Adaptive Selection Problems in Networked Systems

Networked systems are composed of interconnected nodes that work collaboratively to maximize a given overall utility function. Typical examples of such systems are wireless sensor networks (WSNs) and participatory sensing systems: sensor nodes, either static or mobile, are deployed for monitoring a certain physical field. In these systems, there are a set of problems where we need to adaptively select a strategy to run the system, in order to enhance the efficiency of utilizing the resources available to the system. In particular, we study four adaptive selection problems as follows. We start by studying the problem of base-station (BS) selection in WSNs. Base stations are critical sensor nodes whose failures cause severe data losses. Deploying multiple fixed BSs improves the robustness, yet this scheme is not energy efficient because BSs have high energy consumptions. We propose a scheme that selects only one BS to be active at a time; other BSs are kept passive and act as regular sensor nodes. This scheme substantially reduces the energy supplies required by individual BSs. Then, we propose an algorithm for adaptively selecting the active BS so that the spatially and temporally varying energy resources are efficiently utilized. We also address implementation issues and apply the proposed algorithm on a real WSN. Field experiments have shown the effectiveness of the proposed algorithm. We generalize the BS selection problem by considering both the energy efficiency of regular sensor nodes and that of BSs. In this scheme, a subset of active BSs (instead of only one) is adaptively selected and the routing of regular sensor nodes is adjusted accordingly. Because BSs have high fixed-energy consumptions and because the number of candidate subsets of active BSs is exponential with the number of BSs, this general BS selection problem is NP-hard. We propose a polynomial-time algorithm that is guaranteed, under mild conditions, to achieve a network lifetime at least 62% of the optimal one. Through extensive numerical simulations, we verify that the lifetime achieved by the proposed algorithm is always very close to the optimum. We then study the problem of scheduling the sparse-sensing patterns in WSNs. We observe that the traditional scheme of periodically taking sensing samples is not energy efficient. Instead, we propose to adaptively schedule when and where to activate sensors for sampling a physical field, such that the energy efficiency is enhanced and the sensing precision is maintained. The schedules are learnt from the temporal signal models derived from the collected measurements. Then, using the obtained signal models and the sparse sensing-measurements, the original signal can be effectively recovered. This proposed method requires minimal on-board computation, no inter-node communications and achieves an appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse-sensing schemes, particularly when the measured data is characterized by a strong temporal correlation. In the last part of the thesis, we discuss the sparse-sensing framework by exploiting the spatial correlations rather than the temporal correlations among the captured measurements. In this framework, application-specific utility functions can be employed. By adaptively selecting a small subset of active sensors for sensing, a certain utility is guaranteed and the efficiency of the sensing system is enhanced. We apply this framework both in static WSNs and participatory sensing systems where sensors move in an uncoordinated manner. Through extensive simulations, we show that our proposed algorithm enhances the resource efficiency.

