arXiv subjects

Tareq Y. Al-Naffouri

Publications and source records attributed to Tareq Y. Al-Naffouri.

At least 145 records · Page 8Linked to original sources

arXiv2019

A State-of-the-Art Survey on Multidimensional Scaling Based Localization Techniques

Current and future wireless applications strongly rely on precise real-time localization. A number of applications such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/ positioning system is the Global Positioning System (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs) and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity based localization. Most of the localization techniques require ranging measurements such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA) and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS based localization techniques in WSNs, Internet of Things (IoT), cognitive radio networks, and 5G networks.

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arXiv2019

Sensor Placement and Resource Allocation for Energy Harvesting IoT Networks

The paper studies optimal sensor selection for source estimation in energy harvesting Internet of Things (IoT) networks. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to a prescribed spectral and energy budget. To do so, measurement accuracy, communication link quality, and the amount of energy harvested are all taken into account. The sensor selection is studied under both analog and digital transmission schemes from the selected sensors to the fusion center. In the digital transmission case, an information theoretic approach is used to model the transmission rate, observation quantization, and encoding. We numerically prove that with a sufficient system bandwidth, the digital system outperforms the analog system with a possibly different sensor selection. Two source models are studied in this paper: static source estimation for a vector of correlated sources and dynamic state estimation for a scalar source. The design problem of interest is a Boolean non convex optimization problem, which is solved by relaxing the Boolean constraints. We propose a randomized rounding algorithm which generalizes the existing algorithm. The proposed randomized rounding algorithm takes the joint sensor location, power and bandwidth selection into account to efficiently round the obtained relaxed solution.

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arXiv2019

Aeronautical Data Aggregation and Field Estimation in IoT Networks: Hovering & Traveling Time Dilemma of UAVs

The next era of information revolution will rely on aggregating big data from massive numbers of devices that are widely scattered in our environment. Most of these devices are expected to be of low-complexity, low-cost, and limited power supply, which impose stringent constraints on the network operation. In this regard, this paper investigates aerial data aggregation and field estimation from a finite spatial field via an unmanned aerial vehicle (UAV). Instead of fusing, relaying, and routing the data across the wireless nodes to fixed locations access points, a UAV flies over the field and collects the required data for two prominent missions; data aggregation and field estimation. To accomplish these tasks, the field of interest is divided into several subregions over which the UAV hovers to collect samples from the underlying nodes. To this end, we formulate and solve an optimization problem to minimize total hovering and traveling time of each mission. While the former requires the collection of a prescribed average number of samples from the field, the latter ensures for a given field spatial correlation model that the average mean-squared estimation error of the field value is no more than a predetermined threshold at any point. These goals are fulfilled by optimizing the number of subregions, the area of each subregion, the hovering locations, the hovering time at each location, and the trajectory traversed between hovering locations. The proposed formulation is shown to be np-hard mixed integer problem, and hence, a decoupled heuristic solution is proposed. The results show that there exists an optimal number of subregions that balance the tradeoff between hovering and traveling times such that the total time for collecting the required samples is minimized.

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arXiv2019

Towards the Internet of X-things: New Possibilities for Underwater, Underground, and Outer Space Exploration

The rapid growth of the world's population demands more natural resources, food, and space. World-renowned physicist Stephan Hawking has argued that soon we will require another world to live on because we are running out of space and natural resources. This ever-increasing demand for resources and space needs novel technologies to explore natural resources, produce more crops, and explore outer space. Internet of X-things (X-IoT) is an enabling technology to overcome all of the above issues. In this article, we have presented an overview of a unified framework of X-IoT. The framework of X-IoT consists of three major categories. The first one is the Internet of underwater things (IoUT) for smart oceans. The second category is the Internet of underground things (IoUGT) for smart agriculture, seismic monitoring, and Oil/Gas fields. The third category is the Internet of space things (IoST) for outer space exploration, to provide global coverage, and to enable inter-satellite communications. Through this framework, we get to know what has been done since recently and how the technical challenges across the broad spectrum of emerging use cases under the water, underground and over the space are converging toward future solutions.

