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Special Issue "Smart Sensing: Leveraging AI for Sensing"

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

Deadline for manuscript submissions: closed (10 July 2020).

Special Issue Editors

Prof. Yu Wang
Website SciProfiles
Guest Editor
Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: wireless networks; sensor networks; mobile crowd sensing; mobile computing
Special Issues and Collections in MDPI journals
Prof. Dr. Feng Hong
Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, China
Interests: smart sensing; ubiquitous computing
Dr. Hao Wang
Website
Guest Editor
Associate Professor, Norwegian University of Science and Technology, Gjøvik, Norway
Interests: Blockchain; industrial IoT; big data
Special Issues and Collections in MDPI journals
Prof. Yanmin Zhu
Website
Guest Editor
Department of Computer Science and Engineering, Shanghai Jiao Tong University, China
Interests: vehicular networks; wireless sensor networks; crowd sensing networks; data mining

Special Issue Information

Dear Colleagues,

In the Internet of Things (IoT) era, sensing technologies have become important for many intelligent applications (e.g., smart cities, smart grids, smart homes, and smart vehicles). However, because of heterogeneity in sensor data and their different processing requirements (e.g., multisource, real-time, voluminous, continuous, streaming, and spatial-temporal), traditional data processing and integration approaches begin to show their limitations.

Meanwhile, despite the prosperity in artificial intelligence (AI) research, the development of an intelligent system capable of dealing with the diverse sensor data and adapting to different contexts is still in the relatively early stages. Many machine learning or AI algorithms still exhibit some difficulties when applied to spatial-temporal sensor data. Furthermore, applications of AI in different smart sensing areas are still very unbalanced.

This Special Issue aims to report topics on smart sensing, that is, leveraging AI for sensing, from the fundamentals and applications of smart sensing. We are seeking both innovative works in unexplored and/or emerging topics in the broad area of the integration of sensing and AI. We invite submissions on a wide range of smart sensing research, including but not limited to:

– Smart sensing technology
– Signal processing of sensor data;
– Multimodal smart sensing;
– Continuous and ubiquitous sensing;
– Context based on smart sensing;
– Mobile crowd sensing;
– Dependability of smart sensing systems;
– Knowledge discovery from sensor data;
– Machine/deep learning on sensor data;
– Context discovery from sensor data;
– Data analysis for smart sensing;
– Data-driven applications;
– Sensor data applications based on the utilization of web and cloud;
– Mobile computing and applications;
– Smart human computer interaction applications;
– Urban sensing and applications;
– Novel services or applications based on smart sensing.\

Prof. Yu Wang
Prof. Dr. Feng Hong
Dr. Hao Wang
Prof. Yanmin Zhu
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 papers will be 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 2000 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

  • smart sensing 
  • continuous and ubiquitous sensing
  • machine learning on sensor data 
  • context discovery from sensor data
  • data-driven applications

Published Papers (8 papers)

