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Browsing by Author "Tandon, Ravi"

Now showing 1 - 11 of 11
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    Data-Driven Methods for Modeling and Predicting Multivariate Time Series using Surrogates
    Chakraborty, Prithwish (Virginia Tech, 2016-07-05)
    Modeling and predicting multivariate time series data has been of prime interest to researchers for many decades. Traditionally, time series prediction models have focused on finding attributes that have consistent correlations with target variable(s). However, diverse surrogate signals, such as News data and Twitter chatter, are increasingly available which can provide real-time information albeit with inconsistent correlations. Intelligent use of such sources can lead to early and real-time warning systems such as Google Flu Trends. Furthermore, the target variables of interest, such as public heath surveillance, can be noisy. Thus models built for such data sources should be flexible as well as adaptable to changing correlation patterns. In this thesis we explore various methods of using surrogates to generate more reliable and timely forecasts for noisy target signals. We primarily investigate three key components of the forecasting problem viz. (i) short-term forecasting where surrogates can be employed in a now-casting framework, (ii) long-term forecasting problem where surrogates acts as forcing parameters to model system dynamics and, (iii) robust drift models that detect and exploit 'changepoints' in surrogate-target relationship to produce robust models. We explore various 'physical' and 'social' surrogate sources to study these sub-problems, primarily to generate real-time forecasts for endemic diseases. On modeling side, we employed matrix factorization and generalized linear models to detect short-term trends and explored various Bayesian sequential analysis methods to model long-term effects. Our research indicates that, in general, a combination of surrogates can lead to more robust models. Interestingly, our findings indicate that under specific scenarios, particular surrogates can decrease overall forecasting accuracy - thus providing an argument towards the use of 'Good data' against 'Big data'.
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    Design and Maintenance of Event Forecasting Systems
    Muthiah, Sathappan (Virginia Tech, 2021-03-26)
    With significant growth in modern forms of communication such as social media and micro- blogs we are able to gain a real-time understanding into events happening in many parts of the world. In addition, these modern forms of communication have helped shed light into the increasing instabilities across the world via the design of anticipatory intelligence systems [45, 43, 20] that can forecast population level events like civil unrest, disease occurrences with reasonable accuracy. Event forecasting systems are generally prone to become outdated (model drift) as they fail to keep-up with constantly changing patterns and thus require regular re-training in order to sustain their accuracy and reliability. In this dissertation we try to address some of the issues associated with design and maintenance of event forecasting systems in general. We propose and showcase performance results for a drift adaptation technique in event forecasting systems and also build a hybrid system for event coding which is cognizant of and seeks human intervention in uncertain prediction contexts to maintain a good balance between prediction-fidelity and cost of human effort. Specifically we identify several micro-tasks for event coding and build separate pipelines for each with uncertainty estimation capabilities and thereby be able to seek human feedback whenever required for each micro-task independent of the rest.
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    Distributed Storage Systems with Secure and Exact Repair - New Results
    Tandon, Ravi; Amuru, SaiDhiraj; Clancy, Thomas Charles III; Buehrer, R. Michael (IEEE, 2014-02)
    Distributed storage systems (DSS) in the presence of a passive eavesdropper are considered in this paper. A typical DSS is characterized by 3 parameters (n, k, d) where, a file is stored in a distributed manner across n nodes such that it can be recovered entirely from any k out of n nodes. Whenever a node fails, d ∈ [k, n) nodes participate in the repair process. In this paper, we study the exact repair capabilities of a DSS, where a failed node is replaced with its exact replica. Securing this DSS from a passive eavesdropper capable of wiretapping the repair process of any l < k nodes, is the main focus of this paper. Specifically, we characterize the optimal secure storagevs- exact-repair-bandwidth tradeoff region for the (4, 2, 3) DSS when l = 1 and the (n, n − 1, n − 1) DSS when l = n − 2.
