Enhancing Graph Routing Algorithm of Industrial Wireless Sensor Networks Using the Covariance-Matrix Adaptation...

Area of Science:

• Computer Science
• Electrical Engineering
• Network Engineering

Background:

• Industrial Internet of Things (IIoT) adoption necessitates efficient Industrial Wireless Sensor Networks (IWSNs).
• Current graph routing in IWSNs faces challenges with end-to-end transmission time, energy consumption balance, and communication reliability.
• Existing first-path available approaches in graph routing can lead to transmission conflicts and imbalanced energy use.

Purpose of the Study:

• To develop and evaluate advanced graph routing algorithms for IWSNs using Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).
• To optimize routing paths based on distance, residual energy, and end-to-end transmission time.
• To enhance overall network performance, energy efficiency, and communication reliability in IWSNs.

Main Methods:

• Implementation of three single-objective graph routing paths: Path based on Distance (PODis), Path based on residual Energy (POEng), and Path based on End-to-End transmission time (POE2E) using CMA-ES.
• Adaptation of CMA-ES for multi-objective path selection, termed Best Path of Graph Routing with a CMA-ES (BPGR-ES).
• Simulation-based evaluation using MATLAB to compare algorithm performance against existing state-of-the-art methods.

Main Results:

• The BPGR-ES algorithm demonstrated 87.53% more balanced energy consumption among sensor nodes compared to other algorithms.
• BPGR-ES achieved a data packet delivery rate of 99.86%, indicating highly reliable communication.
• The proposed CMA-ES based routing strategies effectively address the limitations of traditional first-path available routing.

Conclusions:

• The BPGR-ES algorithm significantly enhances energy consumption balance and communication reliability in IWSNs.
• CMA-ES is a viable and effective strategy for optimizing graph routing in demanding industrial network environments.
• The developed routing approach contributes to prolonging network lifetime and improving overall IWSN performance.