A Nature-Inspired Approach to Energy-Efficient Relay Selection in Low-Power Wide-Area Networks (LPWAN)

Area of Science:

• Wireless communication networks
• Internet of Things (IoT)
• Algorithm design

Background:

• Low-Power Wide-Area Networks (LPWAN) face coverage blind spots, limiting their effectiveness.
• Extending network range and maintaining energy efficiency are critical challenges for LPWANs, especially for battery-powered IoT devices.

Purpose of the Study:

• To propose an innovative, energy-efficient, nature-inspired relay selection algorithm for LoRa-based LPWANs.
• To address challenges of poor signal range and coverage blind spots in LPWAN deployments.
• To enhance network energy efficiency and extend radio signal coverage.

Main Methods:

• A swarm behavior-inspired approach was utilized for relay localization in the network.
• The algorithm considers LoRa-specific factors like Spreading Factor and device energy budget.
• Simulation experiments were conducted under diverse multi-gateway network topologies with thousands of devices.

Main Results:

• The proposed algorithm effectively bridges communication gaps, reducing the impact of coverage blind spots.
• It significantly outperforms a reference method in preventing battery depletion of relays.
• The heuristic method demonstrates over twice the speed of the exact method with negligible accuracy loss (<2%).

Conclusions:

• The developed algorithm offers an effective solution for extending LPWAN coverage and improving energy efficiency.
• It is vital for enabling reliable communication for battery-powered IoT devices in challenging environments.
• The nature-inspired approach provides a scalable and efficient method for relay selection in large-scale LPWANs.