Submit to Sensors Review for Sensors Propose a Special Issue

Journal Browser

► Journal Browser

Intelligent Sensing, Control and Optimization of Networks

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 20047

Share This Special Issue

Special Issue Editors

Prof. Dr. Guanghui Wen

E-Mail Website
Guest Editor

Department of Systems Science, School of Mathematics, Southeast University, Nanjing 211189, China
Interests: autonomous intelligent systems; complex networked systems; distributed control and optimization; resilient control; distributed reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Dr. Junjie Fu

E-Mail Website
Guest Editor

Department of Systems Science, School of Mathematics, Southeast University, Nanjing 211189, China
Interests: distributed control of constrained multi-agent systems; learning-based control; smart sensor networks

Dr. Jialing Zhou

E-Mail Website
Guest Editor

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: swarm Intelligence; distributed control and optimization; reinforcement learning; and networked games

Special Issue Information

Dear Colleagues,

Complex cyber-physical networks (CCPNs) refer to next-generation networked systems whose normal functioning largely relies on the cyber-physical interactions among neighbored cyber-physical individuals. Many modern critical infrastructures can be modelled as CCPNs. Typical examples of such systems include power grids, the Internet, industrial sensor networks and the public transportation systems. Advances in sensing, communication and computation technology open opportunities to implement various intelligent sensing, control and optimization strategies for CPNNs. Within this context, many advantages can be yielded from CPNNs compared with the traditional networked systems (including both complex networks and multi-agent systems) such as a high precision sensing and control ability, powerful on-line optimization ability, favorable fault-tolerant capability and high scalability. However, many issues need to be addressed before all the above-mentioned advantages can be realized. This Special Issue aims to establish a forum for international researchers from different fields of electrical engineering, systems and control theory, computer science and applied mathematics, to present and evaluate the most recent developments and new ideas in the intelligent sensing, control and optimization of CPNNs, regarding both fundamental theory and practical applications.

Scope: This Special Issue is related to future smart sensors and intelligent sensor networks where effective control and optimization strategies are needed to fully capitalize the advantages of these advanced systems. Furthermore, data-based adaptive sensing, control and optimization techniques are also related to the signal processing, data fusion and deep learning in sensor networks. Therefore, the Special Issue is well aligned with the scope of the journal.

Prof. Dr. Guanghui Wen
Dr. Junjie Fu
Dr. Jialing Zhou
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. Sensors is an international peer-reviewed open access semimonthly 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

intelligent sensing
cyber-physical systems
distributed control
distributed reinforcement learning
distributed optimization
online learning
data-based coordination control
distributed artificial intelligence technology

Published Papers (13 papers)

Download All Papers

Editorial

Jump to: Research, Review

3 pages, 146 KiB

Open AccessEditorial

Intelligent Sensing, Control and Optimization of Networks

by Guanghui Wen, Junjie Fu and Jialing Zhou

Sensors 2024, 24(5), 1648; https://doi.org/10.3390/s24051648 - 3 Mar 2024

Viewed by 600

Abstract

The development of many modern critical infrastructures calls for the integration of advanced technologies and algorithms to enhance the performance, efficiency, and reliability of network systems [...] Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

Research

Jump to: Editorial, Review

26 pages, 4299 KiB

Open AccessArticle

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

by Hao Guo, Lin Lin, Jinlei Wu, Yancheng Lv and Changsheng Tong

Sensors 2024, 24(8), 2600; https://doi.org/10.3390/s24082600 - 18 Apr 2024

Viewed by 427

Abstract

The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

22 pages, 6892 KiB

Open AccessArticle

A Semantic Segmentation Method Based on AS-Unet++ for Power Remote Sensing of Images

by Guojun Nan, Haorui Li, Haibo Du, Zhuo Liu, Min Wang and Shuiqing Xu

Sensors 2024, 24(1), 269; https://doi.org/10.3390/s24010269 - 2 Jan 2024

Cited by 2 | Viewed by 828

Abstract

In order to achieve the automatic planning of power transmission lines, a key step is to precisely recognize the feature information of remote sensing images. Considering that the feature information has different depths and the feature distribution is not uniform, a semantic segmentation method based on a new AS-Unet++ is proposed in this paper. First, the atrous spatial pyramid pooling (ASPP) and the squeeze-and-excitation (SE) module are added to traditional Unet, such that the sensing field can be expanded and the important features can be enhanced, which is called AS-Unet. Second, an AS-Unet++ structure is built by using different layers of AS-Unet, such that the feature extraction parts of each layer of AS-Unet are stacked together. Compared with Unet, the proposed AS-Unet++ automatically learns features at different depths and determines a depth with optimal performance. Once the optimal number of network layers is determined, the excess layers can be pruned, which will greatly reduce the number of trained parameters. The experimental results show that the overall recognition accuracy of AS-Unet++ is significantly improved compared to Unet. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

