42 research outputs found
An Efficient Chaotic Interleaver for Image Transmission over IEEE 802.15.4 Zigbee Network, Journal of Telecommunications and Information Technology, 2011, nr 2
This paper studies a vital issue in wireless communications, which is the transmission of images over wireless networks. IEEE ZigBee 802.15.4 is a short-range communication standard that could be used for small distance multimedia transmissions. In fact, the ZigBee network is a wireless personal area network (WPAN), which needs a strong interleaving mechanism for protection against error bursts. This paper presents a novel chaotic interleaving scheme for this purpose. This scheme depends on the chaotic Baker map. A comparison study between the proposed chaotic interleaving scheme and the traditional block and convolutional interleaving schemes for image transmission over a correlated fading channel is presented. The simulation results show the superiority of the proposed chaotic interleaving scheme over the traditional schemes
Resilient Peer-to-Peer Ranging using Narrowband High-Performance Software-Defined Radios for Mission-Critical Applications
There has been a growing need for resilient positioning for numerous
applications of the military and emergency services that routinely
conduct operations that require an uninterrupted positioning service.
However, the level of resilience required for these applications is difficult
to achieve using the popular navigation and positioning systems available
at the time of this writing. Most of these systems are dependent on
existing infrastructure to function or have certain vulnerabilities that can
be too easily exploited by hostile forces. Mobile ad-hoc networks can
bypass some of these prevalent issues making them an auspicious topic for
positioning and navigation research and development. Such networks
consist of portable devices that collaborate to form wireless
communication links with one another and collectively carry out vital
network functions independent of any fixed centralized infrastructure.
The purpose of the research presented in this thesis is to adapt the
protocols of an existing narrowband mobile ad-hoc communications
system provided by Terrafix to enable range measuring for positioning.
This is done by extracting transmission and reception timestamps of
signals exchanged between neighbouring radios in the network with the
highest precision possible. However, many aspects of the radios forming
this network are generally not conducive to precise ranging, so the
ranging protocols implemented need to either maneuver around these
shortcomings or compensate for loss of precision caused. In particular,
the narrow bandwidth of the signals that drastically reduces the
resolution of symbol timing. The objective is to determine what level of
accuracy and precision is possible using this radio network and whether
one can justify investment for further development. Early experiments
have provided a simple ranging demonstration in a benign environment,
using the existing synchronization protocols, by extracting time data.
The experiments have then advanced to the radioâs signal processing to
adjust the synchronization protocols for maximize symbol timing
precision and correct for clock drift.
By implementing innovative synchronization techniques to the radio
network, ranging data collected under benign conditions can exhibit a
standard deviation of less than 3m. The lowest standard deviation
achieved using only the existing methods of synchronization was over two
orders of magnitude greater. All this is achieved in spite of the very
narrow 10â20kHz bandwidth of the radio signals, which makes producing
range estimates with an error less than 10â100m much more challenging
compared to wider bandwidth systems. However, this figure is beholden
to the relative motion of neighbouring radios in the network and how
frequently range estimates need to be made. This thesis demonstrates
how such a precision may be obtained and how this figure is likely to hold
up when applied in conditions that are not ideal
Agricultural Monitoring System using Images through a LPWAN Network
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.GobernaciĂłn de BoyacĂĄ, Colfuturo, Colciencias, Universidad Santo TomĂĄs, 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 IngenierĂaDoctoradohttps://orcid.org/0000-0002-3554-1531https://scholar.google.com/citations?user=5_dx9REAAAAJ&hl=eshttps://scienti.minciencias.gov.co/cvlac/EnRecursoHumano/query.d
Modélisation formelle des systÚmes de détection d'intrusions
