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Special Issue "Smart Mobile and Sensing Applications"

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

Deadline for manuscript submissions: 1 December 2023 | Viewed by 8687

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Special Issue Editors

Dr. Chien Aun Chan

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Guest Editor

Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
Interests: future wireless networks; 5G; mobile edge networks; distributed computing; Internet of Things; big data analytics; cloud computing; network service virtualization; optical networks

Dr. Ming Yan

E-Mail Website
Guest Editor

School of Information and Communications Engineering, Communication University of China, Beijing 100024,China
Interests: computer vision; convolutional neural nets; learning (artificial intelligence); object detection; 5G mobile communication; cache storage; feature extraction; mobile computing; object recognition; Markov proces
Special Issues, Collections and Topics in MDPI journals

Dr. Chunguo Li

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Guest Editor

School of Information Science and Engineering, Southeast University, Nanjing, China
Interests: artificial intelligence-based image/video signal processing; algorithm design; wireless communications; cyberspace security theories and techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in mobile sensing technologies leveraged by big data analytics and machine learning enable a plethora of applications that could improve productivity, safety, health, and efficiency in a diverse range of use case scenarios—for example, the use of mobile sensing with wearables to assist with remote learning especially during the pandemic, wireless sensing and tracking of consumers’ mobilities to enhance security and user experience, mobile wearable sensing and monitoring to ensure safety and in turns improve productivity in harsh working environments, mobile sensing for social behavioral research and sports analytics, mobile sensing in AR and VR, and much more. Therefore, this Special Issue aims to collect the top-quality research work focusing on addressing emerging challenges in smart mobile and sensing, and smart applications and use case scenarios that could help to enhance our daily lives.

The key topics of interest include (but are not limited to):

Next-generation smart mobile sensing technology;
Smart mobile sensing in wearables;
Smart mobile and sensing design and applications;
Machine learning, deep learning, and big data analytics;
Signal processing for smart sensing;
Privacy-preserving smart sensing;
Surveillance and monitoring applications;
Intelligent AR/VR application with machine/deep learning;
Multimodal/reinforcement/transfer/adversarial learning.

Dr. Chien Aun Chan
Dr. Ming Yan
Dr. Chunguo Li
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 2400 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.

Published Papers (11 papers)

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Research

Open AccessArticle

P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study

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Sensors 2023, 23(6), 3129; https://doi.org/10.3390/s23063129 - 15 Mar 2023

Viewed by 303

Abstract

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks

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Sensors 2023, 23(5), 2518; https://doi.org/10.3390/s23052518 - 24 Feb 2023

Viewed by 422

Abstract

In 5G/B5G communication systems, network slicing is utilized to tackle the problem of the allocation of network resources for diverse services with changing demands. We proposed an algorithm that prioritizes the characteristic requirements of two different services and tackles the problem of allocation and scheduling of resources in the hybrid services system with eMBB and URLLC. Firstly, the resource allocation and scheduling are modeled, subject to the rate and delay constraints of both services. Secondly, the purpose of adopting a dueling deep Q network (Dueling DQN) is to approach the formulated non-convex optimization problem innovatively, in which a resource scheduling mechanism and the

ϵ

-greedy strategy were utilized to select the optimal resource allocation action. Moreover, the reward-clipping mechanism is introduced to enhance the training stability of Dueling DQN. Meanwhile, we choose a suitable bandwidth allocation resolution to increase flexibility in resource allocation. Finally, the simulations indicate that the proposed Dueling DQN algorithm has excellent performance in terms of quality of experience (QoE), spectrum efficiency (SE) and network utility, and the scheduling mechanism makes the performance much more stable. In contrast with Q-learning, DQN as well as Double DQN, the proposed algorithm based on Dueling DQN improves the network utility by 11%, 8% and 2%, respectively. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information

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Sensors 2023, 23(4), 2285; https://doi.org/10.3390/s23042285 - 17 Feb 2023

