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Machine Learning and Intelligent Optimization Data Aggregation in Internet of...
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Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things

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

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 14549

Special Issue Editors

Dr. Pei-Wei Tsai

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Swinburne University of Technology, Mail No. H39, P.O. Box 218, Hawthorn, VIC 3122, Australia
Interests: optimization and workflow management; machine learning; data analytics; city logistics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Timos Sellis

E-Mail Website
Guest Editor
Department of Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn VIC 3122, Australia
Interests: big data; data streams; personalisation; data integration; spatio-temporal database systems
Prof. Dr. Dimitrios Georgakopoulos

E-Mail Website
Guest Editor
Key IoT Lab, Swinburne University of Technology, John St, Hawthorn, VIC 3122, Australia
Interests: Internet of Things; process management; data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine Learning (ML) and Intelligent Optimization are two of the most advanced fields in data science benefiting from the modern computational facilities. Taking the advantage of the powerful computing system, ML has evolved into the deep learning model, which includes more layers in the whole model structure, and is available to be trained mission-ready in a feasible time. The intelligent optimization technology is now capable of operating with large population and multigroup structures for handling large-scale of data.

The Internet of Things (IoT) is the latest Internet evolution that incorporates billions of Internet-connected devices that range from cameras, sensors, RFIDs, smart phones, and wearables, to smart meters, vehicles, medication pills, signs and industrial machines. Such IoT things are often owned by different organizations and people who are deploying and using them for their own purposes. Federations of such IoT devices (referred to as IoT things) can also deliver timely and accurate information that is needed to solve internet-scale problems that have been too difficult to tackle before.

In recent years, research outcomes have shown evidence that data aggregation and data process operations can be smart by combining ML and/or intelligent optimization with IoT. To realize its enormous potential, IoT must provide IoT solutions for discovering needed IoT devices, collecting and integrating their data with efficient ML or optimization techniques, and distilling the high value information each application needs. Such IoT solutions must be capable of filtering, aggregating, correlating, and contextualizing IoT information in real-time, on the move, in the edge and the cloud, and securely and must be capable of introducing data-driven changes to the physical world.

The MDPI Sensors solicits paper submissions and aim to bring together researchers and application developers working on the intersection of ML, optimization, and IoT with next-generation sensor development, distributed, cloud, internet, mobile, ambient, semantic, real-time, secure and privacy-preserving computing. We also aim to explore the application of novel IoT computing results and describe and assess their impact. The Special Issue seeks to compile original contributions that have not been published previously or already submitted to other conferences or journals. Review articles in the related subjects are also welcome.

Dr. Pei-Wei Tsai
Prof. Dr. Timos Sellis
Prof. Dr. Dimitrios Georgeakopoulos

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Research topics of interest track include but not limited to:

  • Machine learning and its applications
  • Evolutionary intelligent optimization and its applications
  • Large-scale IoT device data aggregation
  • Real-time IoT data analysis on the cloud, at the edge, and on the move, including localization, personalization, optimization, and contextualisation of IoT data.
  • IoT Actuation via IoT devices, robots, process-based, and ML-based automation
  • Lower-power and longer-range IoT networking for IoT devices
  • Wearable IoT devices and systems

Published Papers (4 papers)

