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Proceedings Paper

A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications using Raspberry Pi
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Paper Abstract

The advancement of Internet of Things (IoT) technologies, such as low-cost embedded single board computers which integrate sensors, communication hardware, and processing power in one unit, has given more traction to the concept of Smart Cities. Having cheaper processing power at their disposal, the sensing units are capable of gathering increasingly larger amounts of raw data locally, which must be processed before being usable. One concern for this scheme is the amount of infrastructure and network bandwidth needed to transfer the data from the acquisition location to a server, which may be miles away, for further processing. The bandwidth available to the sensor network, distributed through the city, is expanding in a lower rate than the size and bandwidth demand of the network it serves. Therefore, transferring the unprocessed data to a central server does not seem feasible unless major compromises are made in terms of data resolution and size. This paper proposes a local big data based preprocessing scheme before the data is transferred to the storage. Using this scheme can free up the network bandwidth, exploit the otherwise wasted local processing power, and release processing load from the central server, allowing it to serve a larger network without the need for more powerful hardware. By making efficient use of network infrastructure the smart city applications are more affordable and scalable.

Paper Details

Date Published: 13 May 2019
PDF: 8 pages
Proc. SPIE 10989, Big Data: Learning, Analytics, and Applications, 1098902 (13 May 2019); doi: 10.1117/12.2517440
Show Author Affiliations
Behshad Mohebali, Florida State Univ. (United States)
Amirhessam Tahmassebi, Florida State Univ. (United States)
Amir H. Gandomi, Stevens Institute of Technology (United States)
Anke Meyer-Baese, Florida State Univ. (United States)

Published in SPIE Proceedings Vol. 10989:
Big Data: Learning, Analytics, and Applications
Fauzia Ahmad, Editor(s)

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