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    Efficient Resource Allocation Schemes for Wireless Networks with with Diverse Quality-of-Service Requirements
    Kumar, Akshay (Virginia Tech, 2016-08-16)
    Quality-of-Service (QoS) to users is a critical requirement of resource allocation in wireless networks and has drawn significant research attention over a long time. However, the QoS requirements differ vastly based on the wireless network paradigm. At one extreme, we have a millimeter wave small-cell network for streaming data that requires very high throughput and low latency. At the other end, we have Machine-to-Machine (M2M) uplink traffic with low throughput and low latency. In this dissertation, we investigate and solve QoS-aware resource allocation problems for diverse wireless paradigms. We first study cross-layer dynamic spectrum allocation in a LTE macro-cellular network with fractional frequency reuse to improve the spectral efficiency for cell-edge users. We show that the resultant optimization problem is NP-hard and propose a low-complexity layered spectrum allocation heuristic that strikes a balance between rate maximization and fairness of allocation. Next, we develop an energy efficient downlink power control scheme in a energy harvesting small-cell base station equipped with local cache and wireless backhaul. We also study the tradeoff between the cache size and the energy harvesting capabilities. We next analyzed the file read latency in Distributed Storage Systems (DSS). We propose a heterogeneous DSS model wherein the stored data is categorized into multiple classes based on arrival rate of read requests, fault-tolerance for storage etc. Using a queuing theoretic approach, we establish bounds on the average read latency for different scheduling policies. We also show that erasure coding in DSS serves the dual purpose of reducing read latency and increasing the energy efficiency. Lastly, we investigate the problem of delay-efficient packet scheduling in M2M uplink with heterogeneous traffic characteristics. We classify the uplink traffic into multiple classes and propose a proportionally-fair delay-efficient heuristic packet scheduler. Using a queuing theoretic approach, we next develop a delay optimal multiclass packet scheduler and later extend it to joint medium access control and packet scheduling for M2M uplink. Using extensive simulations, we show that the proposed schedulers perform better than state-of-the-art schedulers in terms of average delay and packet delay jitter.
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    Event Detection and Extraction from News Articles
    Wang, Wei (Virginia Tech, 2018-02-21)
    Event extraction is a type of information extraction(IE) that works on extracting the specific knowledge of certain incidents from texts. Nowadays the amount of available information (such as news, blogs, and social media) grows in exponential order. Therefore, it becomes imperative to develop algorithms that automatically extract the machine-readable information from large volumes of text data. In this dissertation, we focus on three problems in obtaining event-related information from news articles. (1) The first effort is to comprehensively analyze the performance and challenges in current large-scale event encoding systems. (2) The second problem involves event detection and critical information extractions from news articles. (3) Third, the efforts concentrate on event-encoding which aims to extract event extent and arguments from texts. We start by investigating the two large-scale event extraction systems (ICEWS and GDELT) in the political science domain. We design a set of experiments to evaluate the quality of the extracted events from the two target systems, in terms of reliability and correctness. The results show that there exist significant discrepancies between the outputs of automated systems and hand-coded system and the accuracy of both systems are far away from satisfying. These findings provide preliminary background and set the foundation for using advanced machine learning algorithms for event related information extraction. Inspired by the successful application of deep learning in Natural Language Processing (NLP), we propose a Multi-Instance Convolutional Neural Network (MI-CNN) model for event detection and critical sentences extraction without sentence level labels. To evaluate the model, we run a set of experiments on a real-world protest event dataset. The result shows that our model could be able to outperform the strong baseline models and extract the meaningful key sentences without domain knowledge and manually designed features. We also extend the MI-CNN model and propose an MIMTRNN model for event extraction with distant supervision to overcome the problem of lacking fine level labels and small size training data. The proposed MIMTRNN model systematically integrates the RNN, Multi-Instance Learning, and Multi-Task Learning into a unified framework. The RNN module aims to encode into the representation of entity mentions the sequential information as well as the dependencies between event arguments, which are very useful in the event extraction task. The Multi-Instance Learning paradigm makes the system does not require the precise labels in entity mention level and make it perfect to work together with distant supervision for event extraction. And the Multi-Task Learning module in our approach is designed to alleviate the potential overfitting problem caused by the relatively small size of training data. The results of the experiments on two real-world datasets(Cyber-Attack and Civil Unrest) show that our model could be able to benefit from the advantage of each component and outperform other baseline methods significantly.
