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Special Issue "Cognitive Computing with Big Data System over Secure Internet of Things "

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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 (28 June 2020) | Viewed by 31188

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Special Issue Editors

Dr. Xiaochun Cheng

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Guest Editor

Department of Computer Science, Middlesex University, London NW4 4BT, UK
Interests: computing; security; IOT; WSN; data science; decision science; education technology
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ding-Zhu Du

E-Mail Website
Guest Editor

Department of Computer Science, University of Texa at Dallas, 800 W. Campbell Road, MS EC31, Richardson, TX 75080, USA
Interests: greedy Approximation with nonsubmodular potential function; nonlinear combinatorial optimization; linear programming and approximation algorithms; Internet of Things; wireless sensor networks

Prof. Dr. Arun Kumar Sangaiah

E-Mail Website
Guest Editor

International Graduate Institute of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan
Interests: Internet of Things; computational intelligence; sustainable computing
Special Issues, Collections and Topics in MDPI journals

Dr. Rongxing Lu

E-Mail Website
Guest Editor

Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3A 0A1, Canada
Interests: wireless network security; cloud and fog computing security; big data security and privacy; secure and privacy in the IoT; secure opportunistic computing; secure social network; applied cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cognitive computing has broad horizons, which cover different characteristics of cognitive activities. The field is highly transdisciplinary in nature, combining principles, methods, and/or technologies from multiple subjects’ areas, such as: Psychology, computer science, artificial intelligence, computer network communication, linguistics, philosophy, neuroscience, etc.

The Internet of Things (IoT) has become a key component for intelligent systems, such as medical systems, intelligent vehicular networks, intelligent building, or smart cities. Low-cost sensing and actuation are available in IoT applications. They enable seamless information exchange and networked interactions of physical and digital objects, such as in personalized human healthcare. This interconnectivity together with large-scale data processing, advanced machine learning, robotics, and new electronic techniques steadily brings innovation and business models of the digital space into the physical world. Secure IoT systems are expected to improve the intelligence of the systems, to improve the interaction between the human and the environment, to enhance reliability, resilience, and agile access control, to improve operational efficiency and energy efficiency, and to optimize resource utilization. Many of the IoT systems and technologies are relatively novel; there are still many untapped applications areas, and numerous challenges and issues that need to be researched further for.

This Special Issue aims for data analysis, knowledge extraction, and decision support solutions based on data technologies and cognitive methods over the secure Internet of Things. This would extend tradition data technologies by incorporating knowledge from domain experts as well as the latest artificial intelligence solutions, such as how to perform medical decision support with the healthcare knowledge and patient data collected by secure IoT systems. The main focus is on research on the latest cognitive computing embedded data technologies to process and to analyse the large amount of data collected through secure IoT systems, and to help human expert decision-making, such as in health care services. Cognitive computing supported data processing facilitates a platform for the scientific community to work for the latest solutions for challenges related to secure IoT application towards smart infrastructure, such as to meet the real-world requirement of healthcare service. We cordially invite investigators to contribute their original research articles, with an emphasis on real-life applications, as well as review articles that will stimulate further activities in this area and improve our understanding of the key scientific problems.

The scope includes (but is not limited to) the following:

Cognitive computing models and prediction analytics (such as for e-health);
Cognitive semantic collective intelligence (such as in medical applications);
Cognitive computing algorithms (such as for smart healthcare systems);
Cognitive design principles and best practices for IoT application development (such as for human health services);
Cognitive reasoning about IoT smart objects (such as for health care);
Cognitive models for big data systems, theory, and applications (such as in e-health);
Cognitive data models (such as for telemedicine);
Edge/fog/IoT for mobile/wireless/pervasive/proactive/personalized service (such as healthcare);
IoT sensors data management;
IoT data mining and analytics (such as for smart medical devices).

Dr. Xiaochun Cheng
Prof. Ding-Zhu Du
Prof. Arun Kumar Sangaiah
Prof. Rongxing Lu
Guest Editors

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Keywords

Cognitive Computing
Big Data
Security
Internet of Things

Published Papers (11 papers)

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Editorial

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2 pages, 162 KiB

Open AccessEditorial

Cognitive Computing with a Big Data System in a Secure Internet of Things

and

Appl. Sci. 2023, 13(12), 7037; https://doi.org/10.3390/app13127037 - 12 Jun 2023

Viewed by 629

Abstract

This editorial aims to summarize the contents of the ten papers included in the Special Issue entitled “Cognitive Computing with a Big Data System in a Secure Internet of Things” [...] Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

