<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns="http://purl.org/rss/1.0/" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/143802">
    <title>OAR@UM Collection:</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/143802</link>
    <description />
    <items>
      <rdf:Seq>
        <rdf:li rdf:resource="https://www.um.edu.mt/library/oar/handle/123456789/144078" />
      </rdf:Seq>
    </items>
    <dc:date>2026-06-29T03:58:16Z</dc:date>
  </channel>
  <item rdf:about="https://www.um.edu.mt/library/oar/handle/123456789/144078">
    <title>Adoption of the LoRa transmission protocol for a low power indoor air quality monitoring system</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/144078</link>
    <description>Title: Adoption of the LoRa transmission protocol for a low power indoor air quality monitoring system
Abstract: Indoor air quality (IAQ) is a critical, often-overlooked public health concern, driving the &#xD;
need for robust Internet of Things (IoT) monitoring systems to optimise building &#xD;
ventilation and energy efficiency. This research addresses two major gaps: the high power &#xD;
consumption of existing wireless sensor nodes and the lack of cost-effective, scalable big &#xD;
data systems for large-scale IAQ monitoring.&#xD;
The core contribution is an ultra-low-power, low-cost wireless sensor node integrating &#xD;
state-of-the-art (SOA) sensors for carbon dioxide, volatile organic compounds, particulate &#xD;
matter, temperature, humidity, and pressure. Utilising dynamic power management, a &#xD;
sleep mode current draw of 270 nA and an average active current of 38 mA is achieved. &#xD;
This translates to an overall energy consumption of approximately 327 μAh per hour, and &#xD;
a projected battery life of 40 months on a 10,500 mAh battery. The achieved power &#xD;
efficiency is significantly better than both comparable academic and commercial SOA &#xD;
devices, even while offering a broader range of sensing capabilities.&#xD;
Complementary to this, the work introduces a cost-effective, LoRa-based big data system &#xD;
for large-scale IAQ monitoring. This system features a novel data forwarding server that &#xD;
calculates Air Quality Index (AQI) and Thermal Comfort Index (TCI) values, storing the &#xD;
enriched data in a document-oriented database. The research also validated a theoretical &#xD;
simulation model for indoor LoRa propagation. Advanced data visualisation was also &#xD;
developed, including a coordinate-based AQI heatmap, enabling smarter building &#xD;
management system (BMS) control.&#xD;
This research establishes a new benchmark for ultra-low-power, modular IAQ technology, &#xD;
coupled with a proven, scalable big data solution, accelerating the adoption of &#xD;
high-density IoT for healthier, smarter buildings.
Description: Ph.D.(Melit.)</description>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </item>
</rdf:RDF>

