Methodology for LoRa Gateway Placement Based on Bio-Inspired Algorithmsfor a Smart Campus in Wooded Area.

Area of Science:

• Electrical Engineering
• Computer Science
• Wireless Communication

Background:

• The Internet of Things (IoT) relies on efficient connectivity for smart devices.
• Low-Power Wide-Area Networks (LPWANs) offer a promising solution for IoT device communication.
• Effective network planning is crucial for maximizing LPWAN coverage and minimizing resource deployment.

Purpose of the Study:

• To empirically adjust and validate the UFPA propagation model for Amazonian urban-forest environments.
• To apply multi-objective optimization using Evolutionary Particle Swarm Optimization (EPSO) for LoRa network planning.
• To determine the optimal number and placement of gateways for comprehensive smart campus coverage.

Main Methods:

• Empirical data collection and statistical analysis in a mixed forest-building environment at UFPA.
• Application of an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic for multi-objective gateway placement.
• Monte Carlo simulations to evaluate the performance and scalability of the proposed optimization methodology.

Main Results:

• The adjusted UFPA propagation model accurately reflects signal behavior in the target environment.
• The EPSO methodology successfully optimized gateway placement, balancing coverage area and gateway count.
• A reception sensitivity of -108 dBm was identified as a key factor, leading to the determination that three gateways are optimal for covering the UFPA smart campus.

Conclusions:

• The proposed EPSO-based gateway placement optimization is a viable strategy for planning future LPWAN deployments.
• Empirical validation and advanced optimization techniques are essential for efficient IoT network design.
• The study provides a practical framework for deploying LPWANs in complex urban and natural environments.