Browsing by Subject "Network coding"

Now showing 1 - 4 of 4

Combatting loss in wireless networks
(2011-12) Rozner, Eric John; Qiu, Lili, Ph. D.; Alvisi, Lorenzo; Chandra, Ranveer; de Veciana, Gustavo; Zhang, Yin
The wireless medium is lossy due to many reasons, such as signal attenuation, multi-path propagation, and collisions. Wireless losses degrade network throughput, reliability, and latency. The goal of this dissertation is to combat wireless losses by developing effective techniques and protocols across different network layers. First, a novel opportunistic routing protocol is developed to overcome wireless losses at the network layer. Opportunistic routing protocols exploit receiver diversity to route traffic in the face of loss. A distinctive feature of the protocol is the performance derived from its optimization can be achieved in real IEEE 802.11 networks. At its heart lies a simple yet realistic model of the network that captures wireless interference, losses, traffic, and MAC-induced dependencies. Then a model-driven optimization algorithm is designed to accurately optimize the end-to-end performance, and techniques are developed to map the resulting optimization solutions to practical routing configurations. Its effectiveness is demonstrated using simulation and testbed experiments. Second, an efficient retransmission scheme (ER) is developed at the link layer for wireless networks. Instead of retransmitting lost packets in their original forms, ER codes packets lost at different destinations and uses a single retransmission to potentially recover multiple packet losses. A simple and practical protocol is developed to realize the idea, and it is evaluated using simulation and testbed experiments to demonstrate its effectiveness. Third, detailed measurement traces are collected to understand wireless losses in dynamic and mobile environments. Existing wireless drivers are modified to enable the logging and analysis of network activity under varying end-host configurations. The results indicate that mobile clients can suffer from consecutive packet losses, or burst errors. The burst errors are then analyzed in more detail to gain further insights into the problem. With these insights, recommendations for future research directions to mitigate loss in mobile environments are presented.
Efficient, provably secure code constructions
(2011-05) Agrawal, Shweta Prem; Vishwanath, Sriram; Boneh, Dan; Zuckerman, David; Garg, Vijay; Caramanis, Constantine; Sanghavi, Sujay
The importance of constructing reliable and efficient methods for securing digital information in the modern world cannot be overstated. The urgency of this need is reflected in mainstream media--newspapers and websites are full of news about critical user information, be it credit card numbers, medical data, or social security information, being compromised and used illegitimately. According to news reports, hackers probe government computer networks millions of times a day, about 9 million Americans have their identities stolen each year and cybercrime costs large American businesses 3.8 million dollars a year. More than 1 trillion worth of intellectual property has already been stolen from American businesses. It is this evergrowing problem of securing valuable information that our thesis attempts to address (in part). In this thesis, we study methods to secure information that are fast, convenient and reliable. Our overall contribution has four distinct threads. First, we construct efficient, "expressive" Public Key Encryption systems (specifically, Identity Based Encryption systems) based on the hardness of lattice problems. In Identity Based Encryption (IBE), any arbitrary string such as the user's email address or name can be her public key. IBE systems are powerful and address several problems faced by the deployment of Public Key Encryption. Our constructions are secure in the standard model. Next, we study secure communication over the two-user interference channel with an eavesdropper. We show that using lattice codes helps enhance the secrecy rate of this channel in the presence of an eavesdropper. Thirdly, we analyze the security requirements of network coding. Network Coding is an elegant method of data transmission which not only helps achieve capacity in several networks, but also has a host of other benefits. However, network coding is vulnerable to "pollution attacks" when there are malicious users in the system. We design mechanisms to prevent pollution attacks. In this setting, we provide two constructions -- a homomorphic Message Authentication Code (HMAC) and a Digital Signature, to secure information that is transmitted over such networks. Finally, we study the benefits of using Compressive Sensing for secure communication over the Wyner wiretap channel. Compressive Sensing has seen an explosion of interest in the last few years with its elegant mathematics and plethora of applications. So far however, Compressive Sensing had not found application in the domain of secrecy. Given its inherent assymetry, we ask (and answer in the affirmative) the question of whether it can be deployed to enable secure communication. Our results allow linear encoding and efficient decoding (via LASSO) at the legitimate receiver, along with infeasibility of message recovery (via an information theoretic analysis) at the eavesdropper, regardless of decoding strategy.