Research

Jump to: Editorial

24 pages, 1777 KiB

Open AccessArticle

An Efficient BGV-type Encryption Scheme for IoT Systems

Wei Yuan

and

Han Gao

Appl. Sci. 2020, 10(17), 5732; https://doi.org/10.3390/app10175732 - 19 Aug 2020

Cited by 4 | Viewed by 2285

Abstract

Internet of Thing (IoT) systems usually have less storage and computing power than desktop systems. This paper proposes an efficient BGV-type homomorphic encryption scheme in order fit for secure computing on IoT system. Our scheme reduces the storage space for switch keys and ciphertext evaluation time comparing with previous BGV-type cryptosystems. Specifically, the switch key in homomorphic computations can be a constant but no longer one for each level. Moreover, the product of two ciphertexts can be at the same sublayer as them and the multiplication operations can be repeated between two sublayers. As a result, the multiplication times will not be limited by L in an L-level circuit and, thus, the ciphertext evaluation time will decrease significantly. We implement the scheme with the C language. The performance test shows that the efficiency of the improved scheme is better than Helib in same configurations. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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13 pages, 610 KiB

Open AccessArticle

Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Wenfeng Hou

Qing Liu

and

Longbing Cao

Appl. Sci. 2020, 10(14), 4893; https://doi.org/10.3390/app10144893 - 16 Jul 2020

Cited by 3 | Viewed by 1803

Abstract

Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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28 pages, 1850 KiB

Open AccessArticle

Exploring Geometric Feature Hyper-Space in Data to Learn Representations of Abstract Concepts

Rahul Sharma

Bernardete Ribeiro

Alexandre Miguel Pinto

and

F. Amílcar Cardoso

Appl. Sci. 2020, 10(6), 1994; https://doi.org/10.3390/app10061994 - 14 Mar 2020

Cited by 4 | Viewed by 2420

Abstract

The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed and hybrid representations. The majority of these approaches focused on addressing concrete concepts notion, but the view of the abstract concept is rarely explored. Moreover, most computational approaches have a predefined structure or configurations. The proposed method, Regulated Activation Network (RAN), has an evolving topology and learns representations of abstract concepts by exploiting the geometrical view of concepts, without supervision. In the article, first, a Toy-data problem was used to demonstrate the RANs modeling. Secondly, we demonstrate the liberty of concept identifier choice in RANs modeling and deep hierarchy generation using the IRIS dataset. Thirdly, data from the IoT’s human activity recognition problem is used to show automatic identification of alike classes as abstract concepts. The evaluation of RAN with eight UCI benchmarks and the comparisons with five Machine Learning models establishes the RANs credibility as a classifier. The classification operation also proved the RANs hypothesis of abstract concept representation. The experiments demonstrate the RANs ability to simulate psychological processes (like concept creation and learning) and carry out effective classification irrespective of training data size. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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16 pages, 6052 KiB

Open AccessArticle

A New Face Recognition Method for Intelligent Security

and

Appl. Sci. 2020, 10(3), 852; https://doi.org/10.3390/app10030852 - 25 Jan 2020

Cited by 11 | Viewed by 3028

Abstract

With the advent of the era of artificial intelligence and big data, intelligent security robots not only improve the efficiency of the traditional intelligent security industry but also propose higher requirements for intelligent security. Aiming to solve the problem of long recognition time and high equipment cost of intelligent security robots, we propose a new face recognition method for intelligent security in this paper. We use the Goldstein branching method for phase unwrapping, which can improve the three-dimensional (3D) face reconstruction effect. Subsequently, by using the three-dimensional face recognition method based on face radial curve elastic matching, different weights are assigned to different curve recognition similarity for weighted fusion as the total similarity for recognition. Experiments show that the method has a higher face recognition rate and is robust to attitude, illumination, and noise. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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15 pages, 1023 KiB

Open AccessArticle

An Automated Refactoring Approach to Improve IoT Software Quality

and

Appl. Sci. 2020, 10(1), 413; https://doi.org/10.3390/app10010413 - 06 Jan 2020

Cited by 4 | Viewed by 2651

Abstract

Internet of Things (IoT) software should provide good support for IoT devices as IoT devices are growing in quantity and complexity. Communication between IoT devices is largely realized in a concurrent way. How to ensure the correctness of concurrent access becomes a big challenge to IoT software development. This paper proposes a general refactoring framework for fine-grained read–write locking and implements an automatic refactoring tool to help developers convert built-in monitors into fine-grained ReentrantReadWriteLocks. Several program analysis techniques, such as visitor pattern analysis, alias analysis, and side-effect analysis, are used to assist with refactoring. Our tool is tested by several real-world applications including HSQLDB, Cassandra, JGroups, Freedomotic, and MINA. A total of 1072 built-in monitors are refactored into ReentrantReadWriteLocks. The experiments revealed that our tool can help developers with refactoring for ReentrantReadWriteLocks and save their time and energy. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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16 pages, 1511 KiB

Open AccessArticle

An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter

and

Appl. Sci. 2019, 9(22), 4968; https://doi.org/10.3390/app9224968 - 18 Nov 2019

Cited by 36 | Viewed by 4046

Abstract

Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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13 pages, 3097 KiB