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    Fundamentals of Cache Aided Wireless Networks
    Sengupta, Avik (Virginia Tech, 2016-12-06)
    Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in content-centric next generation 5G wireless networks by leveraging localized content storage and delivery. Caching generally works in two phases namely (i) storage phase where parts of popular content is pre-fetched and stored in caches at the network edge during time of low network load and (ii) delivery phase where content is distributed to users at times of high network load by leveraging the locally stored content. Cache-aided networks therefore have the potential to leverage storage at the network edge to increase bandwidth efficiency. In this dissertation we ask the following question - What are the theoretical and practical guarantees offered by cache aided networks for reliable content distribution while minimizing transmission rates and increasing network efficiency? We furnish an answer to this question by identifying fundamental Shannon-type limits for cache aided systems. To this end, we first consider a cache-aided network where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models - (i) centralized content delivery where demands are serviced by the central server; and (ii) device-to-device-assisted distributed delivery where demands are satisfied by leveraging the collective content of user caches. For such cache aided networks, we develop a new technique for characterizing information theoretic lower bounds on the fundamental storage-rate trade-off. Furthermore, using the new lower bounds, we establish the optimal storage-rate trade-off to within a constant multiplicative gap and show that, for the case of multiple demands per user, treating each set of demands independently is order-optimal. To address the concerns of privacy in multicast content delivery over such cache-aided networks, we introduce the problem of caching with secure delivery. We propose schemes which achieve information theoretic security in cache-aided networks and show that the achievable rate is within a constant multiplicative factor of the information theoretic optimal secure rate. We then extend our theoretical analysis to the wireless domain by studying a cloud and cache-aided wireless network from a perspective of low-latency content distribution. To this end, we define a new performance metric namely normalized delivery time, or NDT, which captures the worst-case delivery latency. We propose achievable schemes with an aim to minimize the NDT and derive information theoretic lower bounds which show that the proposed schemes achieve optimality to within a constant multiplicative factor of 2 for all values of problem parameters. Finally, we consider the problem of caching and content distribution in a multi-small-cell heterogeneous network from a reinforcement learning perspective for the case when the popularity of content is unknown. We propose a novel topology-aware learning-aided collaborative caching algorithm and show that collaboration among multiple small cells for cache-aided content delivery outperforms local caching in most network topologies of practical interest. The results presented in this dissertation show definitively that cache-aided systems help in appreciable increase of network efficiency and are a viable solution for the ever evolving capacity demands in the wireless communications landscape.
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    Fundamentals of Efficient Spectrum Access and Co-existence with Receiver Nonlinearity
    Padaki, Aditya V. (Virginia Tech, 2018-01-29)
    RF front-ends are nonlinear systems that have nonlinear frequency response and, hence, can impair receiver performance by harmful adjacent channel interference in non-intuitive ways. Next generation wireless networks will see unprecedented diversity across receiver and radio technologies accessing the same band of spectrum in spatio-temporal proximity. Ensuring adjacent channel co-existence is of prime importance for successful deployment and operations of next generation wireless networks. Vulnerabilities of receiver front-end can have a severe detrimental effect on network performance and spectrum co-existence. This dissertation addresses the technological challenges in understanding and accounting for receiver sensitivities in the design of next generation wireless networks. The dissertation has four major contributions. In the first contribution, we seek to understand how receiver nonlinearity impacts performance. We propose a computationally efficient framework to evaluate the adjacent channel interference in a given radio/spectrum environment. We develop novel tractable representation of receiver front-end nonlinearity to specify the adjacent channel signals that contribute to the interference at the desired channel and the total adjacent channel interference power at a given desired channel. In the second contribution, we seek to understand how the impact of receiver nonlinearity performance can be quantified. We quantify receiver performance in the presence of adjacent channel interference using information theoretic metrics. We evaluate the limits on achievable rate accounting for RF front-end nonlinearity and provide a framework to compare disparate receivers by forming generalized metrics. In the third contribution, we seek to understand how the impact of receiver nonlinearity can be managed at the network level. We develop novel and comprehensive wireless network management frameworks that account for the RF nonlinearity, impairments, and diversity of heterogeneous wireless devices. We further develop computationally efficient algorithms to optimize the proposed framework and examine network level performance. We demonstrate through extensive network simulations that the proposed receiver-centric frameworks provide substantially high spectrum efficiency gains over receiver-agnostic spectrum access in dense and diverse next generation wireless networks. In the fourth contribution, we seek to understand how scalable interference networks are with receiver nonlinearity. We propose practical achievable schemes for interference avoidance and assess the scalability of the next generation wireless networks with interference due to receiver nonlinearity. Further, we develop an algorithmic scheme to evaluate the upper bound on scalability of nonlinear interference networks. This provides valuable insights on scalability and schemes for nonlinear adjacent channel interference avoidance in next generation shared spectrum networks.
