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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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An Energy-Efficient LoRa Multi-Hop Protocol through Preamble Sampling for Remote Sensing.

Guus Leenders1, Gilles Callebaut1, Geoffrey Ottoy1

  • 1Dramco, ESAT-WaveCore, KU Leuven, 9000 Ghent, Belgium.

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Summary
This summary is machine-generated.

This study introduces a multi-hop protocol for Internet of Things (IoT) networks, enhancing remote environmental monitoring. The protocol significantly extends sensor range and node lifespan through prolonged preamble sampling and data aggregation, improving energy efficiency.

Keywords:
IoTLoRaenergy efficiencymulti-hop

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Area of Science:

  • Environmental monitoring
  • Wireless sensor networks
  • Internet of Things (IoT)

Background:

  • Existing low-power wide-area networks lack coverage in ultra-remote areas for environmental tracking.
  • Autonomous operation combining ultra-long-range connectivity and low energy consumption is crucial for remote monitoring.

Purpose of the Study:

  • To present a multi-hop protocol for extending sensor range in IoT networks while maintaining low energy consumption.
  • To improve the operational lifespan and energy efficiency of remote environmental monitoring nodes.

Main Methods:

  • Developed a multi-hop protocol featuring prolonged preamble sampling to maximize node sleep time.
  • Implemented data aggregation to minimize transmit energy per payload bit.
  • Conducted real-life experiments and large-scale simulations to validate the protocol.

Main Results:

  • Prolonged preamble sampling increased node lifespan to up to 4 years (from 2 days).
  • Data aggregation reduced energy consumption by up to 61%.
  • Network reliability demonstrated: 90% of nodes achieved at least 70% packet delivery ratio.

Conclusions:

  • The proposed multi-hop network protocol effectively extends sensor range and enhances energy efficiency for remote environmental monitoring.
  • The open-access release of the hardware, protocol stack, and simulation framework facilitates further research and development.