intelligence; embedded systems; medical informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Fang Fan

E-Mail Website
Guest Editor
College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271019, China
Interests: artificial intelligence; computational intelligence; wireless sensor networks; information security; network security

Special Issue Information

Dear Colleagues,

The rapid development and maturity of microelectronic technology, computer technology, wireless communication and sensor technology have promoted the development of low-cost and low-power wireless sensor networks (WSNs). As the key infrastructure of the Internet of things, WSNs have become an important data source for the IT industry. WSNs have the characteristics of rapid deployment, strong survivability and real-time perception. They have been successfully applied in many fields, including military, natural environment monitoring, industrial production process monitoring, medical care, life services, etc. The security and energy efficiency problems in WSNs are related to bottlenecks that restrict their development, and a breakthrough needs to be sought through in-depth research on information theory and optimization theory.

Many optimization problems are black box problems, and it is difficult to obtain their mathematical optimization models. Even if the mathematical expression of the problem can be known, the traditional gradient-based mathematical optimization method cannot easily solve non-convex, non-differentiable and/or non-differentiable objective functions. Meta-heuristic algorithms are an effective method for solving complex optimization problems based on computational intelligence, and have research value and application potential in many fields, including the security and energy efficiency of WSNs.

This Special Issue focuses on the research of information theory and optimization theory in WSNs, and collects new models, ideas and algorithms to solve the security and energy efficiency problems of WSNs. This Special Issue will accept unpublished original papers and comprehensive reviews focused on (but not restricted to) the following research areas:

  • Information theory for WSNs;
  • The application of optimization theory and technology in WSNs;
  • Mathematical modeling for WSNs;
  • Advanced algorithms to solve practical problems of WSNs;
  • Protocol analysis and entropy concept in WSNs;
  • Information security, network security and data security of WSNs;
  • Energy efficiency of WSNs;
  • New applications in WSNs;
  • Application of artificial intelligence technologies in WSNs;
  • Data analysis and experimental design in WSNs

Prof. Dr. Jeng-Shyang Pan
Prof. Dr. Vaclav Snasel
Dr. Chin-Shiuh Shieh
Dr. Fang Fan
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 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. Entropy 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 2600 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
  • meta-heuristic algorithm
  • optimization
  • entropy
  • artificial intelligence
  • security
  • privacy protection
  • energy efficiency
  • energy equalization
  • protocol

Published Papers (4 papers)

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Research

15 pages, 2596 KiB  
Article
Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation
by Jia Zhao, Yuhang Luo, Renbin Xiao, Runxiu Wu and Tanghuai Fan
Entropy 2023, 25(3), 480; https://doi.org/10.3390/e25030480 - 9 Mar 2023
Cited by 2 | Viewed by 1024
Abstract
Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency of the classifier, and the explicit decision [...] Read more.
Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency of the classifier, and the explicit decision mechanism tends to make the training noise degrade the accuracy of the classification model in the prediction stage. This study proposes the Tri-training algorithm for adaptive nearest neighbor density editing and cross-entropy evaluation (TTADEC), which is used to reduce the training noise formed during the classifier iteration and to solve the problem of inaccurate prediction by explicit decision mechanism. First, the TTADEC algorithm uses the nearest neighbor editing to label high-confidence samples. Then, combined with the relative nearest neighbor to define the local density of samples to screen the pre-training samples, and then dynamically expand the training set by adaptive technique. Finally, the decision process uses cross-entropy to evaluate the completed base classifier of training and assign appropriate weights to it to construct a decision function. The effectiveness of the TTADEC algorithm is verified on the UCI dataset, and the experimental results show that compared with the standard Tri-training algorithm and its improvement algorithm, the TTADEC algorithm has better classification performance and can effectively deal with the semi-supervised classification problem where the training set is insufficient. Full article
(This article belongs to the Special Issue Meta-Heuristic Optimizations for the Security and Energy Efficiency of Wireless Sensor Networks)
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16 pages, 785 KiB  
Article
Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem
by Jeng-Shyang Pan, Si-Qi Zhang, Shu-Chuan Chu, Hong-Mei Yang and Bin Yan
Entropy 2023, 25(1), 171; https://doi.org/10.3390/e25010171 - 14 Jan 2023
Cited by 6 | Viewed by 1608
Abstract
The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In [...] Read more.
The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm. Full article
(This article belongs to the Special Issue Meta-Heuristic Optimizations for the Security and Energy Efficiency of Wireless Sensor Networks)
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14 pages, 2528 KiB  
Article
Lossless Image Steganography Based on Invertible Neural Networks
by Lianshan Liu, Li Tang and Weimin Zheng
Entropy 2022, 24(12), 1762; https://doi.org/10.3390/e24121762 - 1 Dec 2022
Cited by 4 | Viewed by 2461
Abstract
Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy [...] Read more.
Image steganography is a scheme that hides secret information in a cover image without being perceived. Most of the existing steganography methods are more concerned about the visual similarity between the stego image and the cover image, and they ignore the recovery accuracy of secret information. In this paper, the steganography method based on invertible neural networks is proposed, which can generate stego images with high invisibility and security and can achieve lossless recovery for secret information. In addition, this paper introduces a mapping module that can compress information actually embedded to improve the quality of the stego image and its antidetection ability. In order to restore message and prevent loss, the secret information is converted into a binary sequence and then embedded in the cover image through the forward operation of the invertible neural networks. This information will then be recovered from the stego image through the inverse operation of the invertible neural networks. Experimental results show that the proposed method in this paper has achieved competitive results in the visual quality and safety of stego images and achieved 100% accuracy in information extraction. Full article
(This article belongs to the Special Issue Meta-Heuristic Optimizations for the Security and Energy Efficiency of Wireless Sensor Networks)
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15 pages, 2668 KiB  
Article
Node Deployment Optimization for Wireless Sensor Networks Based on Virtual Force-Directed Particle Swarm Optimization Algorithm and Evidence Theory
by Liangshun Wu, Junsuo Qu, Haonan Shi and Pengfei Li
Entropy 2022, 24(11), 1637; https://doi.org/10.3390/e24111637 - 10 Nov 2022
Cited by 4 | Viewed by 1314
Abstract
Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model [...] Read more.
Wireless sensor network deployment should be optimized to maximize network coverage. The D-S evidence theory is an effective means of information fusion that can handle not only uncertainty and inconsistency, but also ambiguity and instability. This work develops a node sensing probability model based on D-S evidence. When there are major evidence disputes, the priority factor is introduced to reassign the sensing probability, with the purpose of addressing the issue of the traditional D-S evidence theory aggregation rule not conforming to the actual scenario and producing an erroneous result. For optimizing node deployment, a virtual force-directed particle swarm optimization approach is proposed, and the optimization goal is to maximize network coverage. The approach employs the virtual force algorithm, whose virtual forces are fine-tuned by the sensing probability. The sensing probability is fused by D-S evidence to drive particle swarm evolution and accelerate convergence. The simulation results show that the virtual force-directed particle swarm optimization approach improves network coverage while taking less time. Full article
(This article belongs to the Special Issue Meta-Heuristic Optimizations for the Security and Energy Efficiency of Wireless Sensor Networks)
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