Dual Polarization Radio Localization for Vehicular Networks

WSA 2021; 25th International ITG Workshop on Smart Antennas,

Vehicular networks allow for a variety of applications ranging from platooning to fully automated driving. Most of such applications require the vehicles that constitute the networks to be aware of their relative or absolute position as well as the position of nearby vehicles. To this end, multiple positioning methods can be employed, among such methods are Global Positioning Systems or methods that employ time delay of arrival. This work presents a localization method that employs a dual polarized antenna at the transmitter and receiver side of wireless communications in vehicular networks. The proposed approach does not increase network load as it does not require extra data packets to be sent for localization purposes, and can be used to mitigate position spoofing inside the network. The accuracy and reliability of the proposed method are measured trough a set of numerical simulations, showing sufficient performance for acting as a secondary positioning mechanism capable of providing improved security and reliability to the network.

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Highway On-Ramp Merging for Mixed Traffic: Recent Advances and Future Trends

2021 IEEE 29th International Conference on Network Protocols (ICNP),

Due to the ability to support a wide range of applications and to involve infrastructure elements, connected and automated vehicles (CAVs) technology has played an important role in the development of cooperative intelligent transport systems. Thus, with the available sensing system, CAVs can perceive the surrounding environment. Indeed, due to the involvement of CAVs, communication of vehicles to other related devices using vehicle-to-everything (V2X) communication plays critical roles. This paper summarizes the research and development trends when proposing driving models, with a particular attention to highway on-ramp merging scenarios. The challenges and future research directions are also presented.

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Cooperative Localization for the Internet of Things

2021 16th Annual Conference on Wireless On-demand Network Systems and Services Conference (WONS),

The internet of things (IoT) currently has a large range of applications, from wearable to smart cities. Many of these applications require that the nodes inside the networks know their relative or absolute position. To this end, multiple positioning methods can be applied, among such methods are Global Positioning Systems (GPS) or methods that employ time delay of arrival (TDOA). This work presents node localization methods that employ a dual polarization receiver on a single node, or a virtual array when multiple nodes are capable of cooperating. The proposed approaches aim to minimize the economic cost associated with implementing localization methods, and can be done with simple hardware. The accuracy of the proposed methods is measured trough a set of numerical simulations.

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Multi-Band Antenna Array Geometry Impact on Array Interpolation

Proceedings of the 2021 International Technical Meeting of The Institute of Navigation,

Multi-band or multi-frequency antennas have become essential for many Global Navigation Satellite Systems (GNSS) applications. These antennas allow a receiver to simultaneously receive from multiple bands, which is essential for ionosphere corrections, can help mitigating multipath induced biases, and improve overall system availability. Another advancement that has recently attracted attention in the GNSS community is the usage of antenna arrays at the receiver. These arrays can be used to enhance system performance in multiple ways such as using beamforming to null out interferers or multipath components or enable a receiver to estimate its attitude while relying solely on received GNSS signals. While both multi-band antennas and antenna arrays offer attractive advantages for precise GNSS positioning, merging such systems on a single receiver can be challenging. Antenna arrays have their performance largely dictated by their geometries and the spacing between antenna elements. This spacing is defined with respect to the frequency of the signal that is received at the antenna array. If the spacing is too large the receiver will suffer from inaccuracy introduced by ambiguities that will be present when trying to filter out undesired signals or when trying to estimate the direction of arrival of received signals. If the spacing is too small, the total array directivity will be lower, which will lead to more biased direction of arrival estimations or to beamformers with lobes that are too broad to filter out undesired signals. The relationship between frequency and geometry makes it impossible to create a multi-band antenna array that is optimal for every frequency received, as optimizing one frequency will inevitably lead to performance degradation in the remaining ones. To tackle this issue, a technique known as array interpolation can be employed. Array interpolation consists of creating a mathematical transformation that projects the signal received at a real and imperfect array onto an ideal and abstract receiver. A different array interpolation can be constructed for each individual frequency received at the array. Thus, array interpolation can be a valuable tool for allowing multi-band antenna arrays to achieve high performance over the entire range of frequencies they are designed to receive. This work studies the effects of optimizing antenna array geometries for a given frequency band while applying array interpolation over the array response for the remaining frequency bands. The performance of multiple array interpolation methods is verified, and the tradeoffs between performance and computational complexity is studied.

