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Long-term fault tolerant storage of critical sensor data in the intercloud

Til, J.R. van der (2013) Long-term fault tolerant storage of critical sensor data in the intercloud. Master's Thesis / Essay, Computing Science.

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Wireless sensor networks consist of distributed, wirelessly enabled embedded devices capable of employing a variety of electronic sensors. Each node in a wireless sensor network is equipped with one or more sensors in addition to a microcontroller, a wireless transceiver, and an energy source. The microcontroller functions with the electronic sensors as well as the transceiver to form an efficient system for relaying small amounts of important data with minimal power consumption. All the sensors combined in the wireless sensor network are capable of generating tremendous amounts of data. This data has to be processed as well as stored for possible future requirements. Because storing Petabytes of data is a very specialized task, not every company wants to perform this itself. For this reason we look at the capabilities cloud computing offers to store large amounts of data. However, confidentiality, integrity, availability and performance are concerns when we rely on a single cloud provider. Also the lifetime of the data is tied to the lifetime of the chosen cloud provider. We have improved the Byzantine fault tolerant quorum protocols proposed by Bessani et al. by processing the input data as a stream instead of a large block. Techniques used include encryption, erasure coding, secret sharing, and public key cryptography, to provide a way to store data in a quorum of cloud providers with a space efficiency of roughly one third (1/3). We provide the improved pseudocode with proofs as well as a description of the architecture and design decisions for our implementation. In our performance analysis we show that we are capable of storing up to 500 000 measurements per second on a single virtual machine. Using compression techniques and more machines will allow this number to be increased even more.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:54
Last Modified: 15 Feb 2018 07:54

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