An information theoretic approach to structured high-dimensional problems
(2013-12) Das, Abhik Kumar; Vishwanath, Sriram
A majority of the data transmitted and processed today has an inherent structured high-dimensional nature, either because of the process of encoding using high-dimensional codebooks for providing a systematic structure, or dependency of the data on a large number of agents or variables. As a result, many problem setups associated with transmission and processing of data have a structured high-dimensional aspect to them. This dissertation takes a look at two such problems, namely, communication over networks using network coding, and learning the structure of graphical representations like Markov networks using observed data, from an information-theoretic perspective. Such an approach yields intuition about good coding architectures as well as the limitations imposed by the high-dimensional framework. Th e dissertation studies the problem of network coding for networks having multiple transmission sessions, i.e., multiple users communicating with each other at the same time. The connection between such networks and the information-theoretic interference channel is examined, and the concept of interference alignment, derived from interference channel literature, is coupled with linear network coding to develop novel coding schemes off ering good guarantees on achievable throughput. In particular, two setups are analyzed – the first where each user requires data from only one user (multiple unicasts), and the second where each user requires data from potentially multiple users (multiple multicasts). It is demonstrated that one can achieve a rate equalling a signi ficant fraction of the maximal rate for each transmission session, provided certain constraints on the network topology are satisfi ed. Th e dissertation also analyzes the problem of learning the structure of Markov networks from observed samples – the learning problem is interpreted as a channel coding problem and its achievability and converse aspects are examined. A rate-distortion theoretic approach is taken for the converse aspect, and information-theoretic lower bounds on the number of samples, required for any algorithm to learn the Markov graph up to a pre-speci fied edit distance, are derived for ensembles of discrete and Gaussian Markov networks based on degree-bounded graphs. The problem of accurately learning the structure of discrete Markov networks, based on power-law graphs generated from the con figuration model, is also studied. The eff ect of power-law exponent value on the hardness of the learning problem is deduced from the converse aspect – it is shown that discrete Markov networks on power-law graphs with smaller exponent values require more number of samples to ensure accurate recovery of their underlying graphs for any learning algorithm. For the achievability aspect, an effi cient learning algorithm is designed for accurately reconstructing the structure of Ising model based on power-law graphs from the con figuration model; it is demonstrated that optimal number of samples su ffices for recovering the exact graph under certain constraints on the Ising model potential values.
Optimizing opportunistic communication in wireless networks
(2011-08) Han, Mi Kyung; Qiu, Lili, Ph. D.; Lam, Simon; Zhang, Yin; de Veciana, Gustavo; Lee, Kang-won
Opportunistic communication leverages communication opportunities arising by chance to provide significant performance benefit and even enable communication where it would be impossible otherwise. The goal of this dissertation is to optimize opportunistic communication to achieve good performance in wireless networks. A key challenge in optimizing opportunistic communication arises from dynamic and incidental nature of communication. Complicated wireless interference patterns, high mobility, and frequent fluctuations in wireless medium make the optimization even harder. This dissertation proposes a series of optimization frameworks that systematically optimizes opportunistic communication to achieve good performance in wireless mesh networks and vehicular networks. We make the following three major contributions: First, we develop novel algorithms, techniques, and protocols that optimize opportunistic communication of wireless mesh network to achieve good, predictable user performance. Our framework systematically optimizes end-to-end performance (e.g., total throughput). It yields significant improvement over existing routing schemes. We also show that it is robust against inaccuracy introduced by dynamic network conditions. Second, we propose a novel overlay framework to exploit inter-flow network coding in opportunistic routing. In this framework, an overlay network performs inter-flow coding to effectively reduce traffic imposed on the underlay network, and an underlay network uses optimized opportunistic routing to provide efficient and reliable overlay links. We show that inter-flow coding together with opportunistic routing and rate-limiting brings significant performance benefit. Finally, we develop a novel optimization framework in vehicular networks to effectively leverage opportunistic contacts between vehicles and access points (APs). We develop a new mobility prediction algorithm and an optimization algorithm to determine an efficient replication scheme that exploit the synergy among Internet connectivity, local wireless connectivity, mesh network connectivity, and vehicular relay connectivity. Based on our framework, we develop a practical system that enables high-bandwidth content distribution and demonstrate the effectiveness of our approach using simulation, emulation, and testbed experiments.