Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform

Area of Science:

• Industrial Internet of Things (IIoT)
• Wireless Sensor Networks (WSNs)
• Network Communication Standards

Background:

• Sensor lifetime predictability is crucial for industrial wireless sensor networks (IWSNs) to ensure system availability, cost-efficiency, and safety.
• Dynamic network conditions and centralized deployments in IWSNs significantly impact sensor lifespan.
• The absence of energy-aware mechanisms compromises sensor lifetime predictability.

Purpose of the Study:

• To define a conceptual model for enhancing energy predictability and efficiency in IWSNs.
• To introduce the Predictive Energy-Aware Routing (PEAR) solution for assured network lifetime predictability.
• To validate PEAR's effectiveness within the constraints of the ISA100.11a standard and VR950 IIoT Gateway.

Main Methods:

• Development of a conceptual model for energy-aware mechanisms in IWSNs.
• Implementation of the Predictive Energy-Aware Routing (PEAR) solution.
• Integration of PEAR with the ISA100.11a communication standard and VR950 IIoT Gateway hardware.

Main Results:

• PEAR ensures predictable energy consumption across single and multiple network clusters.
• Intracluster energy balancing reduced overconsumption by 10.4 times after 210 routing changes.
• Intercluster energy balancing increased cluster lifetime by an average of 2.3 times (up to 3.2 times) and reduced average consumption by 23.6%.

Conclusions:

• The PEAR solution demonstrates feasibility and effectiveness in enhancing energy predictability and efficiency in IWSNs.
• PEAR's energy-aware routing and balancing capabilities are suitable for real-world industrial applications.
• The research validates the conceptual model's potential for improving IWSN operational longevity and reliability.