Smart City Data Analytics Prototype
Processing 10M+ sensor records to validate a scalable smart city architecture
Processed over 10 million sensor records during testing
Reduced analytical reporting time by 82%
Validated a scalable architecture for future smart city initiatives
Overview
Smart city initiatives generate enormous quantities of data from IoT sensors monitoring traffic, air quality, energy consumption, and infrastructure conditions. The challenge is not collecting the data — it is processing, analysing, and visualising it in a way that produces actionable insight for urban planners. We designed and built a research prototype that validated the architecture needed to scale before the full system was commissioned.
The Challenge
A public-sector initiative sought to understand urban traffic, environmental conditions, and infrastructure usage through data-driven insights — but needed to validate the architectural approach before committing to full-scale deployment.
The Solution
Designed a research prototype combining IoT data collection, stream processing, real-time analytics, and interactive visualisation dashboards to evaluate feasibility at scale.
How We Approached It
Architecture Design
Evaluated stream processing approaches and selected Kafka-based architecture to handle the ingestion rates required at production scale.
Prototype Build
Built the full pipeline from sensor ingestion to analytics dashboard using real IoT data from pilot sensor deployments.
Load Testing
Simulated full-scale sensor volumes to identify bottlenecks and validate that the architecture held performance targets at 10M+ records.
Findings Report
Delivered a technical feasibility report with architecture recommendations, cost projections, and a phased implementation roadmap.
Key Features Built
Results & Impact
Processed over 10 million sensor records during testing
Reduced analytical reporting time by 82%
Validated a scalable architecture for future smart city initiatives
Technologies
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