Compressed Sensing in Resource-Constrained Environments: From Sensing Mechanism Design to Recovery Algorithms

Compressed Sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for reconstruction. It is promising that CS can be utilized in environments where the signal acquisition process is extremely difficult or costly, e.g., a resource-constrained environment like the smartphone platform, or a band-limited environment like visual sensor network (VSNs). There are several challenges to perform sensing due to the characteristic of these platforms, including, for example, needing active user involvement, computational and storage limitations and lower transmission capabilities. This dissertation focuses on the study of CS in resource-constrained environments. First, we try to solve the problem on how to design sensing mechanisms that could better adapt to the resource-limited smartphone platform. We propose the compressed phone sensing (CPS) framework where two challenging issues are studied, the energy drainage issue due to continuous sensing which may impede the normal functionality of the smartphones and the requirement of active user inputs for data collection that may place a high burden on the user. Second, we propose a CS reconstruction algorithm to be used in VSNs for recovery of frames/images. An efficient algorithm, NonLocal Douglas-Rachford (NLDR), is developed. NLDR takes advantage of self-similarity in images using nonlocal means (NL) filtering. We further formulate the nonlocal estimation as the low-rank matrix approximation problem and solve the constrained optimization problem using Douglas-Rachford splitting method. Third, we extend the NLDR algorithm to surveillance video processing in VSNs and propose recursive Low-rank and Sparse estimation through Douglas-Rachford splitting (rLSDR) method for recovery of the video frame into a low-rank background component and sparse component that corresponds to the moving object. The spatial and temporal low-rank features of the video frame, e.g., the nonlocal similar patches within the single video frame and the low-rank background component residing in multiple frames, are successfully exploited

ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS

Compressive sensing is a mathematical theory concerning exact/approximate recovery of sparse/compressible vectors using the minimum number of measurements called projections. Its theory covers topics such as l1 optimisation, dimensionality reduction, information preserving projection matrices, random projection matrices and others. In this thesis we extend and use the theory of compressive sensing to address the challenges of limited computation power and energy supply in embedded systems. Three different problems are addressed. The first problem is to improve the efficiency of data gathering in wireless sensor networks. Many wireless sensor networks exhibit heterogeneity because of the environment. We leverage this heterogeneity and extend the theory of compressive sensing to cover non-uniform sampling to derive a new data collection protocol. We show that this protocol can realise a more accurate temporal-spatial profile for a given level of energy consumption. The second problem is to realise realtime background subtraction in embedded cameras. Background subtraction algorithms are normally computationally expensive because they use complex models to deal with subtle changes in background. Therefore existing background subtraction algorithms cannot provide realtime performance on embedded cameras which have limited processing power. By leveraging information preserving projection matrices, we derive a new background subtraction algorithm which is 4.6 times faster and more accurate than existing methods. We demonstrate that our background subtraction algorithm can realise realtime background subtraction and tracking in an embedded camera network. The third problem is to enable efficient and accurate face recognition on smartphones. The state-of-the-art face recognition algorithm is inspired by compressive sensing and is based on l1 optimisation. It also uses random projection matrices for dimensionality reduction. A key problem of using random projection matrices is that they give highly variable recognition accuracy. We propose an algorithm to optimise projection matrix to remove this performance variability. This means we can use fewer projections to achieve the same accuracy. This translates to a smaller l1 optimisation problem and reduces the computation time needed on smartphones, which have limited computation power. We demonstrate the performance of our proposed method on smartphones

Zero-padding Network Coding and Compressed Sensing for Optimized Packets Transmission