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Spherical Wave Array Based Positioning for Vehicular Scenarios

IEEE Access,

Smart vehicles are emerging as a possible solution for multiple concerns in road traffic, such as mobility and safety. This work presents radio localization methods based on simultaneous direction of arrival (DOA), time-delay, and range estimation using the SAGE algorithm. The proposed methods do not rely on external sources of information, such as global navigation satellite systems (GNSS). The proposed methods take advantage of signals of opportunity and do not require the transmission of location-specific signals; therefore, they do not increase the network load. A set of simulations using synthetic and measured data is provided to validate the proposed methods, and the results show that it is possible to achieve accuracy down to decimeter and centimeter-level.

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Non-Line-of-Sight Based Radio Localization With Dual-Polarization Antenna Arrays

WSA 2020; 24th International ITG Workshop on Smart Antennas,

This work presents an approach for radio-based localization in non-line-of-sight (NLOS) environments by leveraging a dual-polarization antenna array. By estimating the polarization of the received signal, it is possible to estimate the angle of reflection of a NLOS signal. An estimate of the position of the transmitter concerning the receiver can be obtained based on a joint estimation of the reflection angle of several NLOS signals together with their respective directions of arrival (DOAs) and time differences of arrival (TDOAs). A set of numerical simulations is used to assess the performance of the proposed method.

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GNSS Aided Non-Line-of-Sight Radio Localization via Dual Polarized Arrays

CEUR Workshop Proceedings,

his work presents a radio based localization approach that is capable of accurately positioning radio emitters even when no direct line-of-sight signal is available. A dual polarized array is employed along with the space alternating generalized expectation maximization (SAGE) algorithm. To lighten the computational load and improve the accuracy of the proposed method, Global Navigation Satellite Systems (GNSS) positioning is used to initialize and limit the search area of SAGE. A set of numerical simulations is presented, highlighting the performance of the proposed method.

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M-estimator based Chinese Remainder Theorem with few remainders using a Kroenecker product based mapping vector

Digital Signal Processing,

The Chinese Remainder Theorem (CRT) explains how to estimate an integer-valued number from the knowledge of the remainders obtained by dividing such unknown integer by co-prime integers. As an algebraic theorem, CRT is the basis for several techniques concerning data processing. For instance, considering a single-tone signal whose frequency value is above the sampling rate, the respective peak in the DFT informs the impinging frequency value modulo the sampling rate. CRT is nevertheless sensitive to errors in the remainders, and many efforts have been developed in order to improve its robustness. In this paper, we propose a technique to estimate real-valued numbers by means of CRT, employing for this goal a Kroenecker based M-Estimation (ME), specially suitable for CRT systems with low number of remainders. Since ME schemes are in general computationally expensive, we propose a mapping vector obtained via Kroenecker products which considerably reduces the computational complexity. Furthermore, our proposed technique enhances the probability of estimating an unknown number accurately even when the errors in the remainders surpass 1/4 of the greatest common divisor of all moduli. We also provide a version of the mapping vectors based on tensorial n-mode products, delivering in the end the same information of the original method. Our approach outperforms the state-of-the-art CRT methods not only in terms of percentage of successful estimations but also in terms of smaller average error.

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Performance Assessment for Distributed Broadband Radio Localization

2018 52nd Asilomar Conference on Signals, Systems, and Computers,

Various emerging technologies, such as autonomous vehicles and fully autonomous flying, require precision positioning. This work presents a localization and tracking method based on joint direction of arrival (DOA), time delay, and range estimation using the SAGE algorithm. The proposed method does not rely on external sources of information such as global navigation satellite systems (GNSS). The method is opportunistic and does not require any location-based data exchange. A set of numerical simulations is presented to assess the performance of the proposed method.

