Joint Cross-Layer Optimization Framework for 5G-Enabled Wireless Sensor Networks in Environmental Monitoring Systems

Abstract: Especially now that the deployment of 5G is on the increase, requirements for real-time environmental monitoring have put Wireless Sensor Networks (WSNs) in performance demands exceeding that of traditional protocol design. Current WSN architectures merely optimize the protocol layers (MAC, Network, Transport) after an isolated, parallel design on how the three layers behave. The operation prevents networks from reaping optimum advantages from dynamic, low-latency 5G environments. The main thrust of this research is to address the urgent issue of simultaneous and cross-layer optimization in WSNs by building a unified framework that couples up all protocol layers with real-time 5G feedback to maximize throughput and packet delivery ratios (PDR) sets outcomes. We fight the static, layer Isolated tuning shortcomings by introducing five new types of analytical models. Adaptive Layer-wise Graph Neural Network is the initial model, where ALGNN captures inter-layer dependencies through using shared embeddings that abstract cross-layer states. Second, MADRL-SRS (Multi-Agent Deep Reinforcement Learning with Shared Reward Shaping): Encourages cooperative policy learning across protocol layers and synchronized reward signals. Real-Time Cross-Layer Bayesian Optimization with Hierarchical Priors: Third. Fourth, Hybrid Edge-Cloud Feedback Loop using Delay-Aware Kalman Estimators (HECF-KF) corrects time-drift effects across state decisions for network jitter or 5G variability. Finally, Topology-Adaptive Cross-Layer Entropy Minimization- This would minimize entropy over protocols metrics while ensuring stable routes in dynamic network topologies. Together, these models amount to a completely adaptive optimization pipeline that responds in real time to emerging environmental triggers and 5G network conditions. Experimental simulations show improvements of up to 33% on latency reduction, 32% increase in PDR, and 52% reduction in entropy across the network layers. This brings a strong foundation for the next generation of intelligent environmental monitoring systems over 5G-enabled WSNs. Keywords: Cross-Layer Optimization, Wireless Sensor Networks, Environmental Monitoring, 5G Communication, Real-Time Adaptation, Process. Title: Joint Cross-Layer Optimization Framework for 5G-Enabled Wireless Sensor Networks in Environmental Monitoring Systems Author: Dr. Jaya Manoj Gadge International Journal of Novel Research in Engineering and Science ISSN 2394-7349 Vol. 12, Issue 2, September 2025 - February 2026 Page No: 24-30 Novelty Journals Website: www.noveltyjournals.com Published Date: 26-December-2025 DOI: https://doi.org/10.5281/zenodo.18060457 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/Joint%20Cross-Layer%20Optimization%20Framework-26122025-3.pdf