Ubiquitous Internet of Things (IoT) is destined to connect everybody and everything on a never-before-seen scale. Such networks, however, have to tackle the inherent issues created by the presence of very heterogeneous data transmissions over the same shared network. This very diverse communication, in turn, produces network packets of various sizes ranging from very small sensory readings to comparatively humongous video frames. Such a massive amount of data itself, as in the case of sensory networks, is also continuously captured at varying rates and contributes to increasing the load on the network itself, which could hinder transmission efficiency. However, they also open up possibilities to exploit various correlations in the transmitted data due to their sheer number. Reductions based on this also enable the networks to keep up with the new wave of big data-driven communications by simply investing in the promotion of select techniques that efficiently utilize the resources of the communication systems. One of the solutions to tackle the erroneous transmission of data employs linear coding techniques, which are ill-equipped to handle the processing of packets with differing sizes. Random Linear Network Coding (RLNC), for instance, generates unreasonable amounts of padding overhead to compensate for the different message lengths, thereby suppressing the pervasive benefits of the coding itself. We propose a set of approaches that overcome such issues, while also reducing the decoding delays at the same time. Specifically, we introduce and elaborate on the concept of macro-symbols and the design of different coding schemes. Due to the heterogeneity of the packet sizes, our progressive shortening scheme is the first RLNC-based approach that generates and recodes unequal-sized coded packets. Another of our solutions is deterministic shifting that reduces the overall number of transmitted packets. Moreover, the RaSOR scheme employs coding using XORing operations on shifted packets, without the need for coding coefficients, thus favoring linear encoding and decoding complexities. Another facet of IoT applications can be found in sensory data known to be highly correlated, where compressed sensing is a potential approach to reduce the overall transmissions. In such scenarios, network coding can also help. Our proposed joint compressed sensing and real network coding design fully exploit the correlations in cluster-based wireless sensor networks, such as the ones advocated by Industry 4.0. This design focused on performing one-step decoding to reduce the computational complexities and delays of the reconstruction process at the receiver and investigates the effectiveness of combined compressed sensing and network coding

Statistical Filtering for Multimodal Mobility Modeling in Cyber Physical Systems

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Journal of Telecommunications and Information Technology, 2008, nr 1

kwartalni

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Towards Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The ever-increasing number of resource-constrained Machine-Type Communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as eMBB, mMTC and URLLC, mMTC brings the unique technical challenge of supporting a huge number of MTC devices, which is the main focus of this paper. The related challenges include QoS provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead and Radio Access Network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy Random Access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and NB-IoT. Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions towards addressing RAN congestion problem, and then identify potential advantages, challenges and use cases for the applications of emerging Machine Learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity Q-learning approach in the mMTC scenarios. Finally, we discuss some open research challenges and promising future research directions.Comment: 37 pages, 8 figures, 7 tables, submitted for a possible future publication in IEEE Communications Surveys and Tutorial