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arXiv2019

Localization of Energy Harvesting Empowered Underwater Optical Wireless Sensor Networks

This paper proposes a received signal strength (RSS) based localization framework for energy harvesting underwater optical wireless sensor networks (EH-UOWSNs), where the optical noise sources and channel impairments of seawater pose significant challenges on range estimation. In UOWSNs energy limitation is another major problem due to the limited battery power and difficulty to replace or recharge the battery of an underwater sensor node. In the proposed framework, sensor nodes with insufficient battery harvest ambient energy and start communicating once they have sufficient storage of energy. Network localization is carried out by measuring the RSSs of active nodes, which are modeled based on the underwater optical communication channel characteristics. Thereafter, block kernel matrices are computed for RSS-based range measurements. Unlike the traditional shortest-path approach, the proposed technique reduces the estimation error of the shortest path for each block kernel matrix. Once the complete block kernel matrices are available, a closed form localization technique is developed to find the location of every optical sensor node in the network. An analytical expression for the Cramer Rao lower bound (CRLB) is also derived as a benchmark to evaluate the localization performance of the developed technique. Extensive simulations show that the proposed framework outperforms the well-known network localization techniques.

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arXiv2019

On Achievable Accuracy of Localization in Magnetic Induction-Based Internet of Underground Things for Oil and Gas Reservoirs

Magnetic Induction (MI) is an efficient wireless communication method to deploy operational internet of underground things (IOUT) for oil and gas reservoirs. The IOUT consists of underground things which are capable of sensing the underground environment and communicating with the surface. The MI-based IOUT enable many applications, such as monitoring of the oil rigs, optimized fracturing, and optimized extraction. Most of these applications are dependent on the location of the underground things and therefore require accurate localization techniques. The existing localization techniques for MI-based underground sensing networks are two-dimensional and do not characterize the achievable accuracy of the developed methods which are both crucial and challenging tasks. Therefore, this paper presents the expression of the Cramer Rao lower bound (CRLB) for three-dimensional MI-based IOUT localization which takes into account the channel parameters of the underground magnetic-induction. The derived CRLB provide the suggestions for an MI-based underground localization system by associating the system parameters with the error trend. Numerical results demonstrate that localization accuracy is affected by different channel and networks parameters such as the number of anchors, noise variance, frequency, and the number of underground things.

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arXiv2019

End-to-End Performance Analysis of Underwater Optical Wireless Relaying and Routing Techniques Under Location Uncertainty

On the contrary of low speed and high delay acoustic systems, underwater optical wireless communication (UOWC) can deliver a high speed and low latency service at the expense of short communication ranges. Therefore, multihop communication is of utmost importance to improve degree of connectivity and overall performance of underwater optical wireless networks (UOWNs). In this regard, this paper investigates relaying and routing techniques and provides their end-to-end (E2E) performance analysis under the location uncertainty. To achieve robust and reliable links, we first consider adaptive beamwidths and derive the divergence angles under the absence and presence of a pointing-acquisitioning-and-tracking (PAT) mechanism. Thereafter, important E2E performance metrics (e.g., data rate, bit error rate, transmission power, amplifier gain, etc.) are obtained for two potential relaying techniques; decode & forward (DF) and optical amplify & forward (AF). We develop centralized routing schemes for both relaying techniques to optimize E2E rate, bit error rate, and power consumption. Alternatively, a distributed routing protocol, namely Light Path Routing (LiPaR), is proposed by leveraging the range-beamwidth tradeoff of UOWCs. LiPaR is especially shown to be favorable when there is no PAT mechanism and available network information. In order to show the benefits of multihop communications, extensive simulations are conducted to compare different routing and relaying schemes under different network parameters and underwater environments.

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arXiv2018

On Distributed Routing in Underwater Optical Wireless Sensor Networks

Underwater optical wireless communication (UOWC) is becoming an attractive technology for underwater wireless sensor networks (UWSNs) since it offers high-speed communication links. Although UOWC overcomes the drawbacks of acoustic and radio frequency communication channels such as high latency and low data rate, yet, it has its own limitations. One of the major limitations of UOWC is its limited transmission range which demands to develop a multi-hop network with efficient routing protocols. Currently, the routing protocols for UOWSNs are centralized having high complexity and large end-to-end delay. In this article, first, we present the existing routing protocols for UOWSNs. Based on the existing protocols, we then propose distributed routing protocols to address the problems of high complexity and large end-to-end delay. Numerical results have been provided to show that the proposed routing protocol is superior to the existing protocols in terms of complexity and end-to-end delay. Finally, we have presented open research directions in UOWSNs.

