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In particular, from their biological counterpart, they have inherited the feature that data processing is distributed through a large network of processing units.
Examples include sensor networks, wherein data processing is distributed among the nodes [1]; ad hoc mesh networks, wherein nodes act as relays for each other [2]; and the laboratory testbeds used to evaluate sensor and mesh network protocols [3].
Since data processing is distributed to the site of data collection rather than being performed at one central location, we describe this as a distributed processing surveillance system.
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The processing was distributed on two DSPs (16 DSP cores).
Data processing is implemented as a distributed process run on a computing cluster with 192 processor dual-dual core 2.8 Ghz computer nodes.
However, one of the key issues with such an approach for live data processing is partitioning skew in which data distributed over the processing units are not balanced due to uncertainty of the coming data streams.
For example, the MapReduce programming model along with Hadoop, introduced initially for massive distributed data processing, was explored [ 21– 23].
Data processing was performed in two steps.
All data processing was performed using Matlab.
Data processing was standardized.
Maintaining the archival data (the data that is distributed) in a relational database for flexible processing was both expensive and unnecessary.
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