Energy-driven techniques for massive machine-type communications

In the last few years, a lot of effort has been put into the development of the fifth generation of cellular networks (5G). Given the vast heterogeneity of devices coexisting in these networks, new approaches have been sought to meet all requirements (e.g., data rate, coverage, delay, etc.). Within that framework, massive machine-type communications (mMTC) emerge as a promising candidate to enable many Internet of Things applications. mMTC define a type of systems where large sets of simple and battery-constrained devices transmit short data packets simultaneously. Unlike other 5G use cases, in mMTC, a low cost and power consumption are extensively pursued. Due to these specifications, typical humantype communications (HTC) solutions fail in providing a good service. In this dissertation, we focus on the design of energy-driven techniques for extending the lifetime of mMTC terminals. Both uplink (UL) and downlink (DL) stages are addressed, with special attention to the traffic models and spatial distribution of the devices. More specifically, we analyze a setup where groups of randomly deployed sensors send their (possibly correlated) observations to a collector node using different multiple access schemes. Depending on their activity, information might be transmitted either on a regular or sporadic basis. In that sense, we explore resource allocation, data compression, and device selection strategies to reduce the energy consumption in the UL. To further improve the system performance, we also study medium access control protocols and interference management techniques that take into account the large connectivity in these networks. On the contrary, in the DL, we concentrate on the support of wireless powered networks through different types of energy supply mechanisms, for which proper transmission schemes are derived. Additionally, for a better representation of current 5G deployments, the presence of HTC terminals is also included. Finally, to evaluate our proposals, we present several numerical simulations following standard guidelines. In line with that, we also compare our approaches with state-of-the-art solutions. Overall, results show that the power consumption in the UL can be reduced with still good performance and that the battery lifetimes can be improved thanks to the DL strategies.En els últims anys, s'han dedicat molts esforços al desenvolupament de la cinquena generació de telefonia mòbil (5G). Donada la gran heterogeneïtat de dispositius coexistint en aquestes xarxes, s'han buscat nous mètodes per satisfer tots els requisits (velocitat de dades, cobertura, retard, etc.). En aquest marc, les massive machine-type communications (mMTC) sorgeixen com a candidates prometedores per fer possible moltes aplicacions del Internet of Things. Les mMTC defineixen un tipus de sistemes en els quals grans conjunts de dispositius senzills i amb poca bateria, transmeten simultàniament paquets de dades curts. A diferència d'altres casos d'ús del 5G, en mMTC es persegueix un cost i un consum d'energia baixos. A causa d'aquestes especificacions, les solucions típiques de les human-type communications (HTC) no aconsegueixen proporcionar un bon servei. En aquesta tesi, ens centrem en el disseny de tècniques basades en l'energia per allargar la vida útil dels terminals mMTC. S'aborden tant les etapes del uplink (UL) com les del downlink (DL), amb especial atenció als models de trànsit i a la distribució espacial dels dispositius. Més concretament, analitzem un escenari en el qual grups de sensors desplegats aleatòriament, envien les seves observacions (possiblement correlades) a un node col·lector utilitzant diferents esquemes d'accés múltiple. Depenent de la seva activitat, la informació es pot transmetre de manera regular o esporàdica. En aquest sentit, explorem estratègies d'assignació de recursos, compressió de dades, i selecció de dispositius per reduir el consum d'energia en el UL. Per millorar encara més el rendiment del sistema, també estudiem protocols de control d'accés al medi i tècniques de gestió d'interferències que tinguin en compte la gran connectivitat d'aquestes xarxes. Per contra, en el DL, ens centrem en el suport de les wireless powered networks mitjançant diferents mecanismes de subministrament d'energia, per als quals es deriven esquemes de transmissió adequats. A més, per una millor representació dels desplegaments 5G actuals, també s'inclou la presència de terminals HTC. Finalment, per avaluar les nostres propostes, presentem diverses simulacions numèriques seguint pautes estandarditzades. En aquesta línia, també comparem els nostres enfocaments amb les solucions de l'estat de l'art. En general, els resultats mostren que el consum d'energia en el UL pot reduir-se amb un bon rendiment i que la durada de la bateria pot millorar-se gràcies a les estratègies del DL.En los últimos años, se han dedicado muchos esfuerzos al desarrollo de la quinta generación de telefonía móvil (5G). Dada la gran heterogeneidad de dispositivos coexistiendo en estas redes, se han buscado nuevos métodos para satisfacer todos los requisitos (velocidad de datos, cobertura, retardo, etc.). En este marco, las massive machine-type communications (mMTC) surgen como candidatas prometedoras para hacer posible muchas aplicaciones del Internet of Things. Las mMTC definen un tipo de sistemas en los cuales grandes conjuntos de dispositivos sencillos y con poca batería, transmiten simultáneamente paquetes de datos cortos. A diferencia de otros casos de uso del 5G, en mMTC se persigue un coste y un consumo de energía bajos. A causa de estas especificaciones, las soluciones típicas de las human-type communications (HTC) no consiguen proporcionar un buen servicio. En esta tesis, nos centramos en el diseño de técnicas basadas en la energía para alargar la vida ´útil de los terminales mMTC. Se abordan tanto las etapas del uplink (UL) como las del downlink (DL), con especial atención a los modelos de tráfico y a la distribución espacial de los dispositivos. Más concretamente, analizamos un escenario en el cual grupos de sensores desplegados aleatoriamente, envían sus observaciones (posiblemente correladas) a un nodo colector utilizando diferentes esquemas de acceso múltiple. Dependiendo de su actividad, la información se puede transmitir de manera regular o esporádica. En este sentido, exploramos estrategias de asignación de recursos, compresión de datos, y selección de dispositivos para reducir el consumo de energía en el UL. Para mejorar todavía más el rendimiento del sistema, también estudiamos protocolos de control de acceso al medio y técnicas de gestión de interferencias que tengan en cuenta la gran conectividad de estas redes. Por el contrario, en el DL, nos centramos en el soporte de las wireless powered networks mediante diferentes mecanismos de suministro de energía, para los cuales se derivan esquemas de transmisión adecuados. Además, para una mejor representación de los despliegues 5G actuales, también se incluye la presencia de terminales HTC. Finalmente, para evaluar nuestras propuestas, presentamos varias simulaciones numéricas siguiendo pautas estandarizadas. En esta línea, también comparamos nuestros enfoques con las soluciones del estado del arte. En general, los resultados muestran que el consumo de energía en el UL puede reducirse con un buen rendimiento y que la duración de la batería puede mejorarse gracias a las estrategias del DLPostprint (published version