16 pages, 4852 KiB

Open AccessArticle

Optimal Distributed Finite-Time Fusion Method for Multi-Sensor Networks under Dynamic Communication Weight

by Hang Yu, Keren Dai, Qingyu Li, Haojie Li and He Zhang

Sensors 2023, 23(17), 7397; https://doi.org/10.3390/s23177397 - 24 Aug 2023

Cited by 2 | Viewed by 864

Abstract

Aiming at the problem of distributed state estimation in sensor networks, a novel optimal distributed finite-time fusion filtering method based on dynamic communication weights has been developed. To tackle the fusion errors caused by incomplete node information in distributed sensor networks, the concept of limited iterations of global information aggregation was introduced, namely, fast finite-time convergence techniques. Firstly, a local filtering algorithm architecture was constructed to achieve fusion error convergence within a limited number of iterations. The maximum number of iterations was derived to be the diameter of the communication topology graph in the sensor network. Based on this, the matrix weight fusion was used to combine the local filtering results, thereby achieving optimal estimation in terms of minimum variance. Next, by introducing the generalized information quality (GIQ) calculation method and associating it with the local fusion result bias, the relative communication weights were obtained and embedded in the fusion algorithm. Finally, the effectiveness and feasibility of the proposed algorithm were validated through numerical simulations and experimental tests. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

13 pages, 2432 KiB

Open AccessCommunication

Transmission Line-Planning Method Based on Adaptive Resolution Grid and Improved Dijkstra Algorithm

by Guojun Nan, Zhuo Liu, Haibo Du, Wenwu Zhu and Shuiqing Xu

Sensors 2023, 23(13), 6214; https://doi.org/10.3390/s23136214 - 7 Jul 2023

Cited by 2 | Viewed by 1254

Abstract

An improved Dijkstra algorithm based on adaptive resolution grid (ARG) is proposed to assist manual transmission line planning, shorten the construction period and achieve lower cost and higher efficiency of line selection. Firstly, the semantic segmentation network is used to change the remote sensing image into a ground object-identification image and the grayscale image of the ground object-identification image is rasterized. The ARG map model is introduced to greatly reduce the number of redundant grids, which can effectively reduce the time required to traverse the grids. Then, the Dijkstra algorithm is combined with the ARG and the neighborhood structure of the grid is a multi-center neighborhood. An improved method of bidirectional search mechanism based on ARG and inflection point-correction is adopted to greatly increase the running speed. The inflection point-correction reduces the number of inflection points and reduces the cost. Finally, according to the results of the search, the lowest-cost transmission line is determined. The experimental results show that this method aids manual planning by providing a route for reference, improving planning efficiency while shortening the duration, and reducing the time spent on algorithm debugging. Compared with the comparison algorithm, this method is faster in running speed and better in cost saving and has a broader application prospect. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

16 pages, 1959 KiB

Open AccessArticle

Optimal Linear Filter Based on Feedback Structure for Sensing Network with Correlated Noises and Data Packet Dropout

by Weichen Shang, Hang Yu, Qingyu Li, He Zhang and Keren Dai

Sensors 2023, 23(12), 5673; https://doi.org/10.3390/s23125673 - 17 Jun 2023

Viewed by 840

Abstract

This paper is concerned with the estimation of correlated noise and packet dropout for information fusion in distributed sensing networks. By studying the problem of the correlation of correlated noise in sensor network information fusion, a matrix weight fusion method with a feedback structure is proposed to deal with the interrelationship between multi-sensor measurement noise and estimation noise, and the method can achieve optimal estimation in the sense of linear minimum variance. Based on this, a method is proposed using a predictor with a feedback structure to compensate for the current state quantity to deal with packet dropout that occurs during multi-sensor information fusion, which can reduce the covariance of the fusion results. Simulation results show that the algorithm can solve the problem of information fusion noise correlation and packet dropout in sensor networks, and effectively reduce the fusion covariance with feedback. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

