Project Goal

The main objective of SmartArena is to enhance the visitor experience at BK Arena by integrating a voice-activated AI assistant into the arena's web application. This AI assistant will provide real-time, personalized support to visitors, helping them navigate the arena, access information about events, amenities, and services, make reservations and orders, and receive personalized recommendations.

Problem Statement

Visitors to large venues like BK Arena often face challenges such as finding information about events, locating amenities, and making reservations or orders. Traditional methods of accessing this information can be cumbersome and time-consuming, leading to a suboptimal visitor experience.

Expected Outcome

By the end of this project, learners are expected to achieve the following outcomes:

1. Enhanced Visitor Engagement: Visitors will have an interactive, voice-activated AI assistant that can provide instant responses to their queries, improving their overall experience.
2. Improved Accessibility: The AI assistant will support multiple languages and voice interaction, making it accessible to a diverse audience.
3. Increased Operational Efficiency: The AI assistant will streamline processes such as information dissemination, navigation assistance, and order management, reducing the burden on arena staff.
4. Personalized Visitor Experience: By leveraging user data, the AI assistant will offer personalized recommendations and services, enhancing visitor satisfaction.
5. Seamless Integration: The AI assistant will be seamlessly integrated into the existing web application and edge infrastructure, ensuring reliable and scalable performance.

Project Goal

The main objective of SmartArena is to enhance the visitor experience at BK Arena by integrating a voice-activated AI assistant into the arena's web application. This AI assistant will provide real-time, personalized support to visitors, helping them navigate the arena, access information about events, amenities, and services, make reservations and orders, and receive personalized recommendations.

Project Activities

To successfully achieve the project goal, learners will need to complete the following tasks:

1. Requirements Gathering and Planning:

• Conduct meetings with stakeholders to confirm feature sets and design requirements for the AI assistant.
• Finalize the project scope and create a detailed project plan.

1. AI Model Selection and Setup:

• Choose appropriate TensorFlow Lite models for Natural Language Processing (NLP), Speech-to-Text (STT), and Text-to-Speech (TTS).
• Convert the chosen AI models to TensorFlow Lite format if necessary.

1. Custom Yocto Image Creation:

• Create a custom Yocto Kirkstone image for edge devices, including necessary layers and recipes for TensorFlow Lite, STT, and TTS capabilities.

1. Voice Recognition and TTS Integration:

• Integrate Speech-to-Text capabilities using models like DeepSpeech or Google Speech-to-Text API into the Yocto image.
• Integrate Text-to-Speech capabilities using engines like Google Text-to-Speech or open-source alternatives into the Yocto image.

1. Development Environment Setup:

• Set up development environments for containerization using Balena.
• Configure BalenaCloud and flash BalenaOS onto edge devices.

1. AI Assistant Development:

• Develop the AI assistant service to handle voice commands, process NLP tasks, and generate voice responses using TensorFlow Lite models.
• Ensure the AI assistant can interact with users via the web application.

1. MQTT Communication Setup:

• Implement an MQTT broker (e.g., Eclipse Mosquitto) for real-time communication between edge devices and the web application.
• Configure MQTT topics for task distribution, health monitoring, and data synchronization.

1. Edge Device Configuration:

• Deploy the custom Yocto image and AI models on edge devices.
• Set up and configure MQTT clients on edge devices for seamless communication.

1. Web Application Integration:

• Integrate the AI assistant into the existing web application to provide voice-activated features.
• Ensure the web application can send and receive data to and from the AI assistant via MQTT.

1. Security Implementation:

• Ensure all data transmissions between the web application and edge devices are encrypted using TLS/SSL.
• Implement JWT for secure user sessions and OAuth for secure API access control.

1. Testing and Validation:

• Conduct unit testing of the AI assistant service and voice capabilities.
• Perform integration testing to ensure smooth interaction between the AI assistant, web application, and edge devices.
• Validate the accuracy and responsiveness of the voice recognition and NLP capabilities.

1. Deployment and Monitoring:

• Prepare deployment environments on BalenaCloud.
• Deploy the containerized AI assistant on edge devices and integrate with the web application.
• Monitor deployment and ensure proper operation, addressing any issues that arise.

1. Training and Documentation:

• Create user guides and technical documentation for the AI assistant.
• Train BK Arena staff on using and maintaining the AI assistant.

1. Feedback and Iteration:

• Collect feedback from users and stakeholders.
• Iterate on the design and functionality based on feedback to improve the AI assistant.

Support & Mentorship

To ensure the successful completion of the SmartArena project, we will provide the following support mechanisms to learners:

1. Access to Architecture and Systems:

• System Documentation: Provide detailed documentation on the current system architecture, including components, workflows, and integration points.
• Architecture Overview: Offer an overview of how our systems currently work, including the web application, backend services, and edge devices.

1. Access to Yocto Environment:

• Yocto Project Access: Grant access to our existing Yocto environment, including build configurations and necessary recipes for creating custom images.
• Pre-configured Yocto Builds: Provide access to pre-configured Yocto builds that learners can use as a starting point for developing the AI assistant.

1. Access to Data:

• Sample Data Sets: Supply sample data sets for training and testing the AI models, including voice samples, text data, and user interaction logs.
• Real-time Data Access: Enable access to real-time data streams from the arena for testing the AI assistant's capabilities in a live environment.

Technical Support:

• Helpdesk: Establish a helpdesk for learners to raise technical issues and get prompt resolutions.
• Communication Channel: Create a dedicated communication channel (e.g., Slack) for real-time communication, peer support, and mentor interactions.