18 research outputs found

    Applications of ontology in the internet of things: A systematic analysis

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    Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

    Applications of ontology in the Internet of Things: a systematic analysis

    Get PDF
    Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions

    Agricultural Monitoring System using Images through a LPWAN Network

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    Internet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Gobernación de Boyacá, Colfuturo, Colciencias, Universidad Santo Tomás, Pontificia Universidad JaverianaInternet of things (IoT) has turned into an opportunity to connect millions of devices through communication networks in digital environments. Inside IoT and mainly in the technologies of communication networks, it is possible to find Low Power Wide Area Networks (LPWAN). Within these technologies, there are service platforms in unlicensed frequency bands such as the LoRa Wide Area Network (LoRaWAN). It has features such as low power consumption, long-distance operation between gateway and node, and low data transport capacity. LPWAN networks are not commonly used to transport high data rates as in the case of agricultural images. The main goal of this research is to present a methodology to transport images through LPWAN networks using LoRa modulation. The methodology presented in this thesis is composed of three stages mainly. The first one is image processing and classification process. This stage allows preparing the image in order to give the information to the classifier and separate the normal and abnormal images; i.e. to classify the images under the normal conditions of its representation in contrast with the images that can represent some sick or affectation with the consequent presence of a particular pathology. For this activity. it was used some techniques were used classifiers such as Support Vector Machine SVM, K-means clustering, neuronal networks, deep learning and convolutional neuronal networks. The last one offered the best results in classifying the samples of the images. The second stage consists in a compression technique and reconstruction algorithms. In this stage, a method is developed to process the image and entails the reduction of the high amount of information that an image has in its normal features with the goal to transport the lowest amount of information. For this purpose, a technique will be presented for the representation of the information of an image in a common base that improves the reduction process of the information. For this activity, the evaluated components were Wavelet, DCT-2D and Kronecker algorithms. The best results were obtained by Wavelet Transform. On the other hand, the compres- sion process entails a series of iterations in the vector information, therefore, each iteration is a possibility to reduce that vector until a value with a minimum PSNR (peak signal to noise ratio) that allows rebuilding the original vector. In the reconstruction process, Iterative Hard Thresholding (IHT), Ortogonal MAtching Pur- suit (OMP), Gradient Projection for Sparse Reconstruction (GPSR)and Step Iterative Shrinage/Thresholding (Twist) algorithms were evaluated. Twist showed the best performance in the results. Finally, in the third stage, LoRa modulation is implemented through the creation of LoRa symbols in Matlab with the compressed information. The symbols were delivered for transmission to Software Defined Radio (SDR). In the receptor, a SDR device receives the signal, which is converted into symbols that are in turn converted in an information vector. Then, the reconstruction process is carried out following the description in the last part of stage 2 - compression technique and reconstruction algorithms, which is described in more detailed in chapter 3, section 3.2. Finally, the image reconstructed is presented. The original image and the result image were compared in order to find the differences. This comparison used Peak Signal-to-Noise Ratio (PSNR) feature in order to get the fidelity of the reconstructed image with respect of the original image. In the receptor node, it is possible to observe the pathology of the leaf. The methodology is particularly applied for monitoring abnormal leaves samples in potato crops. This work allows finding a methodology to communicate images through LPWAN using the LoRa modulation technique. In this work, a framework was used to classify the images, then, to process them in order to reduce the amount of data, to establish communication between a transmitter and a receiver through a wireless communication system and finally, in the receptor, to obtain a picture that shows the particularity of the pathology in an agricultural crop.Doctor en IngenieríaDoctoradohttps://orcid.org/0000-0002-3554-1531https://scholar.google.com/citations?user=5_dx9REAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.d

    A deep learning model for network intrusion detection with imbalanced data

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    With an increase in the number and types of network attacks, traditional firewalls and data encryption methods can no longer meet the needs of current network security. As a result, intrusion detection systems have been proposed to deal with network threats. The current mainstream intrusion detection algorithms are aided with machine learning but have problems of low detection rates and the need for extensive feature engineering. To address the issue of low detection accuracy, this paper proposes a model for traffic anomaly detection named a deep learning model for network intrusion detection (DLNID), which combines an attention mechanism and the bidirectional long short-term memory (Bi-LSTM) network, first extracting sequence features of data traffic through a convolutional neural network (CNN) network, then reassigning the weights of each channel through the attention mechanism, and finally using Bi-LSTM to learn the network of sequence features. In intrusion detection public data sets, there are serious imbalance data generally. To address data imbalance issues, this paper employs the method of adaptive synthetic sampling (ADASYN) for sample expansion of minority class samples, to eventually form a relatively symmetric dataset, and uses a modified stacked autoencoder for data dimensionality reduction with the objective of enhancing information fusion. DLNID is an end-to-end model, so it does not need to undergo the process of manual feature extraction. After being tested on the public benchmark dataset on network intrusion detection NSL-KDD, experimental results show that the accuracy and F1 score of this model are better than those of other comparison methods, reaching 90.73% and 89.65%, respectively

    LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review

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    As LoRaWAN is one of the most popular long-range wireless protocols among low-power IoT applications, more and more focus is shifting towards security. In particular, physical layer topics become relevant to improve the security of LoRaWAN nodes, which are often limited in terms of computational power and communication resources. To this end, e.g., detection methods for wireless attacks improve the integrity and robustness of LoRaWAN access. Further, wireless physical layer techniques have potential to enhance key refreshment and device authentication. In this work, we aim to provide a comprehensive review of various vulnerabilities, countermeasures and security enhancing features concerning the LoRaWAN physical layer. Afterwards, we discuss the impact of the reviewed topics on LoRaWAN security and, subsequently, we identify research gaps as well as promising future research directions

    Water Is a Viable Data Storage Medium: A Security and Privacy Viewpoint

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    The security of IoT devices is a major concern that needs to be addressed for their wide adoption. Users are constantly seeking devices that are faster and capable of holding large amounts of data securely. It is purported that water has memory of its own and the ability to retain memory of the substances that are dissolved into it, even after being substantially and serially diluted. It was also observed in the lab setting that the microscopic pattern of water obtained from the same vessel by different people is unique but can easily distinguish those individuals if the same experiment is executed repeatedly. Furthermore, extensive research is already underway that explores the storage of data on water and liquids. This leads to the requirement of taking the security and privacy concerns related to the storage of data on water into consideration, especially when the real-time collection of data related to water through the IoT devices is of interest. Otherwise, the water memory aspect may lead to leakage of the data and, consequently, the data owners identity. Therefore, this article for the first time highlights the security and privacy implications related to water memory and discusses the possible countermeasures to effectively handle these potential threats. This article also presents a framework to securely store sensitive data on water. The proof-of-concept prototype is implemented and tested over a real-world dataset to analyze the feasibility of the proposed framework. The performance analysis yields that the proposed framework can be deployed once data storage on water is widely used

    Cyber Security and Critical Infrastructures 2nd Volume

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    The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

    Approach for GDPR Compliant Detection of COVID-19 Infection Chains

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    While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area
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