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Array Processing Techniques For Direction Of Arrival Estimation, Communications, And Localization In Vehicular And Wireless Sensor Networks

PhD Thesis,

Array signal processing in wireless communication has been a topic of interest in research for over three decades. In the fourth generation (4G) of the wireless communication systems, also known as Long Term Evolution (LTE), multi antenna systems have been adopted according to the Release 9 of the 3rd Generation Partnership Project (3GPP). For the fifth generation (5G) of the wireless communication systems, hundreds of antennas should be incorporated to the devices in a massive multi-user Multiple Input Multiple Output (MIMO) architecture. The presence of multiple antennas provides array gain, diversity gain, spatial gain, and interference reduction. Furthermore, arrays enable spatial filtering and parameter estimation, which can be used to help solve problems that could not previously be addressed from a signal processing perspective. The aim of this thesis is to bridge some gaps between signal processing theory and real world applications. Array processing techniques tradition- ally assume an ideal array. Therefore, in order to exploit such techniques, a robust set of methods for array interpolation are fundamental and are developed in this work. Problems in the field of wireless sensor networks and vehicular networks are also addressed from an array signal processing perspective. In this dissertation, novel methods for array interpolation are presented and their performance in real world scenarios is evaluated. Signal processing concepts are implemented in the context of a wireless sensor network. These concepts provide a level of synchronization sufficient for distributed multi antenna communication to be applied, resulting in improved lifetime and improved overall network behavior. Array signal processing methods are proposed to solve the problem of radio based localization in vehicular network scenarios with applications in road safety and pedestrian protection.

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Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals

Signal Processing,

Two alternatives for interpolation sector discretization are presented.Sectors can be selected adaptively based on received signal power.Two methods for nonlinear interpolation are presented.Performance of the methods is accessed using real array measurements.Nonlinear interpolation outperforms linear approaches in demanding scenarios. Important signal processing techniques need that the response of the different elements of a sensor array have specific characteristics. For physical systems this often is not achievable as the array elements responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches become necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays.

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Antenna Array Based Localization Scheme for Vehicular Networks

2017 IEEE International Conference on Computer and Information Technology (CIT),

Vehicular ad hoc networks (VANETs) are emerging as the possible solution for multiple concerns in road traffic such as mobility and safety. One of the main concerns present in VANETs is the localization and tracking of vehicles. This work presents a passive vehicle localization and tracking method based on direction of arrival (DOA) estimation. The proposed method does not rely on external sources of information such as Global Navigation Satellite Systems (GNSS) and can be used to mitigate the possibility of spoofing or to provide a second independent source of position estimation for integrity purposes. The proposed algorithm uses array signal processing techniques to estimate not only the position but also the direction of other vehicles in network. Furthermore, it is a fully passive method and can alleviate the network load since it does not require any location based data exchange and can be performed by any listening vehicle using the signal of any data transmission. A set of numerical simulations is used to validate the proposed method and the results are shown to be more precise than the average accuracy of Global Position System (GPS) receivers.

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Direction of arrival estimation performance for compact antenna arrays with adjustable size

2017 IEEE MTT-S International Microwave Symposium (IMS),

The quest for compact antenna arrays able to perform robust beamforming and high resolution direction of arrival (DOA) estimation is pushing the antenna array dimensions to progressively shrink, with effects in terms of reduced performance not only for the antenna but also for beamforming and DOA estimation algorithms, for which their assumptions about the antenna properties do not hold anymore. This work shows the design and development of an antenna array with adjustable mutual distance between the single elements: such setup will allow to scientifically analyze the effects that progressive miniaturization, i.e. progressively smaller mutual distances between the antennas, have on the DOA estimation algorithms, as well as show the improvements obtained by using array interpolation methods, i.e. techniques able to create a virtual array response out of the actual array one, such as to comply with the algorithms' requirements on the antenna response.

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Adaptive communication and cooperative MIMO cluster formation for improved lifetime in wireless sensor networks

2016 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE),

One of the main limitations that still keeps Wireless Sensor Networks (WSNs) from being adopted in a large scale is the limited energy supply, i.e. the lifetime of the nodes that constitute the network. The wireless communication between nodes is responsible for most of the energy consumed in WSNs. A promising method to improve the energy efficiency is the usage of a Cooperative Multiple Input Multiple Output (CO-MIMO) scheme, where nodes form clusters to transmit and receive signals using a virtual antenna array. This work presents a study on the energy consumption of multi-hop and single-hop transmission compared to CO-MIMO and how to select the most efficient method. It also proposes a method for adaptively choosing the number of nodes that form a CO-MIMO cluster in order to maximize the lifetime of the network and to avoid disconnections. The proposed method takes into account not only the total energy consumption but also the distribution of energy within the network, aiming to keep the energy distribution across the network as uniform as possible. The effects of the proposed methods in the total available energy of the network and in the distribution of the energy is presented by means of numerical simulations.