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    Intelligent Approaches for Communication Denial
    Amuru, SaiDhiraj (Virginia Tech, 2015-10-05)
    Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great deal of work has been devoted to designing defenses against attacks from malicious nodes (e.g., anti-jamming), while significantly less work has been devoted to the equally important task of designing effective strategies for denying communication between enemy nodes/radios within an area (e.g., jamming). Such denial techniques are especially useful in military applications and intrusion detection systems where untrusted communication must be stopped. In this dissertation, we study these offensive attack procedures, collectively termed as communication denial. The communication denial strategies studied in this dissertation are not only useful in undermining the communication between enemy nodes, but also help in analyzing the vulnerabilities of existing systems. A majority of the works which address communication denial assume that knowledge about the enemy nodes is available a priori. However, recent advances in communication systems creates the potential for dynamic environmental conditions where it is difficult and most likely not even possible to obtain a priori information regarding the environment and the nodes that are present in it. Therefore, it is necessary to have cognitive capabilities that enable the attacker to learn the environment and prevent enemy nodes from accessing valuable spectrum, thereby denying communication. In this regard, we ask the following question in this dissertation ``Can an intelligent attacker learn and adapt to unknown environments in an electronic warfare-type scenario?" Fundamentally speaking, we explore whether existing machine learning techniques can be used to address such cognitive scenarios and, if not, what are the missing pieces that will enable an attacker to achieve spectrum supremacy by denying an enemy the ability to communicate? The first task in achieving spectrum supremacy is to identify the signal of interest before it can be attacked. Thus, we first address signal identification, specifically modulation classification, in practical wireless environments where the interference is often non-Gaussian. Upon identifying the signal of interest, the next step is to effectively attack the victim signals in order to deny communication. We present a rigorous fundamental analysis regarding the attackers performance, in terms of achieving communication denial, in practical communication settings. Furthermore, we develop intelligent approaches for communication denial that employ novel machine learning techniques to attack the victim either at the physical layer, the MAC layer, or the network layer. We rigorously investigate whether or not these learning techniques enable the attacker to approach the fundamental performance limits achievable when an attacker has complete knowledge of the environment. As a result of our work, we debunk several myths about communication denial strategies that were believed to be true mainly because incorrect system models were previously considered and thus the wrong questions were answered.
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    Online Denoising Solutions for Forecasting Applications
    Khadivi, Pejman (Virginia Tech, 2016-09-08)
    Dealing with noisy time series is a crucial task in many data-driven real-time applications. Due to the inaccuracies in data acquisition, time series suffer from noise and instability which leads to inaccurate forecasting results. Therefore, in order to improve the performance of time series forecasting, an important pre-processing step is the denoising of data before performing any action. In this research, we will propose various approaches to tackle the noisy time series in forecasting applications. For this purpose, we use different machine learning methods and information theoretical approaches to develop online denoising algorithms. In this dissertation, we propose four categories of time series denoising methods that can be used in different situations, depending on the noise and time series properties. In the first category, a seasonal regression technique is proposed for the denoising of time series with seasonal behavior. In the second category, multiple discrete universal denoisers are developed that can be used for the online denoising of discrete value time series. In the third category, we develop a noisy channel reversal model based on the similarities between time series forecasting and data communication and use that model to deploy an out-of-band noise filtering in forecasting applications. The last category of the proposed methods is deep-learning based denoisers. We use information theoretic concepts to analyze a general feed-forward deep neural network and to prove theoretical bounds for deep neural networks behavior. Furthermore, we propose a denoising deep neural network method for the online denoising of time series. Real-world and synthetic time series are used for numerical experiments and performance evaluations. Experimental results show that the proposed methods can efficiently denoise the time series and improve their quality.
