Adaptive Spatial Scheduling for Event Traffic in LoRaWAN Networks.

Area of Science:

• Computer Science
• Electrical Engineering
• Internet of Things

Background:

• Low-Power Wide-Area Networks (LoRaWANs) are crucial for Internet-of-Things (IoT) applications like environmental monitoring and disaster management.
• Event-triggered transmissions in LoRaWANs can cause frame collisions, degrading network performance and reliability despite existing interference reduction techniques.

Purpose of the Study:

• To propose a novel adaptive spatial scheduling algorithm to enhance system reliability for event-triggered traffic in LoRaWANs.
• To leverage network feedback and spatial traffic correlation for improved network performance.

Main Methods:

• Developed a new adaptive spatial scheduling algorithm based on learning automata and prior LoRaWAN scheduling advancements.
• Utilized network feedback information and traffic spatial correlation to optimize scheduling decisions.
• Conducted extensive simulations under diverse network conditions to evaluate the algorithm's performance.

Main Results:

• The proposed algorithm demonstrated a decrease of up to 30% in average frame delay compared to a previous event-triggered traffic scheduler.
• Achieved an order of magnitude lower frame delay compared to a baseline algorithm.
• Validated the algorithm's effectiveness in increasing network performance and reliability.

Conclusions:

• Adaptive spatial scheduling is crucial for improving LoRaWAN performance, particularly in scenarios with synchronized event-triggered transmissions.
• Leveraging spatial information in scheduling schemes significantly enhances network reliability for location-sensitive IoT applications.
• The proposed algorithm offers a promising solution for mitigating performance degradation caused by network congestion during critical events.