Introduction
Overview of Smart Homes and the Increasing Role of Edge AI in Anomaly Detection
The rise of Edge AI-powered smart home IoT systems has revolutionized residential spaces by enabling automation, security monitoring, and energy management. However, as these systems grow in complexity, they require efficient Edge AI-driven anomaly detection mechanisms to identify irregularities such as security breaches, abnormal energy consumption, and device failures. By processing data locally, Edge AI ensures real-time responsiveness while preserving privacy and reducing reliance on cloud-based solutions.
Edge AI offers an innovative solution by enabling on-device processing, allowing smart home devices to detect anomalies locally without depending on cloud-based systems. This ensures low-latency responses, reduced privacy risks, and optimized bandwidth usage, making Edge AI an essential component of modern smart home architectures.
Challenges of Traditional Cloud-Based Detection
While cloud-based anomaly detection is widely used, it introduces several inefficiencies:
- Latency Issues – Cloud-based processing requires data transmission, delaying real-time responses to anomalies.
- Privacy Risks – Sensitive data, such as energy consumption and motion sensor readings, is sent to external servers, increasing vulnerability to cyberattacks.
- Network Dependency – Smart home devices require continuous connectivity for cloud-based analysis, leading to operational risks during network failures.
How Edge AI Enables Real-Time, On-Device Processing for Anomaly Detection
By implementing Edge AI models, smart home devices can locally process sensor data to detect anomalies in real-time. This offers several advantages:
- Immediate detection of security threats, equipment failures, or energy inefficiencies.
- Reduced reliance on cloud-based services, ensuring privacy and low bandwidth consumption.
- Efficient power management, allowing anomaly detection on embedded devices without excessive energy usage.
This paper proposes an Edge AI-based anomaly detection framework, integrating Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) to enhance real-time anomaly identification while balancing accuracy and computational efficiency.
Understanding Edge AI for Anomaly Detection
Definition: What Is Edge AI, and How Does It Differ from Cloud-Based AI?
Edge AI refers to artificial intelligence executed locally on IoT devices, rather than relying on cloud servers. Unlike traditional cloud-based AI, Edge AI processes sensor data on the edge device itself, ensuring real-time analytics and decision-making.
Key Advantages of Edge AI in Smart Home Applications
Deploying Edge AI for anomaly detection in smart homes provides multiple benefits:
- Faster Inference – Immediate on-device analysis minimizes delays.
- Enhanced Privacy – Data remains within the home ecosystem, reducing exposure to external threats.
- Lower Bandwidth Usage – Eliminates excessive data transmission, optimizing smart home network efficiency.
Table: Comparison of Cloud-Based and Edge AI Anomaly Detection
| Feature | Cloud-Based AI | Edge AI-Based Detection |
|---|---|---|
| Latency | High (network-dependent) | Low (on-device processing) |
| Privacy | Requires external data transmission | Local execution with minimal exposure |
| Bandwidth Usage | Continuous data uploads required | Optimized, only necessary transmissions |
| Power Consumption | High due to cloud processing | Lower, optimized for embedded devices |
Primary Use Cases: Security Monitoring, Energy Optimization, Predictive Maintenance
- Security Monitoring – Detects intrusions, unauthorized access, and suspicious activity.
- Energy Optimization – Identifies anomalous power consumption to improve smart home efficiency.
- Predictive Maintenance – Predicts device failures before they occur, preventing costly repairs.
Proposed Edge AI-Based Anomaly Detection Framework
Hybrid Detection System Integrating Isolation Forest and LSTM-AE
This research presents a hybrid anomaly detection approach combining:
- Isolation Forest (IF) – A tree-based unsupervised model that quickly flags anomalies in sensor data.
- LSTM Autoencoder (LSTM-AE) – A deep learning model trained to recognize normal behavioral patterns in time-series data, detecting anomalies based on reconstruction errors.
This integration ensures high detection accuracy while maintaining low computational overhead, making it feasible for resource-constrained embedded devices.