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Array interpolation based on multivariate adaptive regression splines

2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM),

Many important signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC, rely on antenna arrays with specific and precise structures. Arrays with such ideal structures, such as a centro-hermitian structure, are often hard to build in practice. Array interpolation is used to enable the usage of these techniques with imperfect (not having a centro-hermitian structure) arrays. Most interpolation methods rely on methods based on least squares (LS) to map the output of a perfect virtual array based on the real array. In this work, the usage of Multivariate Adaptive Regression Splines (MARS) is proposed instead of the traditional LS to interpolate arrays with responses largely different from the ideal.

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Multi-hop Cooperative XIXO Transmission Scheme for Delay Tolerant Wireless Sensor Networks

WSA 2016; 20th International ITG Workshop on Smart Antennas,

Delay Tolerant Wireless Sensor Networks (DT-WSN) are sensor networks sparsely populated where connectivity be- tween sensor nodes is intermittent. The energy consumption is critical to the performance of these networks, since nodes have to carry data for a long period of time due to opportunistic transmissions. This work presents a multi-hop cooperative multiple/single input multiple/single output (C-XIXO) transmission scheme for DT-WSN in order to achieve longer communication ranges and consequently reduce the message delivery time to the sink, maximizing the energy efficiency. Simulations results suggest that the proposed scheme provides higher message propagation speed and reduced energy consumption compared to a state-of-the-art scheme for DT-WSN.

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Evaluation of Space-Time-Frequency (STF)-Coded MIMO-OFDM Systems in Realistic Channel Models

2014 28th International Conference on Advanced Information Networking and Applications Workshops,

By taking into account several dimension of the transmitted signal, such as space, frequency, period and time, MIMO-OFDM systems al- low an increased spectral efficiency and an improved identifiability in comparison to matrix solutions. In this paper, we evaluate MIMO-OFDM systems for geometric scenarios where the narrow band approximation is violated. To this end a new data model is proposed to better represent the behavior of the system in the presence of wide band signals. Moreover, we also relax the assumption that the amount of transmitted antennas is equal to the number of transmitted symbols.

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Unscented Transformation based array interpolation

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),

It is impossible to enforce exact responses for each sensor involved in an antenna array. Important signal processing techniques such as Estimation of Signal Parameters via Rotational Invariance (ESPRIT), Forward Backward Average (FBA) and Spatial Smoothing (SPS) rely on sensor arrays with Vandermonde or centro-hermitian responses. To achieve such responses array interpolation is often necessary. In this work a novel way of performing array interpolation while minimizing the transformation error using the Unscented Transformation (UT) is presented. The UT provides a different method for mapping interpolated regions and also exhibits a new insight into array interpolation and its current limitations. A set of numerical simulations presents promising results for array interpolation employing the UT.

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Multidimensional Array Interpolation Applied to Direction of Arrival Estimation

WSA 2015; 19th International ITG Workshop on Smart Antennas,

In MIMO communications systems, the data has a natural multidimensional structure composed of time, frequency and space dimensions. Recently, multidimensional techniques that take into account the data multidimensional structure have been proposed for model order selection, parameter estimation and prewhitening. These multidimensional techniques require an array with a PARAFAC structure. However, in practice, building antenna arrays with precise geometries is not feasible. In this paper, we propose a multidimensional array interpolation scheme that forces a real imperfect array to become a PARAFAC array. Once the multidimensional interpolation is successfully performed, advantages such as increased identifiability, separation without imposing additional constraints and improved accuracy can be exploited. Numerical simulations show that the proposed method provides improved DOA estimation accuracy when a PARAFAC technique is applied to an originally non-PARAFAC array.

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A Practical Implementation of a Cooperative Antenna Array for Wireless Sensor Networks

ruSMART 2015, NEW2AN 2015: Internet of Things, Smart Spaces, and Next Generation Networks and Systems,

Energy consumption is a key issue to be handled in Wireless Sensor Networks, especially considering low-end sensor nodes, i.e. sensor with severe energy resources limitations. When sensor nodes have their energy resources depleted, they stop working which can compromise the whole network functioning, thus its lifetime. As communication is the most energy-consumption task, enhancements in communication that diminish the amount of messages lost and the need for retransmissions are very important to preserve energy resources and extend the network lifetime. Considering the impact of the energy preservation and the opportunity to exploit it in terms of communication, this paper discusses the practical implementation of a cooperative MIMO scheme based on virtual antenna array using sensor nodes in order to enhance data communication in wireless sensor networks. The conducted experiments present evidence of the feasibility of the proposed approach highlighting performance aspects.