Viewed by 469

Abstract

Movie scene event extraction is a practical task in media analysis, which aims at extracting structured events from unstructured movie scripts. However, although there have been many studies regarding open domain event extraction, there have only been a few studies focusing on movie scene event extraction. Specifically aimed at instances where different argument roles have the same characteristics in a movie scene, we propose the utilization of the correlation between different argument roles, which is beneficial for both movie scene trigger extraction (trigger identification and classification) and movie scene argument extraction (argument identification and classification) in event extraction. To model the correlation between different argument roles, we propose the superior role concept (SRC), a high-level role concept based upon the ordinary argument role. In this paper, we introduce a new movie scene event extraction model with two main features: (1) an attentive high-level argument role module to capture SRC information and (2) an SRC-based graph attention network (GAT) to fuse the argument role correlation information into semantic embeddings. To evaluate the performance of our model, we constructed a movie scene event extraction dataset named MovieSceneEvent and also conducted experiments on a widely used dataset to compare the results with other models. The experimental results show that our model outperforms competitive models, and the correlation information of argument roles helps to improve the performance of movie scene event extraction. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning

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Sensors 2023, 23(4), 1751; https://doi.org/10.3390/s23041751 - 04 Feb 2023

Cited by 1 | Viewed by 413

Abstract

A multi-swarm-evolutionary structure based on the parasitic relationship in the biosphere is proposed in this paper and, according to the conception, the Para-PSO-ABC algorithm (ParaPA), combined with merits of the modified particle swarm optimization (MPSO) and artificial bee colony algorithm (ABC), is conducted with the multimodal routing strategy to enhance the safety and the cost issue for the mobile robot path planning problem. The evolution is divided into three stages, where the first is the independent evolutionary stage, with the same evolution strategies for each swarm. The second is the fusion stage, in which individuals are evolved hierarchically in the parasitism structure. Finally, in the interaction stage, a multi-swarm-elite strategy is used to filter the information through a predefined cross function among swarms. Meanwhile, the segment obstacle-avoiding strategy is proposed to accelerate the searching speed with two fitness functions. The best path is selected according to the performance on the safety and consumption issues. The introduced algorithm is examined with different obstacle allocations and simulated in the real routing environment compared with some typical algorithms. The results verify the productiveness of the parasitism-relation-based structure and the stage-based evolution strategy in path planning. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Indoor Positioning Design for Mobile Phones via Integrating a Single Microphone Sensor and an H₂ Estimator

Yung-Hsiang Chen

Pei-Yu Chang

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Yung-Yue Chen

Sensors 2023, 23(3), 1508; https://doi.org/10.3390/s23031508 - 29 Jan 2023

Viewed by 417

Abstract

An indoor positioning design developed for mobile phones by integrating a single microphone sensor, an H₂ estimator, and tagged sound sources, all with distinct frequencies, is proposed in this investigation. From existing practical experiments, the results summarize a key point for achieving a satisfactory indoor positioning: The estimation accuracy of the instantaneous sound pressure level (SPL) that is inevitably affected by random variations of environmental corruptions dominates the indoor positioning performance. Following this guideline, the proposed H₂ estimation design, accompanied by a sound pressure level model, is developed for effectively mitigating the influences of received signal strength (RSS) variations caused by reverberation, reflection, refraction, etc. From the simulation results and practical tests, the proposed design delivers a highly promising indoor positioning performance: an average positioning RMS error of 0.75 m can be obtained, even under the effects of heavy environmental corruptions. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Research on Smart Tourism Oriented Sensor Network Construction and Information Service Mode

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Sensors 2022, 22(24), 10008; https://doi.org/10.3390/s222410008 - 19 Dec 2022

