Robust Network Transmission and Storage Using Coding

Abstract

This project focused on transmission and storage of information in networks, and considered robustness to adversarial errors, packet losses, link failure, mobility and topology dynamics. It established fundamental limits on performance (capacity, reliability and delay), as well as practical coding schemes and optimization techniques. Among our results, we developed network error correction theory for networks with non-uniform link capacities, non-multicast and a priori unknown number of errors. We applied this theory to design coding schemes for robust key distribution and streaming. We designed codes for error estimation, universal multicast codes robust to changes in network size and number of receivers, and error detection codes for distributed storage. We proposed efficient methods for obtaining network capacity bounds, characterized the impact of the failure of a single link on capacity of some families of networks, and showed equivalence between the Shannon capacity for saturated sources and the stable capacity of networks with probabilistic message arrivals. We characterized optimal resource allocation for maximizing the probability of data recovery under probabilistic access or failure of storage nodes, and optimizing transmission delay in disruption tolerant networks.

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Document Details

Document Type
Technical Report
Publication Date
Aug 09, 2013
Accession Number
ADA590765

Entities

People

  • Tracey Ho

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Coding
  • Computational Complexity
  • Computational Science
  • Construction
  • Disruption Tolerant Networks
  • Error Correction Codes
  • Error Detection Codes
  • Errors
  • Feedback
  • Information Theory
  • Network Topology
  • Networks
  • Packet Loss
  • Probability
  • Topology

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Neural Network Machine Learning.