Open AccessArticle

A Novel Time Constraint-Based Approach for Knowledge Graph Conflict Resolution

and

Appl. Sci. 2019, 9(20), 4399; https://doi.org/10.3390/app9204399 - 17 Oct 2019

Cited by 4 | Viewed by 2355

Abstract

Knowledge graph conflict resolution is a method to solve the knowledge conflict problem in constructing knowledge graphs. The existing methods ignore the time attributes of facts and the dynamic changes of the relationships between entities in knowledge graphs, which is liable to cause high error rates in dynamic knowledge graph construction. In this article, we propose a knowledge graph conflict resolution method, knowledge graph evolution algorithm based on deep learning (Kgedl), which can resolve facts confliction with high precision by combing time attributes, semantic embedding representations, and graph structure features. Kgedl first trains the semantic embedding vector through the relationships between entities. Then, the path embedding vector is trained from the graph structures of knowledge graphs, and the time attributes of entities are combined with the semantic and path embedding vectors. Finally, Kgedl uses a recurrent neural network to make the inconsistent facts appear in the dynamic evolution of the knowledge graph consistent. A large number of experiments on real datasets show that Kgedl outperforms the state-of-the-art methods. Especially, Kgedl achieves 23% higher performance than the classical method numerical Probabilistic Soft Logic (nPSL).in the metric HITS@10. Also, extensive experiments verified that our proposal possess better robustness by adding noise data. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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14 pages, 892 KiB

Open AccessArticle

DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism

and

Appl. Sci. 2019, 9(20), 4205; https://doi.org/10.3390/app9204205 - 09 Oct 2019

Cited by 32 | Viewed by 3839

Abstract

Currently, many cyberattacks use the Domain Generation Algorithm (DGA) to generate random domain names, so as to maintain communication with the Communication and Control (C&C) server. Discovering DGA domain names in advance could help to detect attacks and response in time. However, in recent years, the General Data Protection Regulation (GDPR) has been promulgated and implemented, and the method of DGA classification based on the context information, such as the WHOIS (the information about the registered users or assignees of the domain name), is no longer applicable. At the same time, acquiring the DGA algorithm by reversing malware samples encounters the problem of no malware samples for various reasons, such as fileless malware. We propose a DGA domain name classification method based on Long Short-Term Memory (LSTM) with attention mechanism. This method is oriented to the character sequence of the domain name, and it uses the LSTM combined with attention mechanism to construct the DGA domain name classifier to achieve the rapid classification of domain names. The experimental results show that the method has a good classification result. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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17 pages, 6878 KiB

Open AccessArticle

Fast Face Tracking-by-Detection Algorithm for Secure Monitoring

and

Appl. Sci. 2019, 9(18), 3774; https://doi.org/10.3390/app9183774 - 09 Sep 2019

Cited by 7 | Viewed by 4749

Abstract

This work proposes a fast face tracking-by-detection (FFTD) algorithm that can perform tracking, face detection and discrimination tasks. On the basis of using the kernelized correlation filter (KCF) as the basic tracker, multitask cascade convolutional neural networks (CNNs) are used to detect the face, and a new tracking update strategy is designed. The update strategy uses the tracking result modified by detector to update the filter model. When the tracker drifts or fails, the discriminator module starts the detector to correct the tracking results, which ensures the out-of-view object can be tracked. Through extensive experiments, the proposed FFTD algorithm is shown to have good robustness and real-time performance for video monitoring scenes. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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20 pages, 6359 KiB

Open AccessArticle

Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG

and

Appl. Sci. 2019, 9(14), 2910; https://doi.org/10.3390/app9142910 - 20 Jul 2019

Cited by 4 | Viewed by 2111

Abstract

To combine homomorphic public key encryption with reversible data hiding, a reversible data hiding scheme in homomorphic encrypted image based on EC-EG is proposed. Firstly, the cover image is segmented. The square grid pixel group randomly selected by the image owner has one reference pixel and eight target pixels. The n least significant bits (LSBs) of the reference pixel and all bits of target pixel are self-embedded into other parts of the image by a method of predictive error expansion (PEE). To avoid overflowing when embedding data, the n LSBs of the reference pixel are reset to zero before encryption. Then, the pixel values of the image are encrypted after being encoded onto the points of the elliptic curve. The encrypted reference pixel replaces the encrypted target pixels surrounding it, thereby constructing the mirroring central ciphertext (MCC). In a set of MCC, the data hider embeds the encrypted additional data into the n LSBs of the target pixels by homomorphic addition in ciphertexts, while the reference pixel remains unchanged. The receiver can directly extract additional data by homomorphic subtraction in ciphertexts between the target pixels and the corresponding reference pixel; extract the additional data by subtraction in plaintexts with the directly decrypted image; and restore the cover image without loss. The experimental results show that the proposed scheme has higher security than the similar algorithms, and the average embedding rate of the scheme is 0.25 bpp under the premise of ensuring the quality of the directly decrypted image. Full article

(This article belongs to the Special Issue Cognitive Computing with Big Data System over Secure Internet of Things )

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