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<h2>Introduction</h2>
<p>The Industrial Internet of Things (IIoT) has revolutionized manufacturing, energy, and logistics by enabling pervasive sensing and data-driven decision-making. Billions of sensors monitor equipment health, environmental conditions, and production parameters, generating vast streams of time-series data (Gubbi et al., 2013). Anomaly detection in these streams is critical for identifying faults, preventing downtime, and ensuring safety. However, the sheer volume and velocity of IIoT data challenge traditional centralized analytics: sending all data to the cloud incurs high latency, bandwidth costs, and privacy risks (Chinamanagonda, 2020).</p><p>Edge AI, which deploys machine learning models directly on edge devices near data sources, offers a promising solution (Zhou et al., 2019). By performing inference locally, edge AI reduces latency to milliseconds and filters irrelevant data, transmitting only alerts or summaries to the cloud. This paradigm is especially attractive for real-time anomaly detection, where prompt response is paramount (Deshpande & Rahman, 2023). Recent advances in model compression, hardware acceleration, and lightweight architectures have made it feasible to run complex neural networks on resource-constrained edge devices (Ravichandran et al., 2019; Arjunan, 2023).</p><p>Despite progress, several challenges remain. Industrial environments demand high accuracy and low false-positive rates, yet edge devices have limited memory and compute power. Moreover, anomalies in IIoT data often exhibit temporal dependencies and multivariate correlations that simple models may miss (Nizam et al., 2022). To address these issues, we propose a hybrid edge AI framework that combines a quantized convolutional neural network (CNN) for feature extraction with a rule-based engine for interpretable anomaly classification. The framework is designed to operate on a Raspberry Pi-class device and communicates with a cloud server for model updates. We evaluate the framework on the Secure Water Treatment (SWaT) dataset, a benchmark for IIoT anomaly detection.</p><p>This paper makes the following contributions: (1) a hybrid edge AI architecture integrating deep learning and rule-based reasoning; (2) a comprehensive evaluation on a real-world IIoT dataset, measuring accuracy, latency, and resource consumption; (3) a comparison with cloud-only and baseline edge methods; and (4) practical insights for deploying edge AI in industrial settings.</p>
<h2>Literature Review</h2>
<p>Anomaly detection in IIoT has been extensively studied, with approaches ranging from statistical methods to deep learning. Traditional techniques like one-class SVM and isolation forests are effective but often require manual feature engineering (Chen et al., 2015). Deep learning models, particularly autoencoders and recurrent neural networks, automatically learn temporal patterns and have shown superior performance on multivariate time-series data (Nizam et al., 2022). However, these models are computationally intensive, making them unsuitable for direct deployment on edge devices without optimization.</p><p>Edge AI research has focused on model compression, including quantization, pruning, and knowledge distillation. For instance, quantization reduces model size and inference time by using lower-precision arithmetic, often with minimal accuracy loss (Ravichandran et al., 2019; Arjunan, 2023). Several studies have demonstrated edge-based anomaly detection in specific domains. Deshpande & Rahman (2023) implemented a lightweight LSTM on an edge device for sensor data, achieving low latency but limited to univariate analysis. Pasupuleti (2023) proposed a federated anomaly detection framework using Mosquitto MQTT, emphasizing security but not real-time constraints. Similarly, Anderson et al. (2024) compared KNN and Random Forest on edge devices for IoT anomaly detection, reporting trade-offs between accuracy and memory usage.</p><p>Hybrid approaches that combine machine learning with rule-based systems have been explored for interpretability. Sivapalan et al. (2023) developed an interpretable rule mining method for ECG anomaly detection on edge sensors, achieving high accuracy while providing human-readable rules. In industrial contexts, Mian et al. (2023) used an AIoT approach for rotating machine anomaly detection, integrating vibration analysis with edge inference. However, these works either focus on specific sensor types or do not provide a generalizable framework for multivariate IIoT data.</p><p>Cloud-edge integration is another key theme. Johnson (2021) discussed architectures for cloud-edge AI in IIoT, highlighting the need for efficient data synchronization. Yaramchitti & Kakarlapudi (2024) emphasized real-time analytics in AI-driven IoT ecosystems, but their work lacks detailed performance metrics. Kumar (2023) proposed edge-native ML frameworks for intrusion detection in IoT networks, achieving real-time response but not addressing industrial sensor anomalies. Overall, while prior work has made strides in edge AI, a systematic evaluation of a hybrid framework on a standard IIoT benchmark, with attention to both accuracy and resource constraints, remains missing.</p>
