Efficient Solution for Large-Scale IoT Applications with Proactive Edge-Cloud Publish/Subscribe Brokers Clustering.

Area of Science:

• Computer Science
• Networking
• Distributed Systems

Background:

• Large-scale Internet of Things (IoT) applications generate massive data volumes from geo-distributed devices.
• Designing communication systems for low latency and high scalability in IoT is challenging.

Purpose of the Study:

• To investigate a hierarchical Edge-Cloud publish/subscribe brokers model for efficient event notification delivery.
• To reduce data delivery latency and relay traffic in wide-scale IoT systems.

Main Methods:

• Implemented a two-tier routing scheme within the Edge-Cloud model.
• Utilized collaborative filtering for proactive clustering of geographically proximate edge brokers with similar topics.
• Devised a coordinative scheme for brokers to discover and bridge similar topic channels.

Main Results:

• The proposed model significantly reduces data delivery latency.
• Achieved approximately 7.77% less relay traffic among brokers compared to other Edge-Cloud approaches.
• Demonstrated high scalability for large-scale, delay-sensitive IoT applications.

Conclusions:

• The hierarchical Edge-Cloud publish/subscribe model effectively addresses challenges in large-scale IoT data delivery.
• The proactive clustering and coordinative schemes enhance efficiency and reduce latency.
• The model proves adept at supporting delay-sensitive IoT applications.