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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: 31 December 2019.

Special Issue Editors

Prof. Yu Wang
E-Mail Website
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
E-Mail Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, China
Interests: smart sensing; ubiquitous computing
Dr. Hao Wang
E-Mail Website
Guest Editor
Department of Computer Science, Norwegian University of Science & Technology, Norway
Tel. +47 61 13 52 68
Interests: big data analytics; industrial internet of things; high performance computing; safety-critical systems; communication security
Prof. Yanmin Zhu
E-Mail 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 1800 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 (3 papers)

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Research

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
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
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
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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