Mobisense - data analysis & visualization system

In today’s digital environment, data analysis is critical, especially when working with large datasets from diverse sources. To address this need, I developed MobiSense, a sophisticated system designed to capture signals from customer phones across multiple stores using embedded devices. The platform then leverages these signals to analyze customer activities and preferences, enabling the customization of advertisements and in-store experiences based on user interests.

What the System Does

MobiSense allows store operators to gather and process large datasets efficiently. Users can upload data files—such as CSV and PCAP formats—and receive comprehensive, timely analysis results. The platform’s modular microservices architecture ensures that each component can be independently modified, tested, and deployed. This provides flexibility, scalability, and maintainability, making it easy to adapt the system for future requirements or updates without impacting ongoing operations.

The system is designed to process customer activity data efficiently, providing actionable insights to help businesses understand customer behavior, optimize store layouts, and deliver personalized advertisements.

Technology & Architecture

MobiSense is built on a Django backend, which powers the core logic and APIs. Its microservices architecture allows different components to function independently, enabling modular development and deployment.

The platform is hosted on AWS infrastructure, utilizing services such as Lambda, EC2, RDS, and S3 to ensure seamless performance, reliability, and scalability. This setup allows the system to handle file uploads, process large datasets, and provide real-time feedback efficiently.

The system’s support for CSV and PCAP files ensures compatibility with various data collection methods, while automated processing pipelines reduce manual work and accelerate analysis.

Development & Challenges

Developing MobiSense involved several technical and operational challenges. One major challenge was handling large volumes of heterogeneous data from multiple stores and different devices, which required robust parsing, normalization, and processing pipelines to maintain accuracy and performance.

Another challenge was ensuring scalability and reliability. The system had to process multiple concurrent uploads and analysis requests without affecting performance, requiring careful architecture design, load testing, and cloud resource optimization.

Security and privacy were also crucial considerations. Since the platform deals with potentially sensitive customer signals, data protection and compliance were top priorities, including implementing secure storage, encryption, and access control.

Finally, coordinating the microservices architecture posed challenges in managing dependencies, service communication, and deployment pipelines. Ensuring that updates to one component did not disrupt others required comprehensive automated testing and monitoring strategies.

Development & Operations

In addition to building the platform, I managed the full hosting and monitoring of MobiSense on AWS. This included configuring cloud services for optimal performance and reliability, performing regular maintenance, and implementing automated testing pipelines. These tests run before and after updates to identify potential issues, prevent system failures, and ensure consistent operation.

The modular microservices approach allows updates or modifications to individual components without affecting the rest of the system. This flexibility ensures smooth operations even as new features are introduced or adjustments are made based on evolving business needs.

Key Benefits

MobiSense delivers several benefits to businesses:

• Efficient processing of large and complex datasets from multiple sources.
• A scalable architecture that can grow alongside expanding user and store networks.
• Reliable and stable operations ensured through proactive monitoring and automated testing.
• Flexibility to independently modify and deploy components without downtime, reducing operational risks.

Conclusion

MobiSense demonstrates how combining a modern microservices architecture with cloud infrastructure can create a highly reliable, maintainable, and scalable system. By capturing customer signals, processing them efficiently, and providing actionable insights in real time, the platform empowers businesses to personalize advertisements, optimize in-store experiences, and make data-driven decisions.

This project highlights the potential of embedded devices and data analysis to transform how businesses understand and engage with their customers, making operations smarter, more adaptive, and future-ready.