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Intelligent Perception, Application and Security Mechanism in the 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 May 2020) | Viewed by 58004

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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

Dr. Zheli Liu

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Guest Editor

College of Cyber Science, Nankai University, Tianjin 300350, China
Interests: data security; artificial intelligence security
Special Issues, Collections and Topics in MDPI journals

Dr. Bing Jia

E-Mail Website
Guest Editor

Inner Mongolia University, China
Interests: mobile computing; Internet of Things; intelligent applications

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence (AI) has attracted extensive attention from both academics and industry. There are many relevant important research results. The Internet of Things systems (IoT) have extended the information system to the physical world, greatly expanding the ability of human beings to perceive, to understand and to control the physical world, and profoundly affecting the industry production and lifestyle of human beings. The methods by which to apply AI technologies to the IoT systems and to enhance the intelligence of system perception, understanding, computing, application and security are important to the implementation of intelligent IoT applications.

With the rapid development of IoT technologies, the current IoT environments have many distinctive characteristics, such as: the universality of perception, the ubiquity of connection and information transmission, and the massive volume of communicated data. Considering these characteristics, in order to apply IoT designs in the intelligent, efficient, safe and stable manner, safe and low-cost network management technologies and management methods with learning ability, understanding capability, reasoning ability, and collaborating ability are essential. Hence, the combination of AI technologies with the IoT systems provides prominent advantages. The integration of AI technologies and IoT systems enables the ability to gain valuable insight from the massive volume of data generated. The IoT era is coming. AI will help to stimulate the great potential of the IoT systems.

This special issue aims to attract contributions with new developments of intelligent perception, application and security mechanisms in the Internet of Things, to enhance the intelligence of the IoT systems. The ultimate goal is to promote research and development of AI technologies for IoT systems by publishing high-quality research articles in this rapidly developing field.

Scopes include (but are not limited to) the following:

Theoretical understanding of AI in the IoT
Hidden data awareness
Passive data transmission
Intelligent data processing
Multi-sources heterogeneous data fusion
Security and credibility verification
Dynamic intelligent perception in complex scenarios
Intelligent network
Intelligent application
Intelligent Indoor and Outdoor Seamless Positioning
Data privacy in IOT
Intrusion Detection in IoT systems
Crowd-sensing and Crowdsourcing

Dr. Xiaochun Cheng
Dr. Zheli Liu
Dr. Bing Jia
Guest Editors

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Keywords

IoT
Security
Intelligent application

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Published Papers (13 papers)

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12 pages, 1466 KiB

Open AccessArticle

Blockchain-Based Data-Preserving AI Learning Environment Model for AI Cybersecurity Systems in IoT Service Environments

by Jinsu Kim and Namje Park

Appl. Sci. 2020, 10(14), 4718; https://doi.org/10.3390/app10144718 - 8 Jul 2020

Cited by 38 | Viewed by 6751

Abstract

Artificial intelligence (AI) has a limitation in that it is only in the passive cognition area, so its operating process is not transparent; therefore, the technology relies on learning data. Since raw data for AI learning are processed and inspected manually to assure high quality for sophisticated AI learning, human errors are inevitable, and damaged and incomplete data and differences from the original data may lead to unexpected outputs of AI learning for which processed data are used. In this context, this research examines cases where AI learning data were inaccurate, in terms of cybersecurity, and the need for learning data management before machine learning through analysis of cybersecurity attack techniques, and we propose the direction of establishing a data-preserving AI system, which is a blockchain-based learning data environment model to verify the integrity of learning data. The data-preserving AI learning environment model is expected to prevent cyberattacks and data deterioration that may occur when data are provided and utilized in an open network for the processing and collection of raw data. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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13 pages, 2947 KiB

Open AccessArticle

Analysis of Carbon Dioxide Concentration Prediction Model and Diffusion Tendency of Expiratory by Simultaneous Multipoint Sensing

by Motokazu Moritani, Norifumi Watanabe, Kensuke Miyamoto, Kota Itoda, Junya Imani, Hiroyuki Aoyama and Yoshiyasu Takefuji

Appl. Sci. 2020, 10(13), 4631; https://doi.org/10.3390/app10134631 - 4 Jul 2020

Viewed by 2022

Abstract

Recent indoor air quality studies show that even 1000 parts per million (ppm) concentration of Carbon Dioxide (CO₂) has an adverse effect on human intellectual activities. Therefore, it is required to keep the CO

_{2}

concentration below a certain value in [...] Read more.

