Wake-Up Receiver Simulation Module for OMNeT++ With Realistic Environment Modeling

Wireless Sensor Network (WSN)s increasingly adopt Wake-up Receiver (WuRx) based architectures to enable ultra-low-power operation. However, existing simulation frameworks often overlook the combined impact of environmental variations, link quality fluctuations, and retransmission-induced energy costs, which can lead to inaccurate system-level evaluations. This paper presents an environment-aware WuRx simulation framework implemented in Objective Modular Network Testbed in C++ (OMNeT++), designed to improve the realism of link-quality and energy consumption modeling. The proposed framework captures the relationship between environmental conditions, specifically temperature and humidity, and Link Quality Metrics (LQM), including Received Signal Strength Indicator (RSSI) and Preamble Quality Indicator (PQI), and propagates these effects to Packet Error Rate (PER) and retransmission behavior. A data-driven model based on Artificial Neural Network (ANN) is trained on experimental measurements to estimate LQM, achieving low prediction errors, Root Mean Square Error (RMSE) of 0.635dBm for RSSI and 1.945 for PQI. The integration of the proposed model into the simulator enables dynamic adaptation of link quality to environmental conditions. The framework is further extended with an environment-aware transmission power adaptation scheme that actively selects the minimum power level needed to satisfy a 5% PER target, translating environmental modeling fidelity directly into measurable energy savings. Simulation results show that neglecting environmental variations can increase PER by up to 70% under fixed transmission schemes, leading to excessive retransmissions. In contrast, incorporating environment-aware adaptation reduces the transmitter’s energy consumption by up to 35.29% while preserving communication reliability. The proposed framework provides a reproducible and extensible tool for evaluating environmentally adaptive communication strategies and improving the fidelity of WuRx-based WSN simulations.