Overview of the Multi-Layered Architecture
The system architecture consists of four distinct layers:
- IoT Sensor Layer – Collects temperature, motion, and energy consumption data.
- Edge Processing Layer – Executes machine learning-based anomaly detection in real-time.
- Optional Cloud Layer – Allows model refinements via federated learning for improved accuracy.
- User Alerting System – Provides real-time notifications and automated responses to detected anomalies.
Table: Layered Edge AI Architecture for Anomaly Detection
| Layer | Functionality | Key Components |
|---|---|---|
| IoT Sensor Layer | Data collection | Temperature sensors, motion detectors, energy meters |
| Edge Processing Layer | Real-time detection | Isolation Forest, LSTM Autoencoder |
| Cloud Layer (Optional) | Long-term model updates | Federated learning, cloud API |
| User Alerting System | Notifications & monitoring | Mobile dashboard, web alerts |
Optimization Strategies: Quantization, Model Compression, and Federated Learning
To improve real-time performance, optimization techniques include:
- Quantization – Reducing model precision for faster execution.
- Model Compression – Streamlining architecture for lower memory usage.
- Federated Learning – Enabling collaborative model refinements without cloud dependency.
How Edge AI Processes Smart Home Data
IoT Sensor Data Collection: Temperature, Motion, and Energy Usage Tracking
Smart homes rely on IoT sensors to continuously monitor environmental conditions. These devices collect temperature fluctuations, motion activity, and energy usage patterns, allowing anomaly detection through Edge AI models.
On-Device Machine Learning Execution Instead of Cloud Dependence
By processing sensor data locally, Edge AI minimizes cloud dependency, ensuring instant anomaly detection, privacy preservation, and low-bandwidth operation.
Experimental Setup and Methodology
Dataset Preparation: Synthetic Smart Home Sensor Data and Real-World IoT Datasets
To evaluate the effectiveness of the Edge AI-based anomaly detection framework, the study utilized both synthetic and real-world IoT datasets. These datasets were chosen to simulate realistic smart home environments and ensure robust anomaly detection testing.
Synthetic Dataset Generation
A synthetic IoT dataset was designed to reflect typical smart home sensor readings. This dataset consisted of 100,000 sensor data points across three core metrics:
- Temperature (°C) – Simulated with a normal distribution centered around standard indoor temperatures.
- Motion Detection (Binary: 0/1) – Representing expected human movement patterns in a home setting.
- Energy Usage (Wattage) – Generated to include baseline consumption levels and occasional spikes to emulate high-power events.
To simulate real-world anomalies, 3% of the data points were artificially manipulated, introducing unexpected sensor deviations such as sudden temperature increases, abnormal motion patterns, and excessive energy consumption surges.
Real-World IoT Dataset Evaluation
In addition to synthetic data, the framework was tested on a subset of the CASAS smart home dataset. This dataset includes multivariate sensor streams from actual smart home environments such as motion detection and temperature tracking over extended time periods.
The real-world dataset validation was crucial for demonstrating the generalizability of Edge AI-based anomaly detection beyond controlled simulations.
Implementation Details: Model Architecture, Hyperparameters, and Edge Deployment Platforms
Hybrid Edge AI Model Selection
The proposed framework integrates two machine learning models to optimize anomaly detection accuracy while ensuring low computational overhead:
- Isolation Forest (IF) – A statistical anomaly detection algorithm effective for rapid, low-power anomaly flagging.
- Long Short-Term Memory Autoencoder (LSTM-AE) – A deep learning approach trained to recognize normal sensor behavior over time, flagging anomalies based on reconstruction errors.
By combining these models, the framework achieves high detection accuracy while maintaining real-time responsiveness for embedded smart home environments.
Edge Deployment on IoT Hardware
The anomaly detection system was deployed and evaluated on three key edge devices:
- Raspberry Pi 4 – A cost-effective Edge AI processing device suitable for lightweight deployments.
- NVIDIA Jetson Nano – Optimized for deep learning inference, ensuring efficient anomaly detection with LSTM-AE.
