ACCURATE INDOOR POSITION ESTIMATION TECHNIQUE USING FINGERPRINTING AND LATERATION-BASED APPROACH IN BLUETOOTH TECHNOLOGY

The first part consists of experimental analysis of Bluetooth signal parameters in order to select the best suitable parameter for position estimation. This part also presents a comprehensive experimental analysis to observe the relationship between signal parameters and distance, so that the main source of distance estimation error can be identified. After selecting the best suitable parameter for position estimation, the next issue is to address the distance estimation error and identify its causes. This is handled in the second part of the thesis, which addresses the problem of communication holes. It presents an extended Gradient RSS predictor and filter, which is used to predict and filter RSS measurements in communication holes. The prediction and filtering process is based on the selected signal parameter based on our experimental observations. The refined output measurements are then given to the position estimation algorithm, which is handled in the third part of thesis. The third part of this thesis presents a new filter based hybrid position estimation technique, which integrates the features of fingerprinting and lateration approach. The novel approach used in the proposed hybrid approach is the use of Euclidian distance formula for distance estimation instead of propagation model. Simulation and experimental results validate the performance of proposed hybrid technique and improve the accuracy up to 53.64 % and 25.58 % compared to Lateration and fingerprinting approach, respectively. In summary, this thesis presents a complete framework for indoor position estimation using Bluetooth networks

Towards a low complexity scheme for medical images in scalable video coding

Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve high-quality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is early terminated. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio. © 2013 IEEE

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era

Impact of node deployment and routing for protection of critical infrastructures

Recently, linear wireless sensor networks (LWSNs) have been eliciting increasing attention because of their suitability for applications such as the protection of critical infrastructures. Most of these applications require LWSN to remain operational for a longer period. However, the non-replenishable limited battery power of sensor nodes does not allow them to meet these expectations. Therefore, a shorter network lifetime is one of the most prominent barriers in large-scale deployment of LWSN. Unlike most existing studies, in this paper, we analyze the impact of node placement and clustering on LWSN network lifetime. First, we categorize and classify existing node placement and clustering schemes for LWSN and introduce various topologies for disparate applications. Then, we highlight the peculiarities of LWSN applications and discuss their unique characteristics. Several application domains of LWSN are described. We present three node placement strategies (i.e., linear sequential, linear parallel, and grid) and various deployment methods such as random, uniform, decreasing distance, and triangular. Extensive simulation experiments are conducted to analyze the performance of the three state-of-the-art routing protocols in the context of node deployment strategies and methods. The experimental results demonstrate that the node deployment strategies and methods significantly affect LWSN lifetime. © 2013 IEEE

Applying deep neural networks for user intention identification

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods

Construction of nonlinear component of block cipher using coset graph

In recent times, the research community has shown interest in information security due to the increasing usage of internet-based mobile and web applications. This research presents a novel approach to constructing the nonlinear component or Substitution Box (S-box) of block ciphers by employing coset graphs over the Galois field. Cryptographic techniques are employed to enhance data security and address current security concerns and obstacles with ease. Nonlinear component is a keystone of cryptography that hides the association between plaintext and cipher-text. Cryptographic strength of nonlinear component is directly proportional to the data security provided by the cipher. This research aims to develop a novel approach for construction of dynamic S-boxes or nonlinear components by employing special linear group $PSL(2, \mathbb{Z})$ over the Galois Field $GF\left({2}^{10}\right)$. The vertices of coset diagram belong to $GF\left({2}^{10}\right)$ and can be expressed as powers of α, where α represents the root of an irreducible polynomial $p\left(x\right) = {x}^{10}+{x}^{3}+1$. We constructed several nonlinear components by using ${GF}^{*}\left({2}^{10}\right)$. Furthermore, we have introduced an exceptionally effective algorithm for optimizing nonlinearity, which significantly enhances the cryptographic properties of the nonlinear component. This algorithm leverages advanced techniques to systematically search for and select optimal S-box designs that exhibit improved resistance against various cryptographic attacks

Multilevel Sparse Kernel-Based Interpolation

Radial basis functions (RBFs) have been successfully applied for the last four decades for fitting scattered data in Rd, due to their simple implementation for any d. However, RBF interpolation faces the challenge of keeping a balance between convergence performance and numerical stability. Moreover, to ensure good convergence rates in high dimensions, one has to deal with the difficulty of exponential growth of the degrees of freedom with respect to the dimension d of the interpolation problem. This makes the application of RBFs limited to few thousands of data sites and/or low dimensions in practice. In this work, we propose a hierarchical multilevel scheme, termed sparse kernel-based interpolation (SKI) algorithm, for the solution of interpolation problem in high dimensions. The new scheme uses direction-wise multilevel decomposition of structured or mildly unstructured interpolation data sites in conjunction with the application of kernel-based interpolants with different scaling in each direction. The new SKI algorithm can be viewed as an extension of the idea of sparse grids/hyperbolic cross to kernel-based functions. To achieve accelerated convergence, we propose a multilevel version of the SKI algorithm. The SKI and multilevel SKI (MLSKI) algorithms admit good reproduction properties: they are numerically stable and efficient for the reconstruction of large data in Rd, for d = 2, 3, 4, with several thousand data. SKI is generally superior over classical RBF methods in terms of complexity, run time, and convergence at least for large data sets. The MLSKI algorithm accelerates the convergence of SKI and has also generally faster convergence than the classical multilevel RBF scheme

Multilevel sparse kernel-based interpolation

Radial basis functions (RBFs) have been successfully applied for the last four decades for fitting scattered data in Rd, due to their simple implementation for any d. However, RBF interpolation faces the challenge of keeping a balance between convergence performance and numerical stability. Moreover, to ensure good convergence rates in high dimensions, one has to deal with the difficulty of exponential growth of the degrees of freedom with respect to the dimension d of the interpolation problem. This makes the application of RBFs limited to few thousands of data sites and/or low dimensions in practice. In this work, we propose a hierarchical multilevel scheme, termed sparse kernel-based interpolation (SKI) algorithm, for the solution of interpolation problem in high dimensions. The new scheme uses direction-wise multilevel decomposition of structured or mildly unstructured interpolation data sites in conjunction with the application of kernel-based interpolants with different scaling in each direction. The new SKI algorithm can be viewed as an extension of the idea of sparse grids/hyperbolic cross to kernel-based functions. To achieve accelerated convergence, we propose a multilevel version of the SKI algorithm. The SKI and multilevel SKI (MLSKI) algorithms admit good reproduction properties: they are numerically stable and efficient for the reconstruction of large data in Rd, for d = 2, 3, 4, with several thousand data. SKI is generally superior over classical RBF methods in terms of complexity, run time, and convergence at least for large data sets. The MLSKI algorithm accelerates the convergence of SKI and has also generally faster convergence than the classical multilevel RBF scheme.EThOS - Electronic Theses Online ServiceGBUnited Kingdo