LâĂ©cosystĂšme de la cybersĂ©curitĂ© Ă©volue en permanence en termes du nombre, de la diversitĂ©, et de la complexitĂ© des attaques. De ce fait, les outils de dĂ©tection deviennent inefficaces face Ă certaines attaques. On distingue gĂ©nĂ©ralement trois types de systĂšmes de dĂ©tection dâintrusions : dĂ©tection par anomalies, dĂ©tection par signatures et dĂ©tection hybride. La dĂ©tection par anomalies est fondĂ©e sur la caractĂ©risation du comportement habituel du systĂšme, typiquement de maniĂšre statistique. Elle permet de dĂ©tecter des attaques connues ou inconnues, mais gĂ©nĂšre aussi un trĂšs grand nombre de faux positifs. La dĂ©tection par signatures permet de dĂ©tecter des attaques connues en dĂ©finissant des rĂšgles qui dĂ©crivent le comportement connu dâun attaquant. Cela demande une bonne connaissance du comportement de lâattaquant. La dĂ©tection hybride repose sur plusieurs mĂ©thodes de dĂ©tection incluant celles sus-citĂ©es. Elle prĂ©sente lâavantage dâĂȘtre plus prĂ©cise pendant la dĂ©tection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour lâexpression de rĂšgles de reconnaissance dâattaques. Le nombre dâattaques potentielles Ă©tant trĂšs grand, ces bases de rĂšgles deviennent rapidement difficiles Ă gĂ©rer et Ă maintenir. De plus, lâexpression de rĂšgles avec Ă©tat dit stateful est particuliĂšrement ardue pour reconnaĂźtre une sĂ©quence dâĂ©vĂ©nements. Dans cette thĂšse, nous proposons une approche stateful basĂ©e sur les diagrammes dâĂ©tat-transition algĂ©briques (ASTDs) afin dâidentifier des attaques complexes. Les ASTDs permettent de reprĂ©senter de façon graphique et modulaire une spĂ©cification, ce qui facilite la maintenance et la comprĂ©hension des rĂšgles. Nous Ă©tendons la notation ASTD avec de nouvelles fonctionnalitĂ©s pour reprĂ©senter des attaques complexes. Ensuite, nous spĂ©cifions plusieurs attaques avec la notation Ă©tendue et exĂ©cutons les spĂ©cifications obtenues sur des flots dâĂ©vĂ©nements Ă lâaide dâun interprĂ©teur pour identifier des attaques. Nous Ă©valuons aussi les performances de lâinterprĂ©teur avec des outils industriels tels que Snort et Zeek. Puis, nous rĂ©alisons un compilateur afin de gĂ©nĂ©rer du code exĂ©cutable Ă partir dâune spĂ©cification ASTD, capable dâidentifier de façon efficiente les sĂ©quences dâĂ©vĂ©nements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity,
and the complexity of cyber attacks. Generally, we have three types of Intrusion
Detection System (IDS) : anomaly-based detection, signature-based detection, and
hybrid detection. Anomaly detection is based on the usual behavior description of
the system, typically in a static manner. It enables detecting known or unknown attacks
but also generating a large number of false positives. Signature based detection
enables detecting known attacks by defining rules that describe known attackerâs behavior.
It needs a good knowledge of attacker behavior. Hybrid detection relies on
several detection methods including the previous ones. It has the advantage of being
more precise during detection. Tools like Snort and Zeek offer low level languages to
represent rules for detecting attacks. The number of potential attacks being large,
these rule bases become quickly hard to manage and maintain. Moreover, the representation
of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition
diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular
representation of a specification, that facilitates maintenance and understanding of
rules. We extend the ASTD notation with new features to represent complex attacks.
Next, we specify several attacks with the extended notation and run the resulting specifications
on event streams using an interpreter to identify attacks. We also evaluate
the performance of the interpreter with industrial tools such as Snort and Zeek. Then,
we build a compiler in order to generate executable code from an ASTD specification,
able to efficiently identify sequences of events
Field Programmable Gate Arrays (FPGAs) II
This Edited Volume Field Programmable Gate Arrays (FPGAs) II is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Computer and Information Science. The book comprises single chapters authored by various researchers and edited by an expert active in the Computer and Information Science research area. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on Computer and Information Science, and open new possible research paths for further novel developments
Multibiometric security in wireless communication systems
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and
WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition.
First is the enrolment phase by which the database of watermarked fingerprints with
memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel.
Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present oneâs fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user.
The following three steps then involve speaker recognition including the user
responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user.
In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint
image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and
sliding neighborhood) have been followed with further two steps for embedding, and
extracting the watermark into the enhanced fingerprint image utilising Discrete
Wavelet Transform (DWT).
In the speaker recognition stage, the limitations of this technique in wireless
communication have been addressed by sending voice feature (cepstral coefficients)
instead of raw sample. This scheme is to reap the advantages of reducing the
transmission time and dependency of the data on communication channel, together
with no loss of packet. Finally, the obtained results have verified the claims