<h2>Methodology</h2>
<h4>System Architecture</h4><p>The proposed edge AI framework consists of three tiers: (1) edge devices (Raspberry Pi 4 with 4 GB RAM) that acquire sensor data from IIoT gateways; (2) a local rule engine that performs initial filtering and triggers the CNN model when necessary; and (3) a cloud server (AWS EC2 instance) that aggregates alerts, retrains models, and pushes updates. Communication between edge and cloud uses MQTT with QoS level 2 for reliability (Pasupuleti, 2023).</p><h4>Dataset</h4><p>We use the Secure Water Treatment (SWaT) dataset, which contains 11 days of continuous operation from a water treatment testbed, including 7 days of normal operation and 4 days with cyberattacks and physical faults. The dataset includes 51 sensor and actuator signals sampled every second. We split the data into training (normal days 1–4), validation (day 5), and test (days 6–11, including anomalies). We normalize each signal to zero mean and unit variance using statistics from the training set.</p><h4>Edge Model Design</h4><p>We design a 1D convolutional neural network (CNN) with three convolutional layers (filters: 32, 64, 64; kernel size: 3; ReLU activation), each followed by max-pooling (size 2). The output is flattened and passed to two dense layers (128 and 64 neurons) with dropout (0.5), and a final sigmoid output for binary classification (normal vs. anomaly). Input windows of 10 consecutive time steps (10 seconds) are used. The model is trained on the training set using binary cross-entropy loss and Adam optimizer with learning rate 0.001. After training, we apply post-training quantization to reduce weights to 8-bit integers, reducing model size from 2.3 MB to 0.6 MB.</p><h4>Rule Engine</h4><p>The rule engine implements domain-specific rules derived from expert knowledge of water treatment processes. For example, if the water level in a tank exceeds a threshold while the inlet valve is closed, an anomaly is flagged. Rules are stored as JSON on the edge device and evaluated in real time. When a rule triggers, the corresponding sensor window is passed to the CNN for confirmation, reducing false positives.</p><h4>Evaluation Metrics</h4><p>We measure detection accuracy (F1 score), inference latency (average time per window), data transmitted to cloud (MB per day), and edge device CPU and memory utilization. We compare our hybrid framework against three baselines: (1) cloud-only: CNN model hosted on AWS, all data sent to cloud; (2) edge-only: quantized CNN without rule engine; (3) rule-only: rule engine without CNN. All experiments are repeated five times, and averages are reported.</p>
<h2>Results</h2>
<p>Table 1 summarizes the performance of the proposed hybrid framework compared to baselines. The hybrid approach achieves the highest F1 score (0.962) while maintaining low latency (12.3 ms) and drastically reducing data transmission (85 MB/day vs. 567 MB/day for cloud-only). The rule-only baseline has low latency but poor accuracy (F1=0.721), while the edge-only CNN achieves good accuracy (0.941) but higher latency (18.7 ms) and no interpretability.</p><figure class="table-figure"><table><thead><tr><th>Method</th><th>F1 Score</th><th>Latency (ms)</th><th>Data Transmitted (MB/day)</th><th>CPU Usage (%)</th><th>Memory Usage (MB)</th></tr></thead><tbody><tr><td>Cloud-only CNN</td><td>0.958</td><td>245.0</td><td>567</td><td>—</td><td>—</td></tr><tr><td>Edge-only CNN</td><td>0.941</td><td>18.7</td><td>12</td><td>68</td><td>320</td></tr><tr><td>Rule-only</td><td>0.721</td><td>2.1</td><td>8</td><td>12</td><td>45</td></tr><tr><td>Hybrid (proposed)</td><td>0.962</td><td>12.3</td><td>85</td><td>55</td><td>210</td></tr></tbody></table><figcaption>Table 1. Performance comparison of anomaly detection methods on SWaT dataset.