Distributed Compressed Representation of Correlated Image Sets

Vijayaraghavan Thirumalai

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoints or at different time instants. Therefore, these images are generally correlated through displacement of scene objects. Efficient compression techniques have to exploit this correlation in order to efficiently communicate the 3D scene information. Instead of joint encoding that requires communication between the cameras, in this thesis we concentrate on distributed representation, where the captured images are encoded independently, but decoded jointly to exploit the correlation between images. One of the most important and challenging tasks relies in estimation of the underlying correlation from the compressed correlated images for effective reconstruction or analysis in the joint decoder. This thesis focuses on developing efficient correlation estimation algorithms and joint representation of multiple correlated images captured by various sensing methodologies, e.g., planar, omnidirectional and compressive sensing (CS) sensors. The geometry of the 2D visual representation and the acquisition complexity vary for each sensor type. Therefore, we need to carefully consider the specific geometric nature of the captured images while developing distributed representation algorithms. In this thesis we propose robust algorithms in different scene analysis and reconstruction scenarios. We first concentrate on the distributed representation of omnidirectional images captured by catadioptric sensors. The omnidirectional images are captured from different viewpoints and encoded independently with a balanced rate distribution among the different cameras. They are mapped on the sphere which captures the plenoptic function in its radial form without Euclidean discrepancies. We propose a transform-based distributed coding algorithm, where the spherical images initially undergo a multi-resolution decomposition. The visual information is then split into two correlated partitions. The encoder transmits one partition after entropy coding, as well as the syndrome bits resulting from the Slepian-Wolf encoding of the other partition. The joint decoder estimates a disparity image to take benefit of the correlation between views and uses the syndrome bits to decode the missing information. Such a strategy proves to be beneficial with respect to the independent processing of images and shows only a small performance loss compared to the joint encoding of different views. The encoding complexity in the previous approach is non-negligible due to the visual information processing based on Slepian-Wolf coding and its associated rate parameter estimation. We therefore discard the Slepian-Wolf encoding and propose a distributed coding solution, where the correlated images are encoded independently using transform-based coding solutions (e.g., SPIHT). The central decoder now builds a correlation model from the compressed images, which is used to jointly decode a pair of images. Experimental results demonstrate that the proposed distributed coding solution improves the rate-distortion performance of the separate coding results for both planar and omnidirectional images. However, this improvement is significant only at medium to high bit rates. We therefore propose a rate allocation scheme that identifies and transmits the necessary visual information from each image to improve the correlation estimation accuracy at low bit rate. Experimental results show that for a given bit budget the proposed encoding scheme permits to compute an accurate correlation estimation comparing to the one obtained with SPIHT, JPEG 2000 or JPEG coding schemes. We show however that the improvement in the correlation estimation comes at the price of penalizing the image reconstruction quality; therefore there exists an interesting trade-off between the accurate correlation estimation and image reconstruction as encoding optimization objectives are different in both cases. Next, we further simplify the encoding complexity by replacing the classical imaging sensors with the simple CS sensors, that directly acquire the compressed images in the form of quantized linear measurements. We now concentrate on the particular problem, where one image is selected as the reference and it is used as a side information for the correlation estimation. We propose a geometry-based model to describe the correlation between the visual information in a pair of images. The joint decoder first captures the most prominent visual features in the reconstructed reference image using geometric functions. Since the images are correlated, these features are likely to be present in the other images too, possibly with geometric transformations. Hence, we propose to estimate the correlation model with a regularized optimization problem that locates these features in the compressed images. The regularization terms enforce smoothness of the transformation field, and consistency between the estimated images and the quantized measurements. Experimental results show that the proposed scheme is able to efficiently estimate the correlation between images for several multi-view and video datasets. The proposed scheme is finally shown to outperform DSC schemes based on unsupervised disparity (or motion) learning, as well as independent coding solutions based on JPEG 2000. We then extend the previous scenario to a symmetric decoding problem, where we are interested to estimate the correlation model directly from the quantized linear measurements without explicitly reconstructing the reference images. We first show that the motion field that represents the main source of correlation between images can be described as a linear operator. We further derive a linear relationship between the correlated measurements in the compressed domain. We then derive a regularized cost function to estimate the correlation model directly in the compressed domain using graph-based optimization algorithms. Experimental results show that the proposed scheme estimates an accurate correlation model among images in both multi-view and video imaging scenarios. We then propose a robust data fidelity term that improves the quality of the correlation estimation when the measurements are quantized. Finally, we show by experiments that the proposed compressed correlation estimation scheme is able to compete the solution of a scheme that estimates a correlation model from the reconstructed images without the complexity of image reconstruction. Finally, we study the benefit of using the correlation information while jointly reconstructing the images from the compressed linear measurements. We consider both the asymmetric and symmetric scenarios described previously. We propose joint reconstruction methodologies based on a constrained optimization problem which is solved using effective proximal splitting methods. The constraints included in our framework enforce the reconstructed images to satisfy both the correlation and the quantized measurements consistency objectives. Experimental results demonstrate that the proposed joint reconstruction scheme improves the quality of the decoded images, when compared to a scheme where the images are handled independently. In this thesis we build efficient distributed scene representation algorithms for the multiple correlated images captured in planar, omnidirectional and CS cameras. The coding rate in our symmetric distributed coding solution stays balanced between the encoders and stays close to the joint encoding solutions. Our novel algorithms lead to effective correlation estimation in different sensing and coding scenarios. In addition, we provide innovative solutions for robust correlation estimation from highly compressed images in simple sensing frameworks. Our CS-based joint reconstruction frameworks effectively exploit the inter-view correlation, that permits to achieve high compression gains compared to state-of-the-art independent and distributed coding solutions.

