22 research outputs found
From Dumb Wireless Sensors to Smart Networks using Network Coding
The vision of wireless sensor networks is one of a smart collection of tiny,
dumb devices. These motes may be individually cheap, unintelligent, imprecise,
and unreliable. Yet they are able to derive strength from numbers, rendering
the whole to be strong, reliable and robust. Our approach is to adopt a
distributed and randomized mindset and rely on in network processing and
network coding. Our general abstraction is that nodes should act only locally
and independently, and the desired global behavior should arise as a collective
property of the network. We summarize our work and present how these ideas can
be applied for communication and storage in sensor networks.Comment: To be presented at the Inaugural Workshop of the Center for
Information Theory and Its Applications, University of California - San
Diego, La Jolla, CA, February 6 - 10, 200
Decentralized Erasure Codes for Distributed Networked Storage
We consider the problem of constructing an erasure code for storage over a
network when the data sources are distributed. Specifically, we assume that
there are n storage nodes with limited memory and k<n sources generating the
data. We want a data collector, who can appear anywhere in the network, to
query any k storage nodes and be able to retrieve the data. We introduce
Decentralized Erasure Codes, which are linear codes with a specific randomized
structure inspired by network coding on random bipartite graphs. We show that
decentralized erasure codes are optimally sparse, and lead to reduced
communication, storage and computation cost over random linear coding.Comment: to appear in IEEE Transactions on Information Theory, Special Issue:
Networking and Information Theor
Implementation and performance evaluation of distributed cloud storage solutions using random linear network coding
This paper advocates the use of random linear network coding for storage in distributed clouds in order to reduce storage and traffic costs in dynamic settings, i.e. when adding and removing numerous storage devices/clouds on-the-fly and when the number of reachable clouds is limited. We introduce various network coding approaches that trade-off reliability, storage and traffic costs, and system complexity relying on probabilistic recoding for cloud regeneration. We compare these approaches with other approaches based on data replication and Reed-Solomon codes. A simulator has been developed to carry out a thorough performance evaluation of the various approaches when relying on different system settings, e.g., finite fields, and network/storage conditions, e.g., storage space used per cloud, limited network use, and limited recoding capabilities. In contrast to standard coding approaches, our techniques do not require us to retrieve the full original information in order to store meaningful information. Our numerical results show a high resilience over a large number of regeneration cycles compared to other approaches.Danish Council for Independent Research (Green Mobile Cloud Project DFF-090201372B)Hungarian National Development Agency (Research and Technology Innovation Fund Grant KMR_12-1-2012-0441)European Union (European Social Fund Project FuturICT.hu Grant TAMOP- 4.2.2.C-11/1/KONV-2012-0013