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Research

14 pages, 1592 KiB  
Article
Spatiotemporal Mobility Based Trajectory Privacy-Preserving Algorithm in Location-Based Services
by Zhiping Xu, Jing Zhang, Pei-wei Tsai, Liwei Lin and Chao Zhuo
Sensors 2021, 21(6), 2021; https://doi.org/10.3390/s21062021 - 12 Mar 2021
Cited by 9 | Viewed by 2204
Abstract
Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the [...] Read more.
Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user’s trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user’s trajectory privacy. However, the user’s attributes are rarely considered when constructing the k-anonymity set. It results in that the user’s trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user’s attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user’s attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided. Full article
(This article belongs to the Special Issue Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things)
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19 pages, 4490 KiB  
Article
Generalized Deep Neural Network Model for Cuffless Blood Pressure Estimation with Photoplethysmogram Signal Only
by Yan-Cheng Hsu, Yung-Hui Li, Ching-Chun Chang and Latifa Nabila Harfiya
Sensors 2020, 20(19), 5668; https://doi.org/10.3390/s20195668 - 04 Oct 2020
Cited by 50 | Viewed by 5220
Abstract
Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor [...] Read more.
Due to the growing public awareness of cardiovascular disease (CVD), blood pressure (BP) estimation models have been developed based on physiological parameters extracted from both electrocardiograms (ECGs) and photoplethysmograms (PPGs). Still, in order to enhance the usability as well as reduce the sensor cost, researchers endeavor to establish a generalized BP estimation model using only PPG signals. In this paper, we propose a deep neural network model capable of extracting 32 features exclusively from PPG signals for BP estimation. The effectiveness and accuracy of our proposed model was evaluated by the root mean square error (RMSE), mean absolute error (MAE), the Association for the Advancement of Medical Instrumentation (AAMI) standard and the British Hypertension Society (BHS) standard. Experimental results showed that the RMSEs in systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 4.643 mmHg and 3.307 mmHg, respectively, across 9000 subjects, with 80.63% of absolute errors among estimated SBP records lower than 5 mmHg and 90.19% of absolute errors among estimated DBP records lower than 5 mmHg. We demonstrated that our proposed model has remarkably high accuracy on the largest BP database found in the literature, which shows its effectiveness compared to some prior works. Full article
(This article belongs to the Special Issue Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things)
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12 pages, 5032 KiB  
Article
3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm
by Jeng-Shyang Pan, Qing-Wei Chai, Shu-Chuan Chu and Ning Wu
Sensors 2020, 20(8), 2411; https://doi.org/10.3390/s20082411 - 23 Apr 2020
Cited by 30 | Viewed by 3287
Abstract
In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole [...] Read more.
In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance. Full article
(This article belongs to the Special Issue Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things)
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15 pages, 358 KiB  
Article
Optimizing Sensor Ontology Alignment through Compact co-Firefly Algorithm
by Xingsi Xue and Junfeng Chen
Sensors 2020, 20(7), 2056; https://doi.org/10.3390/s20072056 - 06 Apr 2020
Cited by 41 | Viewed by 2797
Abstract
Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor [...] Read more.
Semantic Sensor Web (SSW) links the semantic web technique with the sensor network, which utilizes sensor ontology to describe sensor information. Annotating sensor data with different sensor ontologies can be of help to implement different sensor systems’ inter-operability, which requires that the sensor ontologies themselves are inter-operable. Therefore, it is necessary to match the sensor ontologies by establishing the meaningful links between semantically related sensor information. Since the Swarm Intelligent Algorithm (SIA) represents a good methodology for addressing the ontology matching problem, we investigate a popular SIA, that is, the Firefly Algorithm (FA), to optimize the ontology alignment. To save the memory consumption and better trade off the algorithm’s exploitation and exploration, in this work, we propose a general-purpose ontology matching technique based on Compact co-Firefly Algorithm (CcFA), which combines the compact encoding mechanism with the co-Evolutionary mechanism. Our proposal utilizes the Gray code to encode the solutions, two compact operators to respectively implement the exploiting strategy and exploring strategy, and two Probability Vectors (PVs) to represent the swarms that respectively focuses on the exploitation and exploration. Through the communications between two swarms in each generation, CcFA is able to efficiently improve the searching efficiency when addressing the sensor ontology matching problem. The experiment utilizes the Conference track and three pairs of real sensor ontologies to test our proposal’s performance. The statistical results show that CcFA based ontology matching technique can effectively match the sensor ontologies and other general ontologies in the domain of organizing conferences. Full article
(This article belongs to the Special Issue Machine Learning and Intelligent Optimization Data Aggregation in Internet of Things)
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