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Research

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Open AccessArticle
Toward Flexible and Efficient Home Context Sensing: Capability Evaluation and Verification of Image-Based Cognitive APIs
Sensors 2020, 20(5), 1442; https://doi.org/10.3390/s20051442 - 06 Mar 2020
Cited by 1
Abstract
Cognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible and efficient context sensing [...] Read more.
Cognitive Application Program Interface (API) is an API of emerging artificial intelligence (AI)-based cloud services, which extracts various contextual information from non-numerical multimedia data including image and audio. Our interest is to apply image-based cognitive APIs to implement flexible and efficient context sensing services in a smart home. In the existing approach with machine learning by us, with the complexity of recognition object and the number of the defined contexts increases by users, it still requires directly manually labeling a moderate scale of data for training and continually try to calling multiple cognitive APIs for feature extraction. In this paper, we propose a novel method that uses a small scale of labeled data to evaluate the capability of cognitive APIs in advance, before training features of the APIs with machine learning, for the flexible and efficient home context sensing. In the proposed method, we exploit document similarity measures and the concepts (i.e., internal cohesion and external isolation) integrate into clustering results, to see how the capability of different cognitive APIs for recognizing each context. By selecting the cognitive APIs that relatively adapt to the defined contexts and data based on the evaluation results, we have achieved the flexible integration and efficient process of cognitive APIs for home context sensing. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction
Sensors 2020, 20(5), 1260; https://doi.org/10.3390/s20051260 - 26 Feb 2020
Abstract
This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features [...] Read more.
This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval
Sensors 2020, 20(1), 291; https://doi.org/10.3390/s20010291 - 04 Jan 2020
Cited by 3
Abstract
A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most [...] Read more.
A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
Quantum Multi-User Broadcast Protocol for the “Platform as a Service” Model
Sensors 2019, 19(23), 5257; https://doi.org/10.3390/s19235257 - 29 Nov 2019
Cited by 7
Abstract
Quantum Cloud Computing is the technology which has the capability to shape the future of computing. In “Platform as a Service (PaaS)” type of cloud computing, the development environment is delivered as a service. In this paper, a multi-user broadcast protocol in network [...] Read more.
Quantum Cloud Computing is the technology which has the capability to shape the future of computing. In “Platform as a Service (PaaS)” type of cloud computing, the development environment is delivered as a service. In this paper, a multi-user broadcast protocol in network is developed with the mode of one master and N slaves together with a sequence of single photons. It can be applied to a multi-node network, in which a single photon sequence can be sent to all the slave nodes simultaneously. In broadcast communication networks, these single photons encode classical information directly through noisy quantum communication channels. The results show that this protocol can realize the secret key generation and sharing of multiple nodes. The protocol we propose is also proved to be unconditionally secure in theory, which indicates its feasibility in theoretical application. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis
Sensors 2019, 19(20), 4365; https://doi.org/10.3390/s19204365 - 09 Oct 2019
Cited by 1
Abstract
With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels [...] Read more.
With the rapid development of marine IoT (Internet of Things), ocean MDTN (Mobile Delay Tolerant Network) has become a research hot spot. Long-term trajectory prediction is a key issue in MDTN. There are no long-term fine-grained trajectory prediction methods proposed for ocean vessels because a vessel’s mobility pattern lacks map topology support and can be easily influenced by the fish moratorium, sunshine duration, etc. A traditional on-land trajectory prediction algorithm cannot be directly utilized in this field because trajectory characteristics of ocean vessels are far different from that on land. To address the problem above, we propose a novel long-term trajectory prediction algorithm for ocean vessels, called L-VTP, by utilizing multiple sailing related parameters and K-order multivariate Markov Chain. L-VTP utilizes multiple sailing related parameters to build multiple state-transition matrices for trajectory prediction based on quantitative uncertainty analysis of trajectories. Trajectories’ sparsity of ocean vessels results in a critical state missing problem of a high-order state-transition matrix. L-VTP automatically traverses other matrices in a specific sequence in terms of quantitative uncertainty results to overcome this problem. Furthermore, the different mobility models of the same vessel during the day and the night are also exploited to improve the prediction accuracy. Privacy issues have been taken into consideration in this paper. A quantitative model considering Markov order, training metadata and privacy leak degree is proposed to help the participant make the trade-off based on their customized requirements. We have performed extensive experiments on two years of real-world trajectory data that include more than two thousand vessels. The experiment results demonstrate that L-VTP can realize fine-grained long-term trajectory prediction with the consideration of privacy issues. The average error of 4.5-hour fine-grained prediction is less than 500 m. In addition, the proposed method can be extended to 10-hour prediction with an average error of 2.16 km, which is also far less than the communication range of ocean vessel communication devices. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
An Adaptive, Discrete Space Oriented Wolf Pack Optimization Algorithm for a Movable Wireless Sensor Network
Sensors 2019, 19(19), 4320; https://doi.org/10.3390/s19194320 - 06 Oct 2019
Cited by 2
Abstract
Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this [...] Read more.
Recently, many related algorithms have been proposed to find an efficient wireless sensor network with good sustainability, a stable connection, and a high covering rate. To further improve the coverage rate of movable wireless sensor networks under the condition of guaranteed connectivity, this paper proposes an adaptive, discrete space oriented wolf pack optimization algorithm for a movable wireless sensor network (DSO-WPOA). Firstly, a strategy of adaptive expansion based on a minimum overlapping full-coverage model is designed to achieve minimum overlap and no-gap coverage for the monitoring area. Moreover, the adaptive shrinking grid search wolf pack optimization algorithm (ASGS-CWOA) is improved to optimize the movable wireless sensor network, which is a discrete space oriented problem. This improvement includes the usage of a target–node probability matrix and the design of an adaptive step size method, both of which work together to enhance the convergence speed and global optimization ability of the algorithm. Theoretical research and experimental results indicate that compared with the coverage algorithm based on particle swarm optimization (PSO-WSN) and classical virtual force algorithm, the newly proposed algorithm possesses the best coverage rate, better stability, acceptable performance in terms of time, advantages in energy savings, and no gaps. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Open AccessArticle
A CSI-Based Indoor Fingerprinting Localization with Model Integration Approach
Sensors 2019, 19(13), 2998; https://doi.org/10.3390/s19132998 - 07 Jul 2019
Cited by 1
Abstract
Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact [...] Read more.
Convenient indoor positioning has become an urgent need due to the improvement it offers to quality of life, which inspires researchers to focus on device-free indoor location. In areas covered with Wi-Fi, people in different locations will to varying degrees have an impact on the transmission of channel state information (CSI) of Wi-Fi signals. Because space is divided into several small regions, the idea of classification is used to locate. Therefore, a novel localization algorithm is put forward in this paper based on Deep Neural Networks (DNN) and a multi-model integration strategy. The approach consists of three stages. First, the local outlier factor (LOF), the anomaly detection algorithm, is used to correct the abnormal data. Second, in the training phase, 3 DNN models are trained to classify the region fingerprints by taking advantage of the processed CSI data from 3 antennas. Third, in the testing phase, a model fusion method named group method of data handling (GMDH) is adopted to integrate 3 predicted results of multiple models and give the final position result. The test-bed experiment was conducted in an empty corridor, and final positioning accuracy reached at least 97%. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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Review

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Open AccessReview
Human Activity Sensing with Wireless Signals: A Survey
Sensors 2020, 20(4), 1210; https://doi.org/10.3390/s20041210 - 22 Feb 2020
Cited by 1
Abstract
Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future [...] Read more.
Wireless networks have been widely deployed with a high demand for wireless data traffic. The ubiquitous availability of wireless signals brings new opportunities for non-intrusive human activity sensing. To enhance a thorough understanding of existing wireless sensing techniques and provide insights for future directions, this survey conducts a review of the existing research on human activity sensing with wireless signals. We review and compare existing research of wireless human activity sensing from seven perspectives, including the types of wireless signals, theoretical models, signal preprocessing techniques, activity segmentation, feature extraction, classification, and application. With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. Based on the analysis of existing research, the survey points out seven challenges on wireless human activity sensing research: robustness, non-coexistence of sensing and communications, privacy, multiple user activity sensing, limited sensing range, complex deep learning, and lack of standard datasets. Finally, this survey presents four possible future research trends, including new theoretical models, the coexistence of sensing and communications, awareness of sensing on receivers, and constructing open datasets to enable new wireless sensing opportunities on human activities. Full article
(This article belongs to the Special Issue Smart Sensing: Leveraging AI for Sensing)
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