_{2}

concentration below a certain value in a room. In this study, in order to analyze the diffusion tendency of carbon dioxide by breathing, we constructed a simultaneous multi-point sensing system equipped with a carbon dioxide concentration sensor to measure indoor environment. Furthermore, it was evaluated whether the prediction model can be effectively used by comparing the prediction value by the model and the actually measured value from the sensor. The experimental results showed that CO

_{2}

by exhaled breathing diffuses evenly throughout the room regardless of the sensor’s relative positions to the human test subjects. The existing model is sufficiently accurate in a room which has above at least a 0.67 cycle/h ventilation cycle. However, there is a large gap between the measured and the model’s predicted values in a room with a low ventilation cycle, and that suggests a measurement with a sensor still is necessary to precisely monitor the indoor air quality. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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19 pages, 5314 KiB

Open AccessArticle

Genetic Algorithm with Radial Basis Mapping Network for the Electricity Consumption Modeling

by Israel Elias, José de Jesús Rubio, Dany Ivan Martinez, Tomas Miguel Vargas, Victor Garcia, Dante Mujica-Vargas, Jesus Alberto Meda-Campaña, Jaime Pacheco, Guadalupe Juliana Gutierrez and Alejandro Zacarias

Appl. Sci. 2020, 10(12), 4239; https://doi.org/10.3390/app10124239 - 20 Jun 2020

Cited by 34 | Viewed by 3010

Abstract

The modified backpropagation algorithm based on the backpropagation with momentum is used for the parameters updating of a radial basis mapping (RBM) network, where it requires of the best hyper-parameters for more precise modeling. Seeking of the best hyper-parameters in a model it is not an easy task. In this article, a genetic algorithm is used to seek of the best hyper-parameters in the modified backpropagation for the parameters updating of a RBM network, and this RBM network is used for more precise electricity consumption modeling in a city. The suggested approach is called genetic algorithm with a RBM network. Additionally, since the genetic algorithm with a RBM network starts from the modified backpropagation, we compare both approaches for the electricity consumption modeling in a city. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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15 pages, 1354 KiB

Open AccessArticle

Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

by Desheng Lyu, Bei Wang and Weizhe Zhang

Appl. Sci. 2020, 10(9), 3126; https://doi.org/10.3390/app10093126 - 30 Apr 2020

Cited by 9 | Viewed by 2635

Abstract

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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14 pages, 2719 KiB

Open AccessArticle

Hessian with Mini-Batches for Electrical Demand Prediction

by Israel Elias, José de Jesús Rubio, David Ricardo Cruz, Genaro Ochoa, Juan Francisco Novoa, Dany Ivan Martinez, Samantha Muñiz, Ricardo Balcazar, Enrique Garcia and Cesar Felipe Juarez

Appl. Sci. 2020, 10(6), 2036; https://doi.org/10.3390/app10062036 - 17 Mar 2020

Cited by 27 | Viewed by 2761

Abstract

The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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16 pages, 18886 KiB

Open AccessArticle

Attention Neural Network for Water Image Classification under IoT Environment

by Yirui Wu, Xuyun Zhang, Yao Xiao and Jun Feng

Appl. Sci. 2020, 10(3), 909; https://doi.org/10.3390/app10030909 - 30 Jan 2020

Cited by 31 | Viewed by 4442

Abstract

With significant development of sensors and Internet of things (IoT), researchers nowadays can easily know what happens in water ecosystem by acquiring water images. Essentially, growing data category and size greatly contribute to solving water pollution problems. In this paper, we focus on classifying water images to sub-categories of clean and polluted water, thus promoting instant feedback of a water pollution monitoring system that utilizes IoT technology to capture water image. Due to low inter-class and high intra-class differences of captured water images, water image classification is challenging. Inspired by the ability to extract highly distinguish features of Convolutional Neural Network (CNN), we aim to construct an attention neural network for IoT captured water images classification that appropriately encodes channel-wise and multi-layer properties to accomplish feature representation enhancement. During construction, we firstly propose channel-wise attention gate structure and then utilize it to construct a hierarchical attention neural network in local and global sense. We carried out comparative experiments on an image dataset about water surface with several studies, which showed the effectiveness of the proposed attention neural network for water image classification. We applied the proposed neural network as a key part of a water image based pollution monitoring system, which helps users to monitor water pollution breaks in real-time and take instant actions to deal with pollution. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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20 pages, 1387 KiB

