Special Issue "Big Data Analytics for Secure and Smart Environmental Services"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Prof. Amir Hussain
E-Mail Website
Guest Editor
Edinburgh Napier University, Scotland, United Kingdom
Interests: big data, cyber-security, Internet of Things, learning, smart cities
Prof. Ahmed Al-Dubai
E-Mail Website
Guest Editor
School of Computing, Edinburgh Napier University, EH10 5DT, UK
Interests: big data, cyber-security, Internet of Things, learning, smart cities
Prof. Bill Buchanan
E-Mail Website
Guest Editor
School of Computing, Edinburgh Napier University, EH10 5DT, UK
Interests: big data, cyber-security, Internet of Things, learning, smart cities
Prof. Jonathan Wu
E-Mail Website
Guest Editor
Faulty of Engineering, University of Windsor, Windsor, ON N9B3P4, Canada
Interests: computer vision systems for active vehicle safety and driver assistance; machine learning and sensor fusion for autonomous driving; sensor technology; big data analytics for medicine; cross-border security; distributed sensing for industrial monitoring and automation
Prof. Kaizhu Huang
E-Mail Website
Guest Editor
Xi’an Jiaotong Liverpool University, Suzhou 215123, China
Interests: big data, machine learning, data mining, pattern recognition, intelligent systems
Prof. Bin Luo
E-Mail Website
Guest Editor
Anhui University, Anhui, Hefei 230039, China
Interests: big data, computer vision, pattern recognition, video analysis, surveillance systems
Prof. Jin Tang
E-Mail Website
Guest Editor
Anhui University, Anhui, Hefei 230039, China
Interests: big data, computer vision, pattern recognition, video analysis, surveillance systems
Dr. Wadii Boulila
E-Mail Website
Guest Editor
RIADI Laboratory, University of Manouba, Tunisia
Interests: big data, remote sensing, deep learning, uncertainty modeling
Prof. Adel M. Alimi
E-Mail Website
Guest Editor
REGIM-Laboratory, University of Sfax, Tunisia
Interests: big data, machine learning, computational intelligence

Special Issue Information

Dear Colleagues,

Today, the volume of remote sensing (RS) data has grown considerably, leading to a new research domain, termed big RS data. Analyzing, modeling, and interpreting RS big data through descriptive, predictive, and prescriptive analytics will aid users to make correct decisions in many fields related to RS. On the other hand, the evolution of Internet of Things (IoT) and cybersecurity technologies is continuing to play an important role in developing smart cities of tomorrow.

The growing combination of big data analytics, IoT, and cybersecurity offer the potential to learn and extract knowledge from heterogeneous RS big data collected from different sources, in order to develop real-world environmental services implemented through secure IoT infrastructures.

This timely Special Issue invites submissions on a range of related topics, including, but not limited to, the following:

  • Novel machine learning algorithms for big RS data (e.g., deep learning, active learning, reinforcement learning, evolutionary computing, etc.);
  • Methods and techniques for collection, cleaning, analyzing, distribution, and visualization of big RS data;
  • Unceratinty modeling in IoT and big RS data;
  • Cybersecurity methods and techniques to enable secure and privacy-assured IoT Big Data analytics for smart environmental services;
  • Infrastructure for IoT applied to big RS data (including cloud and edge computing, high performance computing, emerging hardware architectures, optimization, control, automation, security, etc.);
  • Environmental services, such as regional planning, climate forecasting, crowd management, resources management, land use/cover management, urban sprawl planning, water resource management,
Prof. Amir Hussain
Prof. Ahmed Al-Dubai
Prof. Bill Buchanan
Prof. Jonathan Wu
Prof. Kaizhu Huang
Prof. Bin Luo
Prof. Jin Tang
Dr. Wadii Boulila
Prof. Adel M. Alimi
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. Remote Sensing 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

  • Big remote sensing data
  • Smart environmental services (regional planning, climate forecasting, crowd management, resources management, land use/cover management, urban sprawl planning, water resource management, etc.)
  • Internet of Things
  • Novel machine learning algorithms (deep learning, active learning, reinforcement learning, evolutionary computing, constraint satisfaction problems, etc.)
  • Cybersecurity

Published Papers (3 papers)

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Research

Open AccessArticle
Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network
Remote Sens. 2019, 11(23), 2852; https://doi.org/10.3390/rs11232852 - 01 Dec 2019
Abstract
Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. [...] Read more.
Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles’ scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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Open AccessArticle
Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data
Remote Sens. 2019, 11(21), 2586; https://doi.org/10.3390/rs11212586 - 04 Nov 2019
Abstract
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels [...] Read more.
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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Open AccessArticle
Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors
Remote Sens. 2019, 11(21), 2531; https://doi.org/10.3390/rs11212531 - 29 Oct 2019
Abstract
Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone [...] Read more.
Today’s smartphones are equipped with embedded sensors, such as accelerometers and gyroscopes, which have enabled a variety of measurements and recognition tasks. In this paper, we jointly investigate two types of recognition problems in a joint manner, e.g., human activity recognition and smartphone on-body position recognition, in order to enable more robust context-aware applications. So far, these two problems have been studied separately without considering the interactions between each other. In this study, by first applying a novel data preprocessing technique, we propose a joint recognition framework based on the multi-task learning strategy, which can reduce computational demand, better exploit complementary information between the two recognition tasks, and lead to higher recognition performance. We also extend the joint recognition framework so that additional information, such as user identification with biometric motion analysis, can be offered. We evaluate our work systematically and comprehensively on two datasets with real-world settings. Our joint recognition model achieves the promising performance of 0.9174 in terms of F 1 -score for user identification on the benchmark RealWorld Human Activity Recognition (HAR) dataset. On the other hand, in comparison with the conventional approach, the proposed joint model is shown to be able to improve human activity recognition and position recognition by 5.1 % and 9.6 % respectively. Full article
(This article belongs to the Special Issue Big Data Analytics for Secure and Smart Environmental Services)
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