- ESP32 Microcontroller – A low-power processor used for simplified anomaly detection tasks in constrained environments.
To optimize execution speed, techniques such as model quantization, pruning, and adaptive learning were implemented to reduce computational overhead without sacrificing accuracy.
Hyperparameter Selection and Optimization
A grid search approach was employed to optimize model parameters, balancing speed and precision. The finalized hyperparameters were:
Table: Hyperparameter Configuration for Anomaly Detection Models
| Model | Hyperparameters |
|---|---|
| Isolation Forest | Contamination = 0.03, n_estimators = 100 |
| LSTM Autoencoder | 3 LSTM layers, 32 units per layer, activation = ReLU, epochs = 50, batch size = 32 |
Evaluation Metrics: Accuracy, Inference Speed, Memory Footprint, Energy Consumption
To assess the performance of the proposed Edge AI framework, the following key evaluation metrics were used:
- Detection Accuracy – Comparing true anomaly identification rates for IF, LSTM-AE, and hybrid models.
- Inference Speed – Measuring real-time processing latency on edge devices.
- Memory Footprint – Evaluating RAM usage to determine feasibility for low-power smart home devices.
- Energy Consumption – Comparing power usage across cloud-based vs. Edge AI-based anomaly detection.
Table: Computational Efficiency Comparison of IF vs. LSTM-AE
| Metric | Isolation Forest | LSTM Autoencoder | Hybrid (IF + LSTM-AE) |
|---|---|---|---|
| Accuracy (%) | 89.4 | 93.6 | 92.8 |
| Inference Time (ms) | 22 | 35 | 30 |
| Memory Usage (MB) | 5 | 50 | 25 |
| Power Consumption (W) | 2.8 | 4.2 | 3.5 |
Results: Performance Benchmarks and Comparative Analysis
Detection Accuracy Comparison: IF vs. LSTM-AE vs. Hybrid Model
The LSTM Autoencoder model achieved higher detection accuracy (93.6%) than Isolation Forest (89.4%), proving more effective for time-series anomaly detection. However, IF consumed 30% less power, making it preferable for battery-operated smart home devices.
Inference Latency Improvements with Edge AI vs. Cloud-Based Processing
Deploying Edge AI models reduced anomaly detection latency from 150 ms (cloud-based) to sub-50 ms (on-device processing), ensuring real-time responsiveness.
Energy Efficiency and Resource Utilization Analysis
- Quantization optimized LSTM-AE inference time by 76%, improving execution efficiency.
- Event-triggered execution reduced power usage by 35%, improving IoT device sustainability.
Challenges and Future Directions
Key Hurdles in Real-Time Edge AI Deployment
- Model accuracy vs. computational efficiency trade-off remains a challenge.
- Energy constraints limit continuous anomaly monitoring on battery-powered devices.
Emerging Trends in Edge AI-Based Anomaly Detection
- Federated learning allows collaborative model updates without exposing raw data.
- Transformer-based models may improve anomaly detection efficiency while maintaining low power consumption.
Conclusion
Summary of Edge AI’s Role in Enhancing Real-Time Anomaly Detection in Smart Homes
Edge AI enables real-time, privacy-preserving anomaly detection, minimizing cloud dependency and latency issues.
Trade-Offs Between Accuracy, Speed, and Efficiency in Edge AI Model Deployment
The hybrid IF + LSTM-AE model balances speed and accuracy, proving effective for smart home anomaly detection.
Future Innovations in Edge AI-Based Anomaly Detection
- Self-supervised learning for adaptive anomaly monitoring.
- Lightweight transformers for enhanced sequential anomaly detection at the edge.
Reference: Reis, M.J.C.S.; Serôdio, C. Edge AI for Real-Time Anomaly Detection in Smart Homes. Future Internet 2025, 17, 179. https://doi.org/10.3390/fi17040179
License: This work is licensed under a Creative Commons Attribution (CC BY) 4.0 license. You can find the licensing details at https://creativecommons.org/licenses/by/4.0/.
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