Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks

Area of Science:

• Computer Science
• Network Engineering
• Industrial Automation

Background:

• Industry 4.0 demands low data access latency, a challenge for traditional centralized industrial networks.
• Centralized architectures may fail to meet stringent real-time data delivery requirements.

Purpose of the Study:

• To develop and evaluate a distributed data management method for optimizing data access latency in Industry 4.0.
• To identify and select a minimal set of network proxies for caching data to meet consumer latency constraints.

Main Methods:

• A novel method for selecting network proxies to cache data based on consumer needs and latency tolerance.
• Implementation and performance evaluation on a WSN430 IEEE 802.15.4-enabled wireless sensor network.
• Validation of a simulation model for large-scale and general network topology performance analysis.

Main Results:

• The proposed method guarantees average data access latency below specified thresholds.
• The approach significantly outperforms traditional centralized and existing distributed network solutions.
• Effective balancing of data access latency requirements and the number of deployed proxies.

Conclusions:

• Distributed data management with intelligent proxy caching is a viable solution for Industry 4.0 latency challenges.
• The developed method offers a scalable and efficient approach to enhance industrial network performance.
• This research provides a foundation for more responsive and robust industrial IoT systems.