Mobisense

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 monolithic architecture ensures that components are tightly integrated while remaining modular enough to be independently modified and tested. 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 monolithic architecture enables modular development allowing different components to function independently while being tested ans maintained separately, with deployment handles as a single unified application.

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.

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.

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.

Conclusion

MobiSense demonstrates how combining a modern monolithic 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.