Viewed by 721

Abstract

Smart tourism is the latest achievement of tourism development at home and abroad. It is also an essential part of the smart city. Promoting the application of computer and sensor technology in smart tourism is conducive to improving the efficiency of public tourism services and guiding the innovation of the tourism public service mode. In this paper, we have proposed a new method of using data collected by sensor networks. We have developed and deployed sensors to collect data, which are transmitted to the modular cloud platform, and combined with cluster technology and an Uncertain Support Vector Classifier (A-USVC) location prediction method to assist in emergency events. Considering the attraction of tourists, the system also incorporated human trajectory analysis and intensity of interaction as consideration factors to validate the spatial dynamics of different interests and enhance the tourists’ experience. The system explored the innovative road of computer technology to boost the development of smart tourism, which helps to promote the high-quality development of tourism. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

A Novel Improved YOLOv3-SC Model for Individual Pig Detection

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Sensors 2022, 22(22), 8792; https://doi.org/10.3390/s22228792 - 15 Nov 2022

Cited by 1 | Viewed by 803

Abstract

Pork is the most widely consumed meat product in the world, and achieving accurate detection of individual pigs is of great significance for intelligent pig breeding and health monitoring. Improved pig detection has important implications for improving pork production and quality, as well as economics. However, most of the current approaches are based on manual labor, resulting in unfeasible performance. In order to improve the efficiency and effectiveness of individual pig detection, this paper describes the development of an attention module enhanced YOLOv3-SC model (YOLOv3-SPP-CBAM. SPP denotes the Spatial Pyramid Pooling module and CBAM indicates the Convolutional Block Attention Module). Specifically, leveraging the attention module, the network will extract much richer feature information, leading the improved performance. Furthermore, by integrating the SPP structured network, multi-scale feature fusion can be achieved, which makes the network more robust. On the constructed dataset of 4019 samples, the experimental results showed that the YOLOv3-SC network achieved 99.24% mAP in identifying individual pigs with a detection time of 16 ms. Compared with the other popular four models, including YOLOv1, YOLOv2, Faster-RCNN, and YOLOv3, the mAP of pig identification was improved by 2.31%, 1.44%, 1.28%, and 0.61%, respectively. The YOLOv3-SC proposed in this paper can achieve accurate individual detection of pigs. Consequently, this novel proposed model can be employed for the rapid detection of individual pigs on farms, and provides new ideas for individual pig detection. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Learning Moiré Pattern Elimination in Both Frequency and Spatial Domains for Image Demoiréing

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Sensors 2022, 22(21), 8322; https://doi.org/10.3390/s22218322 - 30 Oct 2022

Viewed by 581

Abstract

Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model’s ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning

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Sensors 2022, 22(21), 8139; https://doi.org/10.3390/s22218139 - 24 Oct 2022

Cited by 2 | Viewed by 793

Abstract

Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network’s spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals

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Sensors 2022, 22(15), 5855; https://doi.org/10.3390/s22155855 - 05 Aug 2022

Cited by 4 | Viewed by 1382

Abstract

Gesture recognition based on wearable devices is one of the vital components of human–computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models’ test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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Open AccessArticle

Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force

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Sensors 2022, 22(13), 4956; https://doi.org/10.3390/s22134956 - 30 Jun 2022

Viewed by 771

Abstract

The gradient vector flow (GVF) model has been widely used in the field of computer image segmentation. In order to achieve better results in image processing, there are many research papers based on the GVF model. However, few models include image structure. In this paper, the smoothness constraint formula of the GVF model is re-expressed in matrix form, and the image knot represented by the Hessian matrix is included in the GVF model. Through the processing of this process, the relevant diffusion partial differential equation has anisotropy. The GVF model based on the Hessian matrix (HBGVF) has many advantages over other relevant GVF methods, such as accurate convergence to various concave surfaces, excellent weak edge retention ability, and so on. The following will prove the advantages of our proposed model through theoretical analysis and various comparative experiments. Full article

(This article belongs to the Special Issue Smart Mobile and Sensing Applications)

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