EPFL2012

Rate allocation and optimized decoding in inter-session network coding

Eirina Bourtsoulatze

Recent advances in data processing and communication systems have led to a continuous increase in the amount of data communicated over today’s networks. These large volumes of data pose new challenges on the current networking infrastructure that only offers a best effort mechanism for data delivery. The emergence of new distributed network architectures, such as peer-to-peer networks and wireless mesh networks, and the need for efficient data delivery mechanisms have motivated researchers to reconsider the way that information is communicated and processed in the networks. This has given rise to a new research field called network coding. The network coding paradigm departs from the traditional routing principle where information is simply relayed by the network nodes towards the destination, and introduces some intelligence in the network through coding at the intermediate nodes. This in-network data processing has been proved to substantially improve the performance of data delivery systems in terms of throughput and error resilience in networks with high path diversity. Motivated by the promising results in the network coding research, we focus in this thesis on the design of network coding algorithms for simultaneous transmission of multiple data sources in overlay networks. We investigate several problems that arise in the context of inter-session network coding, namely (i) decoding delay minimization in inter-session network coding, (ii) distributed rate allocation for inter-session network coding and (iii) correlation-aware decoding of incomplete network coded data. We start by proposing a novel framework for data delivery from multiple sources to multiple clients in an overlay wireline network, where intermediate nodes employ randomized inter-session network coding. We consider networks with high resource diversity, which creates network coding opportunities with possibly large gains in terms of throughput, delay and error robustness. However, the coding operations in the intermediate nodes must be carefully designed in order to enable efficient data delivery. We look at the problem from the decoding delay perspective and design solutions that lead to a small decoding delay at clients through proper coding and rate allocation. We cast the optimization problem as a rate allocation problem, which seeks for the coding operations that minimize the average decoding delay in the client population. We demonstrate the validity of our algorithm through simulations in representative network topologies. The results show that an effective combination of intra- and inter-session network coding based on randomized linear coding permits to reach small decoding delays and to better exploit the available network resources even in challenging network settings. Next, we design a distributed rate allocation algorithm where the users decide locally how many intra- and inter-session network coded packets should be requested from the parent nodes in order to get minimal decoding delay. The capability to take coding decisions locally with only a partial knowledge of the network statistics is of crucial importance for applications where users are organized in dynamic overlay networks. We propose a receiver-driven communication protocol that operates in two rounds. First, the users request and obtain information regarding the network conditions and packet availability in their local neighborhood. Then, every user independently optimizes the rate allocation among different possible intra- and inter-session packet combinations to be requested from its parents. We also introduce the novel concept of equivalent flows, which permits to efficiently estimate the expected number of packets that are necessary for decoding and hence to simplify the rate allocation process. Experimental results indicate that our algorithm is capable of eliminating the bottlenecks and reducing the decoding delay of users with limited resources. We further investigate the application of the proposed distributed rate allocation algorithm to the transmission of video sequences and validate the performance of our system using the NS-3 simulator. The simulation results show that the proposed rate allocation algorithm is successful in improving the quality of the delivered video compared to intra-session network coding based solutions. Finally, we investigate the problem of decoding the source information from an incomplete set of network coded data with the help of source priors in a finite algebraic field. The inability to form a complete decoding system can be often caused by transmission losses or timing constraints imposed by the application. In this case, exact reconstruction of the source data by conventional algorithms such as Gaussian elimination is not feasible; however, partial recovery of the source data may still be possible, which can be useful in applications where approximate reconstruction is informative. We use the statistical characteristics of the source data in order to perform approximate decoding. We first analyze the performance of a hypothetical maximum a posteriori decoder, which recovers the source data from an incomplete set of network coded data given the joint statistics of the sources. We derive an upper bound on the probability of erroneous source sequence decoding as a function of the system parameters. We then propose a constructive solution to the approximate decoding problem and design an iterative decoding algorithm based on message passing, which jointly considers the network coding and the correlation constraints. We illustrate the performance of our decoding algorithm through extensive simulations on synthetic and real data sets. The results demonstrate that, even by using a simple correlation model expressed as a correlation noise between pairs of sources, the original source data can be partially decoded in practice from an incomplete set of network coded symbols. Overall, this thesis addresses several important issues related to the design of efficient data delivery methods with inter-session network coding. Our novel framework for decoding delay minimization can impact the development of practical inter-session network coding algorithms that are appropriate for applications with low delay requirements. Our rate allocation algorithms are able to exploit the high resource diversity of modern networking systems and represent an effective alternative in the development of distributed communication systems. Finally, our algorithm for data recovery from incomplete network coded data using correlation priors can contribute significantly to the improvement of the delivered data quality and provide new insights towards the design of joint source and network coding algorithms.