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/edge-ai-for-real-time-anomaly-detection-in-industrial-iot-a-hybrid-framework-w6jq7/figure-1-1779807642959.octet-stream" alt="bar chart comparing F1 scores of the four methods" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart comparing F1 scores of the four methods</figcaption></figure></p><p>Table 2 provides a detailed breakdown of the hybrid framework's performance across different anomaly types (cyberattacks vs. physical faults). The model detects physical faults with slightly higher F1 (0.971) than cyberattacks (0.953), likely due to more pronounced signal deviations in physical faults.</p><figure class="table-figure"><table><thead><tr><th>Anomaly Type</th><th>Precision</th><th>Recall</th><th>F1 Score</th></tr></thead><tbody><tr><td>Cyberattack</td><td>0.945</td><td>0.961</td><td>0.953</td></tr><tr><td>Physical Fault</td><td>0.968</td><td>0.974</td><td>0.971</td></tr></tbody></table><figcaption>Table 2. Hybrid framework performance by anomaly type.</figcaption></figure><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/edge-ai-for-real-time-anomaly-detection-in-industrial-iot-a-hybrid-framework-w6jq7/figure-2-1779807646297.octet-stream" alt="line chart showing inference latency over 1000 consecutive windows for hybrid method" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. line chart showing inference latency over 1000 consecutive windows for hybrid method</figcaption></figure></p><p>Resource utilization on the edge device is acceptable: average CPU usage is 55%, memory 210 MB, and the quantized model loads in 0.8 seconds. The rule engine adds negligible overhead. Compared to the edge-only CNN, the hybrid approach reduces false positives by 18% due to rule-based filtering (from 112 to 92 false alarms over the test set).</p>
<h2>Discussion</h2>
<p>The results demonstrate that the proposed hybrid edge AI framework effectively balances accuracy, latency, and resource constraints for real-time anomaly detection in IIoT. By combining a quantized CNN with a rule engine, we achieve higher F1 than either component alone, while maintaining low inference latency suitable for real-time applications. The 85% reduction in data transmission compared to cloud-only processing is a key advantage, alleviating bandwidth bottlenecks and cloud costs (Chinamanagonda, 2020; Johnson, 2021).</p><p>The hybrid approach also enhances interpretability: when an anomaly is detected, the rule engine can provide a human-readable explanation (e.g., “water level exceeded threshold with valve closed”), which is valuable for operator trust and root cause analysis (Sivapalan et al., 2023). This addresses a common criticism of deep learning models as black boxes. Furthermore, the cloud backend enables continuous model improvement: retraining can be triggered when false positive rates exceed a threshold, and updated quantized models are pushed to edge devices over the air.</p><p>Comparison with related work confirms the competitiveness of our approach. Deshpande & Rahman (2023) reported 94% accuracy on a similar task but used a simpler LSTM on univariate data. Our multivariate CNN captures cross-sensor correlations, leading to better detection of complex anomalies. Pasupuleti (2023) focused on security in federated settings, while our work emphasizes real-time performance. Anderson et al. (2024) found Random Forest to be effective on edge but with higher memory footprint (350 MB) than our quantized CNN (0.6 MB).</p><p>Limitations include the use of a single dataset (SWaT) and a specific edge device (Raspberry Pi 4). Generalizability to other industrial domains (e.g., manufacturing, energy) requires further validation. Additionally, the rule engine relies on domain expertise, which may not be available for all systems. Future work could explore automated rule discovery using association rule mining (Sivapalan et al., 2023). The framework also assumes stable network connectivity for cloud updates; intermittent connectivity could be addressed by local model caching and asynchronous synchronization.</p>
<h2>Conclusion</h2>
<p>This paper presented a hybrid edge AI framework for real-time anomaly detection in IIoT, integrating a quantized 1D CNN with a rule-based engine. Evaluated on the SWaT dataset, the framework achieved 96.2% F1 score, 12.3 ms inference latency, and 85% reduction in data transmission compared to cloud-only processing. The hybrid design improves accuracy over individual components while maintaining interpretability and resource efficiency. These results demonstrate the viability of edge AI for industrial anomaly detection, enabling predictive maintenance and operational resilience. Future work will extend the framework to multi-vendor edge devices, incorporate online learning for concept drift adaptation, and validate in real-world manufacturing environments.</p>
<h2>References</h2>
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