21 pages, 6193 KiB

Open AccessArticle

Improved Optimization Strategy Based on Region Division for Collaborative Multi-Agent Coverage Path Planning

by Yijie Qin, Lei Fu, Dingxin He and Zhiwei Liu

Sensors 2023, 23(7), 3596; https://doi.org/10.3390/s23073596 - 30 Mar 2023

Cited by 3 | Viewed by 2246

Abstract

In this paper, we investigate the algorithms for traversal exploration and path coverage of target regions using multiple agents, enabling the efficient deployment of a set of agents to cover a complex region. First, the original multi-agent path planning problem (mCPP) is transformed into several single-agent sub-problems, by dividing the target region into multiple balanced sub-regions, which reduces the explosive combinatorial complexity; subsequently, closed-loop paths are planned in each sub-region by the rapidly exploring random trees (RRT) algorithm to ensure continuous exploration and repeated visits to each node of the target region. On this basis, we also propose two improvements: for the corner case of narrow regions, the use of geodesic distance is proposed to replace the Eulerian distance in Voronoi partitioning, and the iterations for balanced partitioning can be reduced by more than one order of magnitude; the Dijkstra algorithm is introduced to assign a smaller weight to the path cost when the geodesic direction changes, which makes the region division more “cohesive”, thus greatly reducing the number of turns in the path and making it more robust. The final optimization algorithm ensures the following characteristics: complete coverage of the target area, wide applicability of multiple area shapes, reasonable distribution of exploration tasks, minimum average waiting time, and sustainable exploration without any preparation phase. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

15 pages, 1134 KiB

Open AccessArticle

A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm

by Quansheng Sun, Tianjun Liao, Haibo Du, Yinfeng Zhao and Chih-Chiang Chen

Sensors 2023, 23(1), 447; https://doi.org/10.3390/s23010447 - 1 Jan 2023

Cited by 2 | Viewed by 1532

Abstract

The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

13 pages, 2620 KiB

Open AccessArticle

Fixed-Time Coverage Control of Mobile Robot Networks Considering the Time Cost Metric

by Qihai Sun, Tianjun Liao, Zhi-Wei Liu, Ming Chi and Dingxin He

Sensors 2022, 22(22), 8938; https://doi.org/10.3390/s22228938 - 18 Nov 2022

Cited by 2 | Viewed by 1298

Abstract

In this work, we studied the area coverage control problem (ACCP) based on the time cost metric of a robot network with an input disturbance in a dynamic environment, which was modeled by a time-varying risk density function. A coverage control method based on the time cost metric was proposed. The area coverage task that considers the time cost consists of two phases: the robot network is driven to cover the task area with a time-optimal effect in the first phase; the second phase is when the accident occurs and the robot is driven to the accident site at maximum speed. Considering that there were movable objects in the task area, a time-varying risk density function was used to describe the risk degree at different locations in the task area. In the presence of the input disturbance, a robust controller was designed to drive each robot, with different maximum control input values, to the position that locally minimized the time cost metric function in a fixed time, and the conditions for maximum control input were obtained. Finally, simulation results and comparison result are presented in this paper. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

18 pages, 2902 KiB

Open AccessArticle

A Novel Adaptive Deskewing Algorithm for Document Images

by Wuzhida Bao, Cihui Yang, Shiping Wen, Mengjie Zeng, Jianyong Guo, Jingting Zhong and Xingmiao Xu

Sensors 2022, 22(20), 7944; https://doi.org/10.3390/s22207944 - 18 Oct 2022

Cited by 3 | Viewed by 2819

Abstract

Document scanning often suffers from skewing, which may seriously influence the efficiency of Optical Character Recognition (OCR). Therefore, it is necessary to correct the skewed document before document image information analysis. In this article, we propose a novel adaptive deskewing algorithm for document images, which mainly includes Skeleton Line Detection (SKLD), Piecewise Projection Profile (PPP), Morphological Clustering (MC), and the image classification method. The image type is determined firstly based on the image’s layout feature. Thus, adaptive correcting is applied to deskew the image according to its type. Our method maintains high accuracy on the Document Image Skew Estimation Contest (DISEC’2013) and PubLayNet datasets, which achieved 97.6% and 80.1% accuracy, respectively. Meanwhile, extensive experiments show the superiority of the proposed algorithm. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

