Efficient Deployment of Predictive Analytics in Edge Gateways: Fall Detection Scenario

1. Efficient Deployment of Predictive Analytics in Edge Gateways: Fall Detection Scenario David Sarabia-Jácome, Ignacio Lacalle, Carlos E. Palau, Manuel Esteve UniversitatPolitècnica de València (UPV) Valencia, España

2. PresentationOutline • Introduction • Motivation and Propose • Architecture • Fall Detection Scenario • Results • Conclusions • Future Research Directions

3. Introduction • AAL systems are cloud-based and rely on wearable devices and IoT nodes to provide innovative healthcare services. • Cloud-based limitations (high bandwidth occupancy, delay, low response time, security and privacy). • Edge computing overcomes these limitations by employing computer and network resources of devices places at the edge of the network to perform data processing (edge analytics). • Edge analytics is in its initial stage and needs other technologies to exploit its potential.

4. Motivation • Rule-based processing is performed at the edge, but it does not scale well, and it is unreliable. • Machine learning models are being employed in other areas (video applications) to perform a detection at the edge (IoT Gateways) • Machine Learning models deployment to the edge is scarcely studied in AAL scenario. • The proposed approaches are inefficient (lack of resources management, accuracy, precision, and high time to make decisions).

5. Propose • Designinganedgearchitecture to deploydeep learning models at the edge to reduce the time to detect a risky. • Implementing the architecture employing a container-based virtualization technique at the edge. • Evaluating the feasibility to use the container-based technique to support the deploy of deep learning models at the edge. • Evaluating the limitations of the deep learning models deployment at the edge in an AAL scenario.

6. Edge Gateway Architecture • Edge-basedarchitecture. • IoT Devices • Wearable sensor • IoT nodes • Edge Computing • IoT Gateway • Predictive Analytics • Cloud Computing • Deep Learning Training • Big Data Store

7. Edge Gateway Architecture • Communication: Supports communication protocols and Wireless technologies. • IoT Devices Handler: bi-directional communication using broker MQTT. • Stream Processing: data transformation operators (aggregation, filter, compression, fusion) • Temporal Database: store processed data and predictive results. Device Manager Temporal Database Stream Processing Predictive Analytics Resource Manager IoT Devices Handler Communication Zigbee Bluetooth 6LoWPAN Wifi

8. Edge Gateway Architecture • PredictiveAnalytics: Detects and predicts patterns from sensor data. DL models trained in the cloud. • Resource Manager: manages limited resource of the node (container-based). container engine facilitates the communication between containers. • Device Manager: to manage remotely and facilitate the deployment of predictive analytics models. Device Manager Temporal Database Stream Processing Predictive Analytics Resource Manager IoT Devices Handler Communication Zigbee Bluetooth 6LoWPAN Wifi

9. Fall Detection Use Case • Elders’ fall detection timely is relevant to enable elder’s independence and low risky quality of life. • Widely studied in the current literature. • Wearable-based approach is the most employed in the community. • Threshold-based methodology is the most employed to detect falls at the edge. • Threshold-based lacks accuracy and precision. • AI techniques (Machine Learning and Deep Learning) improve the accuracy and precision (to reduce false positive and true negative).

10. Fall Detection Use Case CLOUD LAYER • Wearable Belt Sensor using a tri-axial accelerometer (ADXL345). • Wearable is connected to the proposed edge gateway using wireless technology (Wi-Fi) and SSL/TLS. • The stream processing (aggregate and filter) preprocess data using a sliding-window (2999 timesteps). Big Data Analytics Docker Registry APP Developer Deploy Model Processing window size = 2999 Inference Model LSTM / GRU Storing MQTT Broker EDGE GATEWAY WearableBelt Sensor

11. Fall Detection Use Case CLOUD LAYER • DL model trained in the cloud, isolated on a Docker container and, deployed to the edge using Docker registry. • The 2999 timesteps are evaluated by the predictive analytics module (DL model). • Prediction (detect fall) is sent to storing and to MQTT broker to emit an alert to the caregiver smartphone. Big Data Analytics Docker Registry APP Developer Deploy Model Processing window size = 2999 Inference Model LSTM / GRU Storing EDGE GATEWAY MQTT Broker Wearable Belt Sensor

12. Implementation (Edge Gateway) EDGE GATEWAY • Raspberry Pi 2 model B. Raspbian Stretch OS, and Docker engine for ARMv7/ARMv8. • 4 containers: • DL model with TensorFlow and Python Flask app. • Mongo DB to store results. • Mosquitto MQTT broker to enable M2M communication. • Python app to process stream using Streams Library. Deep Learning Model MQTT Mongo DB Streams BINS LIBS BINS LIBS BINS LIBS BINS LIBS Container 4 Container 1 Container 2 Container 3 Docker Engine RaspbianStratch OS Raspberry PI

13. RESULTS • Three volunteers between 35 and 55 years old participated. • Evaluate the time to make a prediction at the edge. • Edge gateway performance is evaluated when it is stressed with more than an IoT wearable belt. • SisFall1 dataset was employed to simulated multiple data streams. 1. A. Sucerquia, J. López, and J. Vargas-Bonilla, “Sisfall: A fall and movement dataset,” Sensors, vol. 17, no. 1, p. 198, jan 2017.

14. RESULTS (Performance of PredictiveAnalytics) • Evaluate the time to make a prediction at the edge and make a comparison to similar and cloud-based approaches. V. Carlettiet at. “A smartphone-based system for detecting falls using anomaly detection” D. Yacchiremaet al. “Fall detection system for elderly people using IoT and big data” Y. Caoet al. “FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation”

15. RESULTS (Deep LearningEdge Gateway) • CPU, memory, and power consumption parameters are evaluated in the experimental test-bed. • Number of IoT wearable connected to the edge gateway is increased to evaluate its limitations.

16. RESULTS (Deep LearningEdge Gateway) • Inference time is increased when the number of wearables also do. • The edge gateway provides an acceptable time inference less than 15 seconds until 10 wearable simultaneous connected, but if the wearable is increased the inference time is not suitable.

17. Conclusion • The edgearchitectureproposeddemonstrates to be suitable to deployDL models at the edge. • Deploying DL models at the edge reduces the time to make a decision (inference time) in risky situations, and improves the state of the art by reducing in 8 seconds the time to make a decision to similar approaches and in 34 seconds to cloud-based approach. • Container-based virtualization is an efficient way to manage the limited resources at the edge and suitable for AAL scenarios. • The edge architecture proposed is capable of support 10 wearables with a degradation of the services (inference time) in 15 seconds.

18. Future Research Directions • A camera-based approach will be studied following the edge architecture proposedto evaluate its limitations and improvements. • An evaluation of the edge gateway performance to support other DL models (Convolutional Neural Network). • A large scale deployment will be developed to evaluate the edge architecture performance. • A complete system will be designed by including Big Data Architecture on the cloud and more security features (identity manager, certificates, among others).