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Sensor Localization via Diversely Polarized Antennas

2014 IEEE International Conference on Distributed Computing in Sensor Systems,

Wireless sensor networks (WSNs) are currently employed in a vast number of different applications ranging from home automation and health care to military systems. Although their application may vary greatly, WSNs share a common set of characteristics such as a limited energy supply and simple hardware. A common issue related with the application of WSNs is sensor localization, for some types of applications it is important that the sensors know the relative or absolute position of other sensors in the network, such as surveillance of monitoring networks. If sensors are randomly placed they may resort a wide range of methods such as Global Navigation Satellite Systems (GNSS) or received signal strength indicators (RSSI). In this work we present an alternative to relative sensor localization by employed a crossed dipole antenna in the reception and a known polarization in the transmission. The accuracy of the proposed methods is measured trough numerical simulations and results are presented.

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A signal adaptive array interpolation approach with reduced transformation bias for DOA estimation of highly correlated signals

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),

Sensor arrays with Vandermonde or centro-hermitian responses cannot always be constructed. However, such array response struc- ture can be achieved by means of a mapping which transforms the real array response to an array response with the desired properties by applying array interpolation algorithms. In this work a low-complexity, multi-sector, signal adaptive array interpolation approach that achieves low transformation bias in the presence of highly correlated signals is presented. Estimation of Signal Parameters via Rotational Invariance (ESPRIT) algorithm with Forward Backward Average (FBA) and Spatial Smoothing (SPS) as well as model order estimation is applied after array interpolation in conjunction with the Vandermonde Invariance Transformation (VIT) to obtain precise high resolution estimates in closed form. A set of numerical simulations show that the proposed ap- proach provides precise estimates for arbitrary array responses in highly correlated signal signal environments.

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Reduced Rank TLS Array Interpolation for DOA Estimation

WSA 2014; 18th International ITG Workshop on Smart Antennas,

Important array signal processing techniques such as Spatial Smoothing, Forward Backward Averaging and Root-MUSIC require arrays with precise and specific geometries and responses. However, building sensor arrays with such demanding characteristics is not always possible. To deal with these possible limitations the real array response can be interpolated into the desired response applying array interpolation methods. In this work we study array interpolation methods for cases where the knowledge of the real array response is incomplete or contains errors. To address these imperfections a novel Total Least Squares (TLS) approach for calculating the transformation matrices is presented. Furthermore, a novel reduced rank regression approach is used to reduce the bias introduced by the transformation matrix onto the final direction of arrival (DOA) estimation.

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Improved Array Interpolation for Reduced Bias in DOA Estimation for GNSS

Proceedings of the 2014 International Technical Meeting of The Institute of Navigation,

Array signal processing has been the focus of particular interest in recent decades. Recently antenna arrays were incorporate to GNSS receivers seeking to improve overall precision and robustness against interference as well as spoofing. In multi-antenna GNSS receivers, direction of arrival (DOA) estimation of the impinging signals onto the antenna array is an important processing step in order to introduce knowledge of the DOAs of the satellites into beamforming algorithms and to achieve interference and spoofing detection. In this work, we present an alternative way of calculating the transform matrices by taking into account the estimates of information on the signal received and the spatial structure of the noise during the measurements. The extra information introduced into the calculation of the results significantly decreased bias when performing DOA estimation over the transformed dataset even when containing highly correlated signals like multipath or spoofing. After applying the transform FBA and SPS can “decorrelate” multipath and spoofing signals and thus multipath mitigation and spoofing detection can be enhanced significantly.

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Energy Harvesting Photovoltaic System to Charge a Cell Phone in Indoor Environments

2014 International Conference on Composite Materials & Renewable Energy Applications (ICCMREA),

Research advances in materials science improved gradually photovoltaic systems efficiency. However, such systems are limited to work in the presence of sun light, and they also depend on the geographic localization and on the period of the year, usually limited to 6 to 8 hours a day. In order to take maximum advantage of solar panels, it is crucial to use them also in cloudy weather or even at night. Therefore, in this paper, we propose to recycle light energy from artificial light sources to enable the use photovoltaic systems along 24 hours a day. We validate our proposal by means of measurements performed using artificial light in indoor environments. As a practical result, we show that 7 hours recharging in an indoor environments implies in 94.08 % of the overall cell phone battery capacity. Furthermore, we also propose a circuit for charging of a battery of a cell phone.

