An optimized decentralized internet of things (IoT) architecture for long range wireless area network (LoRaWAN) within IBM node-red

Clicks: 2
ID: 286658
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #3,145 of 3,757 articles by views in Malay Journal

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 3,757 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
In this study, the development of an optimized decentralized Internet of Things (IoT) architecture for Long Range Wireless Area Network (LoRaWAN) with IBM NODE-RED is presented. An architecture, based on the guidelines provided by The Things Network, was deployed for the collection of reference data. A new optimized architecture, equipped with Bit Shifting and a Support Vector Machine (SVM) Classifier as payload filter, was also deployed. The new architecture is designed for a more efficient payload preparation process, ideally using less data than the reference architecture. Moreover, the SVM classifier filters unhealthy payloads out, triggering a downlink to request a replacement payload. To avoid errors and an infinite loop of request, the proponent has included a counter. Lastly, the new architecture is mapped in IBM Node-RED for ease of use and data visualization purposes. Results have shown that Manual Bit Shifting has reduced payload size by as much as 15.55% and airtime by as much as 11.04%. This increases the capacity of the system to send 40 more messages per day. In addition, the SVM classifier has improved the credibility of the system to collect data, as tested repeatedly. Overall, the study has optimized the entire process, as shown in the comparison of RSSI and SNR results between the two architectures. Hence, it can be concluded that through the improvement made in various parts of the architecture, the new architecture has been proven to be faster, more efficient, and more reliable in collecting usable data, collectively optimizing it compared to the reference architecture by The Things Network.
Reference Key
persistent_1760659250_68f1873285b47 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rapliza, Annamitz A.
Journal Malay Journal
Year 2020
DOI
DOI not found
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.