Sensify: Cloud Storage with AI Analytics Smart Sensing, Sharper Decisions

Abstract

In modern IoT ecosystems, the ability to efficiently collect, store, and analyze real-time sensor data is essential for enabling timely and data-driven decision-making. Traditional cloud database solutions often involve complex configurations, recurring maintenance burdens, and high deployment costs, making them unsuitable for lightweight, scalable, or educational IoT applications. To address these limitations, this project presents Sensify, a fully automated cloud-based data acquisition and analytics framework built using Google Sheets and Google Apps Script. The system enables seamless sensor data ingestion through a custom API, which accepts JSON-based payloads and appends the readings to a dynamically managed spreadsheet acting as a cloud storage layer. Automated routines periodically convert accumulated data into CSV format, transmit the file to external endpoints via HTTP POST, and reset the sheet for uninterrupted operation, thereby eliminating manual intervention and reducing backend infrastructure requirements.
To enhance the interpretability and decision-making capability of the stored data, AI-driven analytical modules are integrated to compute statistical summaries, detect anomalies, and identify emerging trends across environmental parameters such as temperature, humidity, light intensity, and air quality. These insights are visualized through correlation plots, time-series analyses, and distribution characteristics, enabling users to observe patterns, diagnose sensor issues, and monitor environmental behaviour effectively. The proposed system offers a low-cost, scalable, and highly accessible solution suitable for IoT monitoring, educational deployments, research applications, and lightweight cloud analytics. By combining real-time ingestion, automated processing, and intelligent analytics, the framework demonstrates a robust approach for building practical, maintenance-free IoT data pipelines that support rapid insights and reliable long-term operation.