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Applying Cooperative MIMO Technique in an Adaptive Routing Mechanism for Wireless Sensor Networks

2013 IEEE Conference on Wireless Sensor (ICWISE),

Energy consumption in Wireless Sensor Networks (WSNs) is a limiting factor that hinders the application of such networks into solving a broader set of problems. Various ways of saving energy have been proposed, from energy efficient processing to power aware cluster organization. With communication between nodes being responsible for a large part of the energetic demand, energy efficient methods of communication have been proposed, with multi-hop communication being a wide used technique, capable of minimizing energy consumption and spreading it amongst the network. However, multi-hop is not always more efficient and is prone to a high delay, due to the decode and forward mechanism usually employed. In this paper a cooperative MIMO technique is studied, its energy consumption analyzed, and a mechanism for integrating it in existing WSNs and allowing its coexistence with multi-hop communication is suggested. Energy efficiency, packet delivery delay and packet loss ratios are analyzed and the results compared to standard WSNs.

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Array Interpolation Methods with applications in Wireless Sensor Networks and Global Positioning Systems

Master Thesis,

In the last three decades the study of antenna array signal processing techniques has received significant attention. A large number of techniques have been developed with different purposes such as the estimation of the direction of arrival (DOA), filtering or spatial separation of received signals, estimation of time delay of arrival (TDOA), Doppler frequency estimation and precoding of transmitted signals to maximize the power received by a different array. DOA estimation techniques are of particular interest for positioning systems based on radio waves such as the global positioning system (GPS) and for sensor mapping in wireless sensor networks (WSNs). These applications have the particular requirement of demanding the estimations to be made in real time or with reduced computational complexity. DOA estimation techniques that fulfill these requirements demand very specific antenna array structures that cannot, in general, be obtained in real implementations. In this work a set of techniques is presented that allows the interpolation of signals received in arrays of arbitrary geometry into arrays of specific geometry efficiently and robustly to allow the application of efficient DOA estimation techniques in arrays of arbitrary geometry. As an application of the proposed techniques precise mapping for WSNs and precise positioning for GPS receivers is presented.

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Synchronization for Cooperative MIMO in Wireless Sensor Networks

ruSMART 2013, NEW2AN 2013: Internet of Things, Smart Spaces, and Next Generation Networking,

The application of Wireless Sensor Networks (WSNs) is hindered by the limited energy budget available for the member nodes. Energy aware solutions have been proposed for all tasks involved in WSNs, such as processing, routing, cluster formation and communication. With communication being responsible for a large part of the energetic demand of WSNs energy efficient communication is paramount. The application of MIMO (Multiple-Input Multiple-Output) techniques in WSNs emerges as a efficient alternative for long range communications, how- ever, MIMO communication require precise synchronization in order to achieve good performance. In this paper the problem of transmission synchronization for WSNs employing Cooperative MIMO is studied, the main problems and limitations are highlighted and a synchronization method is proposed.

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Using MIMO Techniques to Enhance Communication Among Static and Mobile Nodes in Wireless Sensor Networks

2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA),

Wireless sensor networks are evolving to hybridnetworks in which static and mobile sensor nodes cooperate in order to address challenging requirements imposed by new emerging applications. However, due to the ad hoc nature of the network and especially to resources constraints of the sensor nodes, this cooperation is not trivial, requiring a number of retransmissions thus wasting precious resources. In this paper the use of cooperative multiple input multiple output (MIMO) techniques is proposed to overcome transmission problems, ensuring a reliable and more efficient communication link with less retransmissions. Extensive simulation experiments support the proposal, and the results highlight the benefits in using MIMO to deliver messages from static to mobile nodes in wireless sensor networks.