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    Resource Allocation with Carrier Aggregation for Spectrum Sharing in Cellular Networks
    Shajaiah, Haya Jamal (Virginia Tech, 2016-04-29)
    Recently, there has been a massive growth in the number of mobile users and their traffic. The data traffic volume almost doubles every year. Mobile users are currently running multiple applications that require higher bandwidth which makes users so limited to the service providers' resources. Increasing the utilization of the existing spectrum can significantly improve network capacity, data rates and user experience. Spectrum sharing enables wireless systems to harvest under-utilized swathes of spectrum, which would vastly increase the efficiency of spectrum usage. Making more spectrum available can provide significant gain in mobile broadband capacity only if those resources can be aggregated efficiently with the existing commercial mobile system resources. Carrier aggregation (CA) is one of the most distinct features of 4G systems including Long Term Evolution Advanced (LTE-Advanced). In this dissertation, a resource allocation with carrier aggregation framework is proposed to allocate multiple carriers resources optimally among users with elastic and inelastic traffic in cellular networks. We use utility proportional fairness allocation policy, where the fairness among users is in utility percentage of the application running on the user equipment (UE). A resource allocation (RA) with CA is proposed to allocate single or multiple carriers resources optimally among users subscribing for mobile services. Each user is guaranteed a minimum quality of service (QoS) that varies based on the user's application type. In addition, a resource allocation with user discrimination framework is proposed to allocate single or multiple carriers resources among users running multiple applications. Furthermore, an application-aware resource block (RB) scheduling with CA is proposed to assign RBs of multiple component carriers to users' applications based on a utility proportional fairness scheduling policy. We believe that secure spectrum auctions can revolutionize the spectrum utilization of cellular networks and satisfy the ever increasing demand for resources. Therefore, a framework for multi-tier dynamic spectrum sharing system is proposed to provide an efficient sharing of spectrum with commercial wireless system providers (WSPs) with an emphasis on federal spectrum sharing. The proposed spectrum sharing system (SSS) provides an efficient usage of spectrum resources, manages intra-WSP and inter-WSP interference and provides essential level of security, privacy, and obfuscation to enable the most efficient and reliable usage of the shared spectrum. It features an intermediate spectrum auctioneer responsible for allocating resources to commercial WSPs' base stations (BS)s by running secure spectrum auctions. In order to insure truthfulness in the proposed spectrum auction, an optimal bidding mechanism is proposed to enable BSs (bidders) to determine their true bidding values. We also present a resource allocation based on CA approach to determine the BS's optimal aggregated rate allocated to each UE from both the BS's permanent resources and winning auctioned spectrum resources.
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    Towards Optimal Secure Distributed Storage Systems with Exact Repair
    Tandon, Ravi; Amuru, SaiDhiraj; Clancy, Thomas Charles III; Buehrer, R. Michael (IEEE, 2016-06)
    Distributed storage systems in the presence of a wiretapper are considered. A distributed storage system (DSS) is parameterized by three parameters (𝑛, 𝑘, 𝑑), in which a file stored across n distributed nodes, can be recovered from any 𝑘 out of 𝑛 nodes. This is called as the reconstruction property of a DSS. If a node fails, any 𝑑 out of (𝑛-1) nodes help in the repair of the failed node so that the regeneration property of the DSS is preserved. For such a (𝑛, 𝑘, 𝑑)-DSS, two types of wiretapping scenarios are investigated: (a) Type-I (node) adversary which can wiretap the data stored on any 𝑙< 𝑘 nodes; and a more severe (b) Type-II (repair data) adversary which can wiretap the contents of the repair data that is used to repair a set of l failed nodes over time. The focus of this work is on the practically relevant setting of exact repair regeneration in which the repair process must replace a failed node by its exact replica. We make new progress on several non-trivial instances of this problem which prior to this work have been open. The main contribution of this paper is the optimal characterization of the secure storage-vs-exact-repair-bandwidth tradeoff region of a (𝑛, 𝑘, 𝑑)-DSS, with 𝑛 ≤ 4 and any 𝑙 < 𝑘 in the presence of both Type-I and Type-II adversaries. While the problem remains open for a general (𝑛, 𝑘, 𝑑)-DSS with 𝑛 > 4, we present extensions of these results to a (𝑛, 𝑛-1, 𝑛-1)-DSS, in presence of a Type-II adversary that can observe the repair data of any 𝑙 = (𝑛-2) nodes. The key technical contribution of this work is in developing novel information theoretic converse proofs for the Type-II adversarial scenario. From our results, we show that in the presence of Type-II attacks, the only efficient point in the storage-vs-exact-repair-bandwidth tradeoff is the MBR (minimum bandwidth regenerating) point. This is in sharp contrast to the case of a Type-I attack in which the storage-vs-exactrepair-bandwidth tradeoff allows a spectrum of operating points beyond the MBR point.