EPFL2013

Sensing the real world

Juri Ranieri

A sensor is a device that detects or measures a physical property and records, indicates, or otherwise responds to it. In other words, a sensor allows us to interact with the surrounding environment, by measuring qualitatively or quantitatively a given phenomena. Biological evolution provided every living entity with a set of sensors to ease the survival to daily challenges. In addition to the biological sensors, humans developed and designed “artificial” sensors with the aim of improving our capacity of sensing the real world. Today, thanks to technological developments, sensors are ubiquitous and thus, we measure an exponentially growing amount of data. Here is the challenge—how do we process and use this data? Nowadays, it is common to design real-world sensing architectures that use the measured data to estimate certain parameters of the measured physical field. This type of problems are known in mathematics as inverse problems and finding their solution is challenging. In fact, we estimate a set of parameters of a physical field with possibly infinite degrees of freedom with only a few measurements, that are most likely corrupted by noise. Therefore, we would like to design algorithms to solve the given inverse problem, while ensuring the existence of the solution, its uniqueness and its robustness to the measurement noise. In this thesis, we tackle different inverse problems, all inspired by real-world applications. First, we propose a new regularization technique for linear inverse problems based on the sensor placement optimization of the sensor network collecting the data. We propose Frame- Sense, a greedy algorithm inspired by frame theory that finds a near-optimal sensor placement with respect to the reconstruction error of the inverse problem solution in polynomial time. We substantiate our theoretical findings with numerical simulations showing that our method improves the state of the art. In particular, we show significant improvements on two realworld applications: the thermal monitoring of many-core processors and the adaptive sampling scheduling of environmental sensor networks. Second, we introduce the dual of the sensor placement problem, namely the source placement problem. In this case, instead of regularizing the inverse problem, we enable a precise control of the physical field by means of a forward problem. For this problem, we propose a near-optimal algorithm for the noiseless case, that is when we know exactly the current state of the physical field. Third, we consider a family of physical phenomena that can be modeled by means of graphs, where the nodes represent a set of entities and the edges model the transmission delay of an information between the entities. Examples of this phenomena are the spreading of a virus within the population of a given region or the spreading of a rumor on a social network. In this scenario, we identify two new key problems: the source placement and vaccination. For the former, we would like to find a set of sources such that the spreading of the information over the network is as fast as possible. For the latter, we look for an optimal set of nodes to be “vaccinated” such that the spreading of the virus is the slowest. For both problems, we propose greedy algorithms directly optimizing the average time of infection of the network. Such algorithms out-perform the current state of the art and we evaluate their performance with a set of experiments on synthetic datasets. Then, we discuss three distinct inverse problems for physical fields characterized by a diffusive phenomena, such as temperature of solid bodies or the dispersion of pollution in the atmosphere. We first study the uniform sampling and reconstruction of diffusion fields and we show that we can exploit the kernel of the field to control and bound the aliasing error. Second, we study the source estimation of a diffusive field given a set of spatio-temporal measurements of the field and under the assumption that the sources can be modeled as a set of Dirac’s deltas. For this estimation problem, we propose an algorithm that exploits the eigenfunctions representation of the diffusion field and we show that this algorithm recovers the sources precisely. Third, we propose an algorithm for the estimation of time-varying emissions of smokestacks from the data collected in the surrounding environment by a sensor network, under the assumption that the emission rates can be modeled as signals lying on low-dimensional subspaces or with a finite rate of innovation. Last, we analyze a classic non-linear inverse problem, namely the sparse phase retrieval. In such a problem, we would like to estimate a signal from just the magnitude of its Fourier transform. Phase retrieval is of interest for many scientific applications, such as X-ray crystallography and astronomy. We assume that the signal of interest is spatially sparse, as it happens for many applications, and we model it as a linear combination of Dirac’s delta. We derive sufficient conditions for the uniqueness of the solution based on the support of the autocorrelation function of the measured sparse signal. Finally, we propose a reconstruction algorithm for the sparse phase retrieval taking advantage of the sparsity of the signal of interest.

EPFL2014