Elaris Computing Nexus
Empirical Evaluation of Cross Layer Optimization in Edge AI Driven Wireless Sensor Networks for Precision Industrial Monitoring
Elaris Computing Nexus
Received On : 06 November 2025
Revised On : 02 January 2026
Accepted On : 30 January 2026
Published On : 07 February 2026
Volume 02, 2026
Pages : 027-037
Abstract
The Industrial Internet of Things (IIoT) is growing rapidly and comes with tremendous challenges in latency, bandwidth, energy efficiency, etc. in centralized cloud architecture. To solve these bottlenecks a Cross-Layer Optimization (CLO) framework is proposed for Edge-AI integration of Wireless Sensor Networks (WSN). Unlike the vertical signaling pathway established by traditional decoupled network stack methodology, this pathway is created between the Medium Access Control (MAC) and Application layers. This integration of the structure enables the system to dynamically adjust Neural Network (NN) inference parameters such as model depth and bit-precision -- according to the quality of the link in real-time as well as the energy residuals at each node. The proposed software architecture uses an adaptive pruning and quantization engine to scale the computational intensity depending on the changing network conditions. Using a high fidelity simulation environment within the Python framework, the performance of the framework is tested alongside a scenario of Precision Industrial Monitoring. Experimental results show a 25 per cent reduction in end-to-end latency and a 15 per cent increase in network lifetime as compared to normal rows, non-optimized Edge-AI level based deployments. This research is a solid software engineering design blueprint for how to implement distributed intelligence in resource-constrained environments, with high reliability and real-time responsiveness for critical industrial infrastructures.
Keywords
Cross-Layer Optimization, Edge-AI, Wireless Sensor Networks (WSN), Industrial Internet of Things (IIoT), Adaptive Resource Management.
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Cite this Article
Mao Zedong, “Empirical Evaluation of Cross Layer Optimization in Edge AI Driven Wireless Sensor Networks for Precision Industrial Monitoring”, Elaris Computing Nexus, pp. 027-037, 2026, doi: 10.65148/ECN/2026003.
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© 2026 Mao Zedong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.