Towards Statistical Machine Learning for Edge Analytics in Large Scale Networks: Real-Time Gaussian Function Generation with Generic DSP

Simple item page
dc.contributor.authorOyekanlu, Emmanuel A
dc.contributor.authorOnidare, Samuel O
dc.contributor.authorOladele, Paul O
dc.date.accessioned2018-06-07T10:35:07Z
dc.date.available2018-06-07T10:35:07Z
dc.date.issued2018-05-31
dc.description.abstractThe smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytics in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other Gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, Gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the Gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which Gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based solution using this DSP in the smart grid and other IoT systems may not be too challenging.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/410
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.subjectDigital Signal Processoren_US
dc.subjectPower line communicationen_US
dc.subjectsmart griden_US
dc.subjectmachine learningen_US
dc.subjectopen-sourceen_US
dc.subjectembedded Cen_US
dc.subjectbig dataen_US
dc.titleTowards Statistical Machine Learning for Edge Analytics in Large Scale Networks: Real-Time Gaussian Function Generation with Generic DSPen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
SMAGRINET PAPER.pdf
Size:
621.45 KB
Format:
Adobe Portable Document Format
Description:
Main Article of an edited IEEE International conference proceedings
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.69 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections