IoT Data Quality Challenges Hamper ML Pipelines in Kenya's 2,500-Device Network

A developer managing over 2,500 IoT devices across Kenya encountered persistent data quality issues that prevented reliable machine learning processing. Key problems included sensor drift caused