Open AccessArticle

Table Recognition for Sensitive Data Perception in an IoT Vision Environment

by Jin Zhang, Yanmiao Xie, Weilai Liu and Xiaoli Gong

Appl. Sci. 2019, 9(19), 4162; https://doi.org/10.3390/app9194162 - 4 Oct 2019

Cited by 4 | Viewed by 2743

Abstract

Internet of Things (IoT) technology allows us to measure, compute, and decide about the physical world around us in a quantitative and intelligent way. It makes all kinds of intelligent IoT devices popular. We are continually perceived and recorded by intelligent IoT devices, especially vision devices such as cameras and mobile phones. However, a series of security issues have arisen in recent years. Sensitive data leakage is the most typical and harmful one. Whether we are just browsing files unintentionally in sight of high-definition (HD) security cameras, or internal ghosts are using mobile phones to photograph secret files, it causes sensitive data to be captured by intelligent IoT vision devices, resulting in irreparable damage. Although the risk of sensitive data diffusion can be reduced by optical character recognition (OCR)-based packet filtering, it is difficult to use it with sensitive data presented in table form. This is because table images captured by the intelligent IoT vision device face issues of perspective transformation, and interferences of circular stamps and irregular handwritten signatures. Therefore, a table-recognition algorithm based on a directional connected chain is proposed in this paper to solve the problem of identifying sensitive table data captured by intelligent IoT vision devices. First, a Directional Connected Chain (DCC) search algorithm is proposed for line detection. Then, valid line mergence and invalid line removal is performed for the searched DCCs to detect the table frame, to filter the irregular interferences. Finally, an inverse perspective transformation algorithm is used to restore the table after perspective transformation. Experiments show that our proposed algorithm can achieve accuracy of at least 92%, and filter stamp interference completely. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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19 pages, 6607 KiB

Open AccessArticle

Performance Evaluation of Region-Based Convolutional Neural Networks Toward Improved Vehicle Taillight Detection

by Zhenzhou Wang, Wei Huo, Pingping Yu, Lin Qi, Shanshan Geng and Ning Cao

Appl. Sci. 2019, 9(18), 3753; https://doi.org/10.3390/app9183753 - 8 Sep 2019

Cited by 9 | Viewed by 3331

Abstract

Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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17 pages, 775 KiB

Open AccessArticle

A Lightweight Hash-Based Blockchain Architecture for Industrial IoT

by Byoungjin Seok, Jinseong Park and Jong Hyuk Park

Appl. Sci. 2019, 9(18), 3740; https://doi.org/10.3390/app9183740 - 7 Sep 2019

Cited by 89 | Viewed by 8064

Abstract

Blockchain is a technology that can ensure data integrity in a distributed network, and it is actively applied in various fields. Recently, blockchain is gaining attention due to combining with the Internet of Things (IoT) technology in the industrial field. Moreover, many researchers have proposed the Industrial IoT (IIoT) architecture with blockchain for data integrity and efficient management. The IIoT network consists of many heterogeneous devices (e.g., sensors, actuators, and programmable logic controllers (PLC)) with resources-constrained, and the availability of the network must be preferentially considered. Therefore, applying the existed blockchain technology is still challenging. There are some results about the technique of constructing blockchain lightly to solve this challenge. However, in these results, the analysis in perspective of cryptographic performance (area, throughput, and power consumption) has not been considered sufficiently, or only focused on the architecture of the blockchain network. The blockchain technology is based on cryptographic techniques, and the main part is a cryptographic hash function. Therefore, if we construct the blockchain-based IIoT architecture, we have to consider the performance of the hash function. Many lightweight hash functions have been proposed recently for the resource-constrained environment, and it can also be used to the blockchain. Therefore, in this paper, we analyze the considerations of lightweight blockchain for IIoT. Also, we conduct an analysis of lightweight hash for blockchain, and propose a new lightweight hash-based blockchain architecture that can change the hash algorithm used for mining adjust to network traffic. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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21 pages, 513 KiB