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Antenna Array Based Positioning Scheme for Unmanned Aerial Vehicles

WSA 2013; 17th International ITG Workshop on Smart Antennas,

Recently antenna arrays have been incorporated in Unmanned Aerial Vehicles (UAVs) in order to improve their communications capacities. Such antenna arrays can be further exploited in order to provide accurate estimation of a UAV pose and attitude which are necessary for the UAV movement and control. In this paper, we propose a pose estimation solution based on the 2-D Standard ESPRIT with Forward Backward Averaging (FBA) for determining the directions of arrival (DOAs) of the incoming signals. Moreover, by exploiting the geometry of the antenna array, it is possible to estimate the antenna positions, and then, by applying the TRIAD algorithm, it is possible to compute the attitude. We show, by means of simulations, that our proposed solution provides a very accurate pose estimation.

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Improved landing radio altimeter for unmanned aerial vehicles based on an antenna array

2012 IV International Congress on Ultra Modern Telecommunications and Control Systems,

Unmanned aerial vehicles (UAVs) are used in various applications such as civil and military surveillance, law enforcement, and support in natural disasters as well as in hazardous environments. Approaching and landing are necessary steps for all UAVs, indicating that radio altimeters are needed. In this paper, a radio altimeter based on an antenna array is proposed. Our solution allows some improvements over the traditional radio altimeter such as more precise altitude estimation, ground imaging without the need of side looking radar, mapping the obstacles positions and detecting the ground inclination and topology. Another important contribution of this paper is a review of traditional radio altimeters along with a performance comparison between the level-crossing detection and the digital signal processing frequency detection - which is based on the fast Fourier transform algorithm.

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Using cooperative MIMO techniques and UAV relay networks to support connectivity in sparse Wireless Sensor Networks

2013 International Conference on Computing, Management and Telecommunications (ComManTel),

One possible way to define the end of lifetime for a Wireless Sensor Network (WSN) is to set a threshold for the number of disconnections among the sensor nodes so that above this level the WSN becomes unable to provide the quality of services required by the users or even totally loses its ability to provide any service at all. Disconnections isolate sensors or group of sensors which cannot deliver their acquired data, thus constituting a sparse nonfunctional WSN, although some of its isolated or grouped sensors remain operational. A possible way to overcome such a problem is to provide an alternative reliable connection via other types of nodes to support the communication among isolated parts of a disconnected network. This paper proposes the use of cooperative multiple input multiple output (MIMO) techniques to support communication among static sensors in a sparse WSN and a relay network composed of Unmanned Aerial Vehicles (UAVs) keeping the WSN connected, thus extending its lifetime. Simulations of the proposed approach are performed and the acquired results highlight the benefits of this proposal.

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Cooperative MIMO for Wireless Sensor Network and Antenna Array based Solutions for Unmanned Aerial Vehicles

Bachelor Thesis,

he cheapening and increasing miniaturization of electronic components has enabled the use of wireless sensor networks for various purposes, from disaster prevention to patient monitoring in hospitals. These devices are generally battery powered and have great restrictions in its physical dimensions which makes it imperative that their energy efficiency is maximized. The use of unmanned aerial vehicles (UAVs) in conjunction with sensor networks has emerged as a viable solution for maintaining communication between network nodes. Techniques that use multiple antennas can be applied to minimize energy consumption in wireless sensor networks and assist in communication of such networks with groups of UAVs. The same set of antennas used for communication in UAVs can be used to provide other benefits, such as implementation of an altimeter and a precise positioning system that does not rely on external agents. This work presents a set of techniques for antenna arrays that can improve the efficiency of sensor networks, provide automated and safe control for UAVs and enable efficient interaction between these two systems.

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Applying MIMO Techniques to Minimize Energy Consumption for Long Distances Communications in Wireless Sensor Networks

Internet of Things, Smart Spaces, and Next Generation Networking Volume 7469,

This paper explores the usage of cooperative multiple input multiple output (MIMO) technique to minimize energy consumption used to establish communications among distant nodes in a wireless sensor network (WSN). As energy depletion is an outstanding problem in WSN research field, a number of techniques aim to preserve such resource, especially by means of savings during communication among sensor nodes. One such wide used technique is multi-hop communication to diminish the energy required by a single node to transmit a given message, providing a homogeneous consumption of the energy resources among the nodes in the network. However, it is not the case that multi-hop is always more efficient than single-hop, even that it may represent a great depletion of a single node’s energy. In this paper a cooperative MIMO transmission technique for WSN is presented, which is compared to single-hop and multi-hop transmission ones, highlighting its advantages in relation to both. Simulation results support the statement about the utility in applying the proposed technique for energy saving purposes.

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