# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 6 Conclusions and Future Work
### Future Work  
While the proposed framework presents promising results, several avenues for improvement remain. A key challenge in deploying the proposed Edge AI framework in commercial smart home environments is interoperability with existing IoT ecosystems. Current smart home platforms like Google Home, Amazon Alexa, and Apple HomeKit rely on cloud-based processing, which may limit Edge AI integration. Future work should explore the following:  
* Custom firmware adaptations for compatibility with smart home hubs (e.g., OpenHAB, Home Assistant). * Edge-cloud hybrid models, where initial anomaly detection occurs on the device, and high-confidence anomalies are verified via cloud APIs. * Real-time API connectivity for direct integration with IoT security services, enabling automated responses to detected anomalies (e.g., activating security cameras or sending alerts). One limitation of the current implementation is the fixed retraining schedule for the LSTM Autoencoder. Future work should explore adaptive retraining techniques, where the model updates dynamically based on detected data drift rather than on a fixed timeline. This would minimize unnecessary retraining while maintaining high detection accuracy. Additionally, the hybrid anomaly detection approach (IF + LSTM-AE) could be further optimized by dynamically selecting the model based on the type of anomaly detected. A meta-learning framework could be introduced to determine the most effective model in real-time based on anomaly characteristics. To further enhance the suitability of the framework for low-power edge devices, future research should investigate the following:  
* Ultra-low-power AI hardware (e.g., TensorFlow Lite, TinyML, NVIDIA Jetson Nano optimizations). * Event-triggered inference, where the anomaly detection model only activates when sensor deviations surpass predefined thresholds, reducing unnecessary energy consumption. The emergence of transformer-based anomaly detection models (e.g., Time Series Transformer, Temporal Fusion Transformer) presents a potential alternative to LSTM-based approaches. Future studies should evaluate whether transformer models can outperform LSTM-AE in time-series anomaly detection while remaining computationally efficient for Edge AI applications. Additionally, self-supervised learning techniques could be explored to reduce dependency on labelled datasets, allowing the system to adapt to new patterns without requiring manually labelled training data. To validate the framework in real-world scenarios, long-term deployment in smart home environments should be conducted. Future work should achieve these objectives:  
* Implement continuous model monitoring to assess real-world false positive and false negative rates over time. * Investigate federated learning strategies, where multiple IoT devices collaborate to update models without sharing raw sensor data, enhancing privacy while improving detection performance across different smart homes. With the increasing efficiency of transformer-based models for time-series data, future research will explore whether lightweight transformer variants can enhance anomaly detection while maintaining computational efficiency on edge devices. Specifically, we should investigate the following:  
* Time Series Transformer (TST)--A variant designed for sequential anomaly detection, emphasizing capturing long-range dependencies. * Temporal Fusion Transformer (TFT)--A deep learning model that combines attention mechanisms with interpretable AI for time-series forecasting. * MobileViT and TinyBERT--Transformer-based architectures optimized for low-power devices. If optimized for edge inference, these models could potentially replace LSTM Autoencoders, providing better sequential anomaly detection with lower latency. By addressing these areas, future research can further enhance the robustness, adaptability, and efficiency of Edge AI-based anomaly detection, paving the way for intelligent, autonomous IoT anomaly detection solutions in smart homes and beyond.