Open AccessArticle

PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing

by Xiaochen Yang, Ming Xu, Shaojing Fu and Yuchuan Luo

Appl. Sci. 2019, 9(18), 3695; https://doi.org/10.3390/app9183695 - 5 Sep 2019

Cited by 1 | Viewed by 2177

Abstract

Mobile sensing mines group information through sensing and aggregating users’ data. Among major mobile sensing applications, the distinct counting problem aiming to find the number of distinct elements in a data stream with repeated elements, is extremely important for avoiding waste of resources. Besides, the privacy protection of users is also a critical issue for aggregation security. However, it is a challenge to meet these two requirements simultaneously since normal privacy-preserving methods would have negative influence on the accuracy and efficiency of distinct counting. In this paper, we propose a Privacy-Preserving Distinct Counting scheme (PPDC) for mobile sensing. Through integrating the basic idea of homomorphic encryption into Flajolet-Martin (FM) sketch, PPDC allows an aggregator to conduct distinct counting over large-scale datasets without disrupting privacy of users. Moreover, PPDC supports various forms of sensing data, including camera images, location data, etc. PPDC expands each bit of the hashing values of users’ original data, FM sketch is thus enhanced for encryption to protect users’ privacy. We prove the security of PPDC under known-plaintext model. The theoretic and experimental results show that PPDC achieves high counting accuracy and practical efficiency with scalability over large-scale data sets. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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21 pages, 2390 KiB

Open AccessArticle

Semantic-Based Representation Binary Clone Detection for Cross-Architectures in the Internet of Things

by Zhenhao Luo, Baosheng Wang, Yong Tang and Wei Xie

Appl. Sci. 2019, 9(16), 3283; https://doi.org/10.3390/app9163283 - 10 Aug 2019

Cited by 20 | Viewed by 3584

Abstract

Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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16 pages, 1322 KiB

Open AccessArticle

Identity Management and Access Control Based on Blockchain under Edge Computing for the Industrial Internet of Things

by Yongjun Ren, Fujian Zhu, Jian Qi, Jin Wang and Arun Kumar Sangaiah

Appl. Sci. 2019, 9(10), 2058; https://doi.org/10.3390/app9102058 - 18 May 2019

Cited by 84 | Viewed by 6870

Abstract

Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features have also introduced new security problems. To solve the problem, in this paper, blockchain-based identity management combining access control mechanism is designed under edge computing. The self-certified cryptography is utilized to realize the registration and authentication of network entities. We bind the generated implicit certificate to its identity and construct the identity and certificate management mechanism based on blockchain. Secondly, an access control mechanism based on Bloom filter is designed and integrated with identity management. Moreover, for secure communication in resource-constrained edge devices, a lightweight secret key agreement protocol based on self-authenticated public key is constructed. These mechanisms work together to provide data security guarantees for IIoT such as authentication, auditability, and confidentiality. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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Review

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20 pages, 1824 KiB

Open AccessReview

VPNFilter Malware Analysis on Cyber Threat in Smart Home Network

by Jose Costa Sapalo Sicato, Pradip Kumar Sharma, Vincenzo Loia and Jong Hyuk Park

Appl. Sci. 2019, 9(13), 2763; https://doi.org/10.3390/app9132763 - 9 Jul 2019

Cited by 47 | Viewed by 7986

Abstract

Recently, the development of smart home technologies has played a crucial role in enhancing several real-life smart applications. They help improve the quality of life through systems designed to enhance convenience, comfort, entertainment, health of the householders, and security. Note, however, that malware attacks on smart home devices are increasing in frequency and volume. As people seek to improve and optimize comfort in their home and minimize their daily home responsibilities at the same time, this makes them attractive targets for a malware attack. Thus, attacks on smart home-based devices have emerged. The goals of this paper are to analyze the different aspects of cyber-physical threats on the smart home from a security perspective, discuss the types of attacks including advanced cyber-attacks and cyber-physical system attacks, and evaluate the impact on a smart home system in daily life. We have come up with a taxonomy focusing on cyber threat attacks that can also have potential impact on a smart home system and identify some key issues about VPNFilter malware that constitutes large-scale Internet of Things (IoT)-based botnet malware infection. We also discuss the defense mechanism against this threat and mention the most infected routers. The specific objective of this paper is to provide efficient task management and knowledge related to VPNFilter malware attack. Full article

(This article belongs to the Special Issue Intelligent Perception, Application and Security Mechanism in the Internet of Things)

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