A Hybrid CNN-BiLSTM Model for Minimizing Packet Loss in IoT-Enabled Wireless Sensor Networks

Sensors embedded in Wireless Sensor Networks (WSNs) form a foundation in the Internet of Things (IoT) architecture.Nonetheless, packet loss caused by unreliable communication, interference, and energy limitations continues to be a major issue.In this paper, we propose a Convolutional Neural Networks and Bidirectional Long Short Term Memory (CNN-BiLSTM) combined Deep Learning (DL) approach for packet loss minimization in IoT based WSNs.Our model uniquely integrates CNN to capture spatial features with a BiLSTM to capture temporal dependencies, allowing for more accurate inherent prediction of packet loss and intelligent routing in IoT-enabled WSNs.This hybrid design allows for the proposed model to outperform independent deep learning models and traditional routing protocols in both prediction accuracy and performance at the network level.Given the traditional models such as AODV and independent LSTM/CNN approaches.Proposed model has a packet loss reduction of 52%, an overall throughput improvement of 18.7%, and maintained low latency and energy consumption, contributing to the overall success of routing decisions in practical WSN scenarios.This makes the proposed hybrid model is highly suitable for the implementation in the real-time applications.