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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 May 2020) | Viewed by 14352

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Dr. Bo Wei

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Guest Editor

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: Mobile computing; internet of things; wireless sensor networks; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Wai Lok Woo

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Co-Guest Editor

Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, UK
Interests: machine learning; artificial intelligence; computational intelligence; data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ubiquitous Internet of Things (IoT) has enabled many applications, such as smart homes/cities, environmental monitoring, and industrial automation. Compared with the use of the traditional IoT for data collection, the core of intelligent IoT is its application of signal modalities such as video, images, audio, wireless radio, and motion measurements for accurate and efficient perception. The use of intelligent IoT bridges the boundary between machine learning/artificial intelligence (AI) algorithms and resource-constrained embedded IoT devices. Computational power availability and energy consumption are two important factors in intelligent IoT. In order to facilitate efficient perception, intelligent IoT introduces the optimization of machine learning and AI algorithms. Recently, tailored machine learning/AI algorithms have been extensively explored to enable accurate, efficient, and real-time recognition in intelligent IoT devices. The derived algorithms are expected to achieve optimal performance between accuracy and efficiency. Energy-saving communication has also been investigated in IoT device connectivity to facilitate data analytics on cloud/edge servers because the conventional wireless communication among IoT devices consumes a great deal of energy.

The focus of this Special Issue will be on a broad range of topics including the internet of things, machine learning, and data fusion, involving the introduction and development of new advanced theoretical algorithms and experimental application. Potential topics include, but are not limited to:

Internet of things: machine learning and artificial-intelligence-driven applications;
Chatbots technology;
Natural language processing;
Autonomous vehicle technology;
Wearable sensors and IoT for monitoring and computing;
Pervasive mobile computing and wireless sensor networks: communication and applications;
Human–computer interaction for context awareness;
Edge computing for efficient perception;
Cyber-physical-social systems and constructs;
Other emerging applications of intelligent internet of things.

Dr. Bo Wei
Prof. Dr. Wai Lok Woo
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. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

internet of X-things
machine learning
computational intelligence
mobile computing
social signal processing
wireless sensor networks
human–computer interaction
signal, image, and video processing

Published Papers (4 papers)

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Research

24 pages, 1483 KiB

Open AccessArticle

Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

by Rebeen Ali Hamad, Longzhi Yang, Wai Lok Woo and Bo Wei

Appl. Sci. 2020, 10(15), 5293; https://doi.org/10.3390/app10155293 - 30 Jul 2020

Cited by 26 | Viewed by 3548

Abstract

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities. Full article

(This article belongs to the Special Issue Advances in Intelligent Internet of Things)

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17 pages, 1028 KiB

Open AccessArticle

A Comparison of Different Models of Glycemia Dynamics for Improved Type 1 Diabetes Mellitus Management with Advanced Intelligent Analysis in an Internet of Things Context

by Ignacio Rodríguez-Rodríguez, José-Víctor Rodríguez, José-María Molina-García-Pardo, Miguel-Ángel Zamora-Izquierdo and María-Teresa Martínez-Inglés Ignacio Martínez-Inglés

Appl. Sci. 2020, 10(12), 4381; https://doi.org/10.3390/app10124381 - 25 Jun 2020

Cited by 11 | Viewed by 2911

Abstract

The metabolic disease Type 1 Diabetes Mellitus (DM1) is caused by a reduction in the production of pancreatic insulin, which causes chronic hyperglycemia. Patients with DM1 are required to perform multiple blood glucose measurements on a daily basis to monitor their blood glucose dynamics through the use of capillary glucometers. In more recent times, technological developments have led to the development of cutting-edge biosensors and Continuous Glucose Monitoring (CGM) systems that can monitor patients’ blood glucose levels on a real-time basis. This offers medical providers access to glucose oscillations modeling interventions that can enhance DM1 treatment and management approaches through the use of novel disruptive technologies, such as Cloud Computing (CC), big data, Intelligent Data Analysis (IDA) and the Internet of Things (IoT). This work applies some advanced modeling techniques to a complete data set of glycemia-related biomedical features—obtained through an extensive, passive monitoring campaign undertaken with 25 DM1 patients under real-world conditions—in order to model glucose level dynamics through the proper identification of patterns. Hereby, four methods, which are run through CC due to the high volume of data collected, are applied and compared within an IoT context. The results show that Bayesian Regularized Neural Networks (BRNN) offer the best performance (0.83 R²) with a reduced Root Median Squared Error (RMSE) of 14.03 mg/dL. Full article

(This article belongs to the Special Issue Advances in Intelligent Internet of Things)

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13 pages, 2797 KiB

Open AccessFeature PaperArticle

RADIO: Parameterized Generative Radar Data Augmentation for Small Datasets

by Marcel Sheeny, Andrew Wallace and Sen Wang

Appl. Sci. 2020, 10(11), 3861; https://doi.org/10.3390/app10113861 - 2 Jun 2020

Cited by 12 | Viewed by 4667

Abstract

We present a novel, parameterised radar data augmentation (RADIO) technique to generate realistic radar samples from small datasets for the development of radar-related deep learning models. RADIO leverages the physical properties of radar signals, such as attenuation, azimuthal beam divergence and speckle noise, for data generation and augmentation. Exemplary applications on radar-based classification and detection demonstrate that RADIO can generate meaningful radar samples that effectively boost the accuracy of classification and generalisability of deep models trained with a small dataset. Full article

(This article belongs to the Special Issue Advances in Intelligent Internet of Things)

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21 pages, 1114 KiB

Open AccessArticle

Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation

by Uthman Baroudi, Mohammad Alshaboti, Anis Koubaa and Sahar Trigui

Appl. Sci. 2020, 10(9), 3264; https://doi.org/10.3390/app10093264 - 8 May 2020

Cited by 12 | Viewed by 2727

Abstract

In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios. Full article

(This article belongs to the Special Issue Advances in Intelligent Internet of Things)

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