14 pages, 1408 KiB

Open AccessArticle

A New Positioning Method for Climbing Robots Based on 3D Model of Transmission Tower and Visual Sensor

by Yansheng Liu, Junyi You, Haibo Du, Shuai Chang and Shuiqing Xu

Sensors 2022, 22(19), 7288; https://doi.org/10.3390/s22197288 - 26 Sep 2022

Cited by 1 | Viewed by 1885

Abstract

With the development of robot technology and the extensive application of robots, the research on special robots for some complex working environments has gradually become a hot topic. As a special robot applied to transmission towers, the climbing robot can replace humans to work at high altitudes to complete bolt tightening, detection, and other tasks, which improves the efficiency of transmission tower maintenance and ensures personal safety. However, it is mostly the ability to autonomously locate in the complex environment of the transmission tower that limits the industrial applications of the transmission tower climbing robot. This paper proposes an intelligent positioning method that integrates the three-dimensional information model of transmission tower and visual sensor data, which can assist the robot in climbing and adjusting to the designated working area to guarantee the working accuracy of the climbing robots. The experimental results show that the positioning accuracy of the method is within 1 cm. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

24 pages, 10393 KiB

Open AccessArticle

Design of FOPID Controller for Pneumatic Control Valve Based on Improved BBO Algorithm

by Min Zhu, Zihao Xu, Zhaoyu Zang and Xueping Dong

Sensors 2022, 22(17), 6706; https://doi.org/10.3390/s22176706 - 5 Sep 2022

Cited by 4 | Viewed by 2301

Abstract

Aiming at the problems of nonlinearity and inaccuracy in the model of the pneumatic control valve position in the industrial control process, a valve position control method based on a fractional-order PID controller is proposed. The working principle of the pneumatic control valve is analyzed, and its mathematical model is established. In order to improve the accuracy of the model, an improved biogeography-based optimization algorithm is proposed to tune the parameters of the fractional-order PID controller in view of the wide range and high complexity of the fractional-order PID controller. The initialization of the chaotic graph, the adjustment of the migration model, and the improvement of the migration operator and the mutation operator are introduced to improve the algorithm optimization ability, which is used for the model identification of the control valve control system. The simulation and experimental results clearly show that, compared with the integer-order PID controller, the designed fractional-order PID controller has faster response speed and control accuracy, which can better meet the requirements of pneumatic control valve position control. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

24 pages, 440 KiB

Open AccessReview

A Brief Survey of Recent Advances and Methodologies for the Security Control of Complex Cyber–Physical Networks

by Ying Wan and Jinde Cao

Sensors 2023, 23(8), 4013; https://doi.org/10.3390/s23084013 - 15 Apr 2023

Cited by 1 | Viewed by 1552

Abstract

Complex cyber–physical networks combine the prominent features of complex networks and cyber–physical systems (CPSs), and the interconnections between the cyber layer and physical layer usually pose significant impacts on its normal operation. Many vital infrastructures, such as electrical power grids, can be effectively modeled as complex cyber–physical networks. Given the growing importance of complex cyber–physical networks, the issue of their cybersecurity has become a significant concern in both industry and academic fields. This survey is focused on some recent developments and methodologies for secure control of complex cyber–physical networks. Besides the single type of cyberattack, hybrid cyberattacks are also surveyed. The examination encompasses both cyber-only hybrid attacks and coordinated cyber–physical attacks that leverage the strengths of both physical and cyber attacks. Then, special focus will be paid to proactive secure control. Reviewing existing defense strategies from topology and control perspectives aims to proactively enhance security. The topological design allows the defender to resist potential attacks in advance, while the reconstruction process can aid in reasonable and practical recovery from unavoidable attacks. In addition, the defense can adopt active switching-based control and moving target defense strategies to reduce stealthiness, increase the cost of attacks, and limit the attack impacts. Finally, conclusions are drawn and some potential research topics are suggested. Full article

(This article belongs to the Special Issue Intelligent Sensing, Control and Optimization of Networks)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Intelligent Sensing, Control and Optimization of Networks

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (13 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI