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Smart Mobile and Sensing Applications

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

Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 41306

Special Issue Editors


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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
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
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

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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.

Published Papers (20 papers)

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19 pages, 7070 KiB  
Article
Innovating Household Food Waste Management: A User-Centric Approach with AHP–TRIZ Integration
by Shuyun Wang, Hyunyim Park and Jifeng Xu
Sensors 2024, 24(3), 820; https://doi.org/10.3390/s24030820 - 26 Jan 2024
Viewed by 1007
Abstract
Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design [...] Read more.
Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users’ actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users’ genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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19 pages, 2802 KiB  
Article
Remote Multi-Person Heart Rate Monitoring with Smart Speakers: Overcoming Separation Constraint
by Thu Tran, Dong Ma and Rajesh Balan
Sensors 2024, 24(2), 382; https://doi.org/10.3390/s24020382 - 8 Jan 2024
Viewed by 1196
Abstract
Heart rate is a key vital sign that can be used to understand an individual’s health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones [...] Read more.
Heart rate is a key vital sign that can be used to understand an individual’s health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present an approach that can minimize interference and thereby enable simultaneous heart rate monitoring of multiple individuals in close proximity using a commonly available smart speaker prototype. Our user study, conducted under various real-life scenarios, demonstrates the system’s accuracy in sensing two users’ heart rates when they are seated next to each other with a median error of 0.66 beats per minute (bpm). Moreover, the system can successfully monitor up to four people in close proximity. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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17 pages, 2282 KiB  
Article
A Short Video Classification Framework Based on Cross-Modal Fusion
by Nuo Pang, Songlin Guo, Ming Yan and Chien Aun Chan
Sensors 2023, 23(20), 8425; https://doi.org/10.3390/s23208425 - 12 Oct 2023
Viewed by 1236
Abstract
The explosive growth of online short videos has brought great challenges to the efficient management of video content classification, retrieval, and recommendation. Video features for video management can be extracted from video image frames by various algorithms, and they have been proven to [...] Read more.
The explosive growth of online short videos has brought great challenges to the efficient management of video content classification, retrieval, and recommendation. Video features for video management can be extracted from video image frames by various algorithms, and they have been proven to be effective in the video classification of sensor systems. However, frame-by-frame processing of video image frames not only requires huge computing power, but also classification algorithms based on a single modality of video features cannot meet the accuracy requirements in specific scenarios. In response to these concerns, we introduce a short video categorization architecture centered around cross-modal fusion in visual sensor systems which jointly utilizes video features and text features to classify short videos, avoiding processing a large number of image frames during classification. Firstly, the image space is extended to three-dimensional space–time by a self-attention mechanism, and a series of patches are extracted from a single image frame. Each patch is linearly mapped into the embedding layer of the Timesformer network and augmented with positional information to extract video features. Second, the text features of subtitles are extracted through the bidirectional encoder representation from the Transformers (BERT) pre-training model. Finally, cross-modal fusion is performed based on the extracted video and text features, resulting in improved accuracy for short video classification tasks. The outcomes of our experiments showcase a substantial superiority of our introduced classification framework compared to alternative baseline video classification methodologies. This framework can be applied in sensor systems for potential video classification. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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20 pages, 5660 KiB  
Article
DPACFuse: Dual-Branch Progressive Learning for Infrared and Visible Image Fusion with Complementary Self-Attention and Convolution
by Huayi Zhu, Heshan Wu, Xiaolong Wang, Dongmei He, Zhenbing Liu and Xipeng Pan
Sensors 2023, 23(16), 7205; https://doi.org/10.3390/s23167205 - 16 Aug 2023
Cited by 2 | Viewed by 1158
Abstract
Infrared and visible image fusion aims to generate a single fused image that not only contains rich texture details and salient objects, but also facilitates downstream tasks. However, existing works mainly focus on learning different modality-specific or shared features, and ignore the importance [...] Read more.
Infrared and visible image fusion aims to generate a single fused image that not only contains rich texture details and salient objects, but also facilitates downstream tasks. However, existing works mainly focus on learning different modality-specific or shared features, and ignore the importance of modeling cross-modality features. To address these challenges, we propose Dual-branch Progressive learning for infrared and visible image fusion with a complementary self-Attention and Convolution (DPACFuse) network. On the one hand, we propose Cross-Modality Feature Extraction (CMEF) to enhance information interaction and the extraction of common features across modalities. In addition, we introduce a high-frequency gradient convolution operation to extract fine-grained information and suppress high-frequency information loss. On the other hand, to alleviate the CNN issues of insufficient global information extraction and computation overheads of self-attention, we introduce the ACmix, which can fully extract local and global information in the source image with a smaller computational overhead than pure convolution or pure self-attention. Extensive experiments demonstrated that the fused images generated by DPACFuse not only contain rich texture information, but can also effectively highlight salient objects. Additionally, our method achieved approximately 3% improvement over the state-of-the-art methods in MI, Qabf, SF, and AG evaluation indicators. More importantly, our fused images enhanced object detection and semantic segmentation by approximately 10%, compared to using infrared and visible images separately. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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15 pages, 2234 KiB  
Article
An Asymmetric Encryption-Based Key Distribution Method for Wireless Sensor Networks
by Yuan Cheng, Yanan Liu, Zheng Zhang and Yanxiu Li
Sensors 2023, 23(14), 6460; https://doi.org/10.3390/s23146460 - 17 Jul 2023
Cited by 4 | Viewed by 1066
Abstract
Wireless sensor networks are usually applied in hostile areas where nodes can easily be monitored and captured by an adversary. Designing a key distribution scheme with high security and reliability, low hardware requirements, and moderate communication load is crucial for wireless sensor networks. [...] Read more.
Wireless sensor networks are usually applied in hostile areas where nodes can easily be monitored and captured by an adversary. Designing a key distribution scheme with high security and reliability, low hardware requirements, and moderate communication load is crucial for wireless sensor networks. To address the above objectives, we propose a new key distribution scheme based on an ECC asymmetric encryption algorithm. The two-way authentication mechanism in the proposed scheme not only prevents illegal nodes from accessing the network, but also prevents fake base stations from communicating with the nodes. The complete key distribution and key update methods ensure the security of session keys in both static and dynamic environments. The new key distribution scheme provides a significant performance improvement compared to the classical key distribution schemes for wireless sensor networks without sacrificing reliability. Simulation results show that the proposed new scheme reduces the communication load and key storage capacity, has significant advantages in terms of secure connectivity and attack resistance, and is fully applicable to wireless sensor networks. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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24 pages, 8072 KiB  
Article
A Mobile Sensing Framework for Bridge Modal Identification through an Inverse Problem Solution Procedure and Moving-Window Time Series Models
by Mohammad Talebi-Kalaleh and Qipei Mei
Sensors 2023, 23(11), 5154; https://doi.org/10.3390/s23115154 - 28 May 2023
Cited by 1 | Viewed by 1433
Abstract
With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing [...] Read more.
With the rise and development of smart infrastructures, there has been a great demand for installing automatic monitoring systems on bridges, which are key members of transportation networks. In this regard, utilizing the data collected by the sensors mounted on the vehicles passing over the bridge can reduce the costs of the monitoring systems, compared with the traditional systems where fixed sensors are mounted on the bridge. This paper presents an innovative framework for determining the response and for identifying modal characteristics of the bridge, utilizing only the accelerometer sensors on the moving vehicle passing over it. In the proposed approach, the acceleration and displacement response of some virtual fixed nodes on the bridge is first determined using the acceleration response of the vehicle axles as the input. An inverse problem solution approach based on a linear and a novel cubic spline shape function provides the preliminary estimations of the bridge’s displacement and acceleration responses, respectively. Since the inverse solution approach is only capable of determining the response signal of the nodes with high accuracy in the vicinity of the vehicle axles, a new moving-window signal prediction method based on auto-regressive with exogenous time series models (ARX) is proposed to complete the responses in the regions with large errors (invalid regions). The mode shapes and natural frequencies of the bridge are identified using a novel approach that integrates the results of singular value decomposition (SVD) on the predicted displacement responses and frequency domain decomposition (FDD) on the predicted acceleration responses. To evaluate the proposed framework, various numerical but realistic models for a single-span bridge under the effect of a moving mass are considered; the effects of different levels of ambient noise, the number of axles of the passing vehicle, and the effect of its speed on the accuracy of the method are investigated. The results show that the proposed method can identify the characteristics of the three main modes of the bridge with high accuracy. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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16 pages, 2905 KiB  
Article
TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network
by Wangli Hao, Kai Zhang, Li Zhang, Meng Han, Wangbao Hao, Fuzhong Li and Guoqiang Yang
Sensors 2023, 23(11), 5092; https://doi.org/10.3390/s23115092 - 26 May 2023
Cited by 3 | Viewed by 1608
Abstract
Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often [...] Read more.
Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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12 pages, 3049 KiB  
Article
Supporting Tremor Rehabilitation Using Optical See-Through Augmented Reality Technology
by Kai Wang, Dong Tan, Zhe Li and Zhi Sun
Sensors 2023, 23(8), 3924; https://doi.org/10.3390/s23083924 - 12 Apr 2023
Cited by 1 | Viewed by 1496
Abstract
Tremor is a movement disorder that significantly impacts an individual’s physical stability and quality of life, and conventional medication or surgery often falls short in providing a cure. Rehabilitation training is, therefore, used as an auxiliary method to mitigate the exacerbation of individual [...] Read more.
Tremor is a movement disorder that significantly impacts an individual’s physical stability and quality of life, and conventional medication or surgery often falls short in providing a cure. Rehabilitation training is, therefore, used as an auxiliary method to mitigate the exacerbation of individual tremors. Video-based rehabilitation training is a form of therapy that allows patients to exercise at home, reducing pressure on rehabilitation institutions’ resources. However, it has limitations in directly guiding and monitoring patients’ rehabilitation, leading to an ineffective training effect. This study proposes a low-cost rehabilitation training system that utilizes optical see-through augmented reality (AR) technology to enable tremor patients to conduct rehabilitation training at home. The system provides one-on-one demonstration, posture guidance, and training progress monitoring to achieve an optimal training effect. To assess the system’s effectiveness, we conducted experiments comparing the movement magnitudes of individuals with tremors in the proposed AR environment and video environment, while also comparing them with standard demonstrators. Participants wore a tremor simulation device during uncontrollable limb tremors, with tremor frequency and amplitude calibrated to typical tremor standards. The results showed that participants’ limb movement magnitudes in the AR environment were significantly higher than those in the video environment, approaching the movement magnitudes of the standard demonstrators. Hence, it can be inferred that individuals receiving tremor rehabilitation in the AR environment experience better movement quality than those in the video environment. Furthermore, participant experience surveys revealed that the AR environment not only provided a sense of comfort, relaxation, and enjoyment but also effectively guided them throughout the rehabilitation process. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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14 pages, 11762 KiB  
Article
P2P Cloud Manufacturing Based on a Customized Business Model: An Exploratory Study
by Dian Huang, Ming Li, Jingfei Fu, Xuefei Ding, Weiping Luo and Xiaobao Zhu
Sensors 2023, 23(6), 3129; https://doi.org/10.3390/s23063129 - 15 Mar 2023
Cited by 4 | Viewed by 1502
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 [...] Read more.
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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24 pages, 10648 KiB  
Article
Slicing Resource Allocation Based on Dueling DQN for eMBB and URLLC Hybrid Services in Heterogeneous Integrated Networks
by Geng Chen, Rui Shao, Fei Shen and Qingtian Zeng
Sensors 2023, 23(5), 2518; https://doi.org/10.3390/s23052518 - 24 Feb 2023
Cited by 4 | Viewed by 1791
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 [...] Read more.
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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14 pages, 1817 KiB  
Article
Movie Scene Event Extraction with Graph Attention Network Based on Argument Correlation Information
by Qian Yi, Guixuan Zhang, Jie Liu and Shuwu Zhang
Sensors 2023, 23(4), 2285; https://doi.org/10.3390/s23042285 - 17 Feb 2023
Cited by 2 | Viewed by 1519
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 [...] Read more.
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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20 pages, 842 KiB  
Article
A Novel Swarm Intelligence Algorithm with a Parasitism-Relation-Based Structure for Mobile Robot Path Planning
by Hui Ren, Luli Gao, Xiaochen Shen, Mengnan Li and Wei Jiang
Sensors 2023, 23(4), 1751; https://doi.org/10.3390/s23041751 - 4 Feb 2023
Cited by 2 | Viewed by 1438
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 [...] Read more.
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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23 pages, 5087 KiB  
Article
Indoor Positioning Design for Mobile Phones via Integrating a Single Microphone Sensor and an H2 Estimator
by Yung-Hsiang Chen, Pei-Yu Chang and Yung-Yue Chen
Sensors 2023, 23(3), 1508; https://doi.org/10.3390/s23031508 - 29 Jan 2023
Cited by 1 | Viewed by 1255
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 [...] Read more.
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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17 pages, 5321 KiB  
Article
Research on Smart Tourism Oriented Sensor Network Construction and Information Service Mode
by Ruomei Tang, Chenyue Huang, Xinyu Zhao and Yunbing Tang
Sensors 2022, 22(24), 10008; https://doi.org/10.3390/s222410008 - 19 Dec 2022
Cited by 2 | Viewed by 1812
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 [...] Read more.
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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14 pages, 16168 KiB  
Article
A Novel Improved YOLOv3-SC Model for Individual Pig Detection
by Wangli Hao, Wenwang Han, Meng Han and Fuzhong Li
Sensors 2022, 22(22), 8792; https://doi.org/10.3390/s22228792 - 15 Nov 2022
Cited by 6 | Viewed by 1733
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 [...] Read more.
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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12 pages, 8286 KiB  
Article
Learning Moiré Pattern Elimination in Both Frequency and Spatial Domains for Image Demoiréing
by Chenming Liu, Yongbin Wang, Nenghuan Zhang, Ruipeng Gang and Sai Ma
Sensors 2022, 22(21), 8322; https://doi.org/10.3390/s22218322 - 30 Oct 2022
Cited by 1 | Viewed by 2000
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 [...] Read more.
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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19 pages, 3250 KiB  
Article
A Routing Optimization Method for Software-Defined Optical Transport Networks Based on Ensembles and Reinforcement Learning
by Junyan Chen, Wei Xiao, Xinmei Li, Yang Zheng, Xuefeng Huang, Danli Huang and Min Wang
Sensors 2022, 22(21), 8139; https://doi.org/10.3390/s22218139 - 24 Oct 2022
Cited by 13 | Viewed by 2866
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 [...] Read more.
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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25 pages, 8859 KiB  
Article
Multi-Category Gesture Recognition Modeling Based on sEMG and IMU Signals
by Yujian Jiang, Lin Song, Junming Zhang, Yang Song and Ming Yan
Sensors 2022, 22(15), 5855; https://doi.org/10.3390/s22155855 - 5 Aug 2022
Cited by 22 | Viewed by 3383
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 [...] Read more.
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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14 pages, 1676 KiB  
Article
Image Segmentation Using Active Contours with Hessian-Based Gradient Vector Flow External Force
by Qianqian Qian, Ke Cheng, Wei Qian, Qingchang Deng and Yuanquan Wang
Sensors 2022, 22(13), 4956; https://doi.org/10.3390/s22134956 - 30 Jun 2022
Cited by 5 | Viewed by 1936
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 [...] Read more.
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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Review

Jump to: Research

49 pages, 6630 KiB  
Review
A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions
by Mohammed Okmi, Lip Yee Por, Tan Fong Ang, Ward Al-Hussein and Chin Soon Ku
Sensors 2023, 23(9), 4350; https://doi.org/10.3390/s23094350 - 28 Apr 2023
Cited by 4 | Viewed by 6533
Abstract
Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors [...] Read more.
Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial–temporal patterns of crime, and ambient population measures have a significant impact on crime rates. Full article
(This article belongs to the Special Issue Smart Mobile and Sensing Applications)
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