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arXiv2018

Performance Analysis of Connectivity and Localization in Multi-Hop Underwater Optical Wireless Sensor Networks

Underwater optical wireless links have limited range and intermittent connectivity due to the hostile aquatic channel impairments and misalignment between the optical transceivers. Therefore, multi-hop communication can expand the communication range, enhance network connectivity, and provide a more precise network localization scheme. In this regard, this paper investigates the connectivity of underwater optical wireless sensor networks (UOWSNs) and its impacts on the network localization performance. Firstly, we model UOWSNs as randomly scaled sector graphs where the connection between sensors is established by point-to-point directed links. Thereafter, the probability of network connectivity is analytically derived as a function of network density, communication range, and optical transmitters' divergence angle. Secondly, the network localization problem is formulated as an unconstrained optimization problem and solved using the conjugate gradient technique. Numerical results show that different network parameters such as the number of nodes, divergence angle, and transmission range significantly influence the probability of a connected network. Furthermore, the performance of the proposed localization technique is compared to well-known network localization schemes and the results show that the localization accuracy of the proposed technique outperforms the literature in terms of network connectivity, ranging error, and number of anchors.

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arXiv2018

Outlier Detection and Optimal Anchor Placement for 3D Underwater Optical Wireless Sensor Networks Localization

Location is one of the basic information required for underwater optical wireless sensor networks (UOWSNs) for different purposes such as relating the sensing measurements with precise sensor positions, enabling efficient geographic routing techniques, and sustaining link connectivity between the nodes. Even though various two-dimensional UOWSNs localization methods have been proposed in the past, the directive nature of optical wireless communications and three-dimensional (3D) deployment of sensors require to develop 3D underwater localization methods. Additionally, the localization accuracy of the network strongly depends on the placement of the anchors. Therefore, we propose a robust 3D localization method for partially connected UOWSNs which can accommodate the outliers and optimize the placement of the anchors to improve the localization accuracy. The proposed method formulates the problem of missing pairwise distances and outliers as an optimization problem which is solved through half quadratic minimization. Furthermore, analysis is provided to optimally place the anchors in the network which improves the localization accuracy. The problem of optimal anchor placement is formulated as a combination of Fisher information matrices for the sensor nodes where the condition of D-optimality is satisfied. The numerical results indicate that the proposed method outperforms the literature substantially in the presence of outliers.

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arXiv2018

Precise Performance Analysis of the LASSO under Matrix Uncertainties

In this paper, we consider the problem of recovering an unknown sparse signal $\xv_0 \in \mathbb{R}^n$ from noisy linear measurements $\yv = \Hm \xv_0+ \zv \in \mathbb{R}^m$. A popular approach is to solve the $\ell_1$-norm regularized least squares problem which is known as the LASSO. In many practical situations, the measurement matrix $\Hm$ is not perfectely known and we only have a noisy version of it. We assume that the entries of the measurement matrix $\Hm$ and of the noise vector $\zv$ are iid Gaussian with zero mean and variances $1/n$ and $σ_{\zv}^2$. In this work, an imperfect measurement matrix is considered under which we precisely characterize the limiting behavior of the mean squared error and the probability of support recovery of the LASSO. The analysis is performed when the problem dimensions grow simultaneously to infinity at fixed rates. Numerical simulations validate the theoretical predictions derived in this paper.

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arXiv2018

Underwater Optical Wireless Communications, Networking, and Localization: A Survey

Underwater wireless communications can be carried out through acoustic, radio frequency (RF), and optical waves. Compared to its bandwidth limited acoustic and RF counterparts, underwater optical wireless communications (UOWCs) can support higher data rates at low latency levels. However, severe aquatic channel conditions (e.g., absorption, scattering, turbulence, etc.) pose great challenges for UOWCs and significantly reduce the attainable communication ranges, which necessitates efficient networking and localization solutions. Therefore, we provide a comprehensive survey on the challenges, advances, and prospects of underwater optical wireless networks (UOWNs) from a layer by layer perspective which includes: 1) Potential network architectures; 2) Physical layer issues including propagation characteristics, channel modeling, and modulation techniques 3) Data link layer problems covering link configurations, link budgets, performance metrics, and multiple access schemes; 4) Network layer topics containing relaying techniques and potential routing algorithms; 5) Transport layer subjects such as connectivity, reliability, flow and congestion control; 6) Application layer goals and state-of-the-art UOWN applications, and 7) Localization and its impacts on UOWN layers. Finally, we outline the open research challenges and point out the future directions for underwater optical wireless communications, networking, and localization research.

