A Hybrid CNN-BiLSTM Model for Minimizing Packet Loss in IoT-Enabled Wireless Sensor Networks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".