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arXiv2017

Hand Gesture Recognition Using Ultrasonic Waves

This paper presents a new method for detecting and classifying a predefined set of hand gestures using a single transmitter and a single receiver utilizing a linearly frequency modulated ultrasonic signal. Gestures are identified based on estimated range and received signal strength (RSS) of reflected signal from the hand. Support Vector Machine (SVM) was used for gesture detection and classification. The system was tested using experimental setup and achieved an average accuracy of 88%.

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arXiv2017

Compressive Sensing for Millimeter Wave Antenna Array Diagnosis

The radiation pattern of an antenna array depends on the excitation weights and the geometry of the array. Due to wind and atmospheric conditions, outdoor millimeter wave antenna elements are subject to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. Handheld devices are also subject to blockages from random finger placement and/or finger prints. These blockages cause absorption and scattering to the signal incident on the array, and change the array geometry. This distorts the far-field radiation pattern of the array leading to an increase in the sidelobe level and decrease in gain. This paper studies the effects of blockages on the far-field radiation pattern of linear arrays and proposes two array diagnosis techniques for millimeter wave antenna arrays. The proposed techniques jointly estimate the locations of the blocked antennas and the induced attenuation and phase shifts. Numerical results show that the proposed techniques provide satisfactory results in terms of fault detection with reduced number of measurements (diagnosis time) provided that the number of blockages is small compared to the array size.

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arXiv2017

SNR Estimation in Linear Systems with Gaussian Matrices

This paper proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results are provided, and show that the proposed method is very accurate.

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arXiv2017

Enhancing Secrecy with Multi-Antenna Transmission in Millimeter Wave Vehicular Communication Systems

Millimeter wave (mmWave) vehicular communication systems will provide an abundance of bandwidth for the exchange of raw sensor data and support driver-assisted and safety-related functionalities. Lack of secure communication links, however, may lead to abuses and attacks that jeopardize the efficiency of transportation systems and the physical safety of drivers. In this paper, we propose two physical layer (PHY) security techniques for vehicular mmWave communication systems. The first technique uses multiple antennas with a single RF chain to transmit information symbols to a target receiver and noise-like signals in non-receiver directions. The second technique uses multiple antennas with a few RF chains to transmit information symbols to a target receiver and opportunistically inject artificial noise in controlled directions, thereby reducing interference in vehicular environments. Theoretical and numerical results show that the proposed techniques provide higher secrecy rate when compared to traditional PHY security techniques that require digital or more complex antenna architectures.

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arXiv2017

Perturbation-Based Regularization for Signal Estimation in Linear Discrete Ill-posed Problems

Estimating the values of unknown parameters from corrupted measured data faces a lot of challenges in ill-posed problems. In such problems, many fundamental estimation methods fail to provide a meaningful stabilized solution. In this work, we propose a new regularization approach and a new regularization parameter selection approach for linear least-squares discrete ill-posed problems. The proposed approach is based on enhancing the singular-value structure of the ill-posed model matrix to acquire a better solution. Unlike many other regularization algorithms that seek to minimize the estimated data error, the proposed approach is developed to minimize the mean-squared error of the estimator which is the objective in many typical estimation scenarios. The performance of the proposed approach is demonstrated by applying it to a large set of real-world discrete ill-posed problems. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods in most cases. In addition, the approach also enjoys the lowest runtime and offers the highest level of robustness amongst all the tested benchmark regularization methods.

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arXiv2017

Numerically Stable Evaluation of Moments of Random Gram Matrices with Applications

This paper is focuses on the computation of the positive moments of one-side correlated random Gram matrices. Closed-form expressions for the moments can be obtained easily, but numerical evaluation thereof is prone to numerical stability, especially in high-dimensional settings. This letter provides a numerically stable method that efficiently computes the positive moments in closed-form. The developed expressions are more accurate and can lead to higher accuracy levels when fed to moment based-approaches. As an application, we show how the obtained moments can be used to approximate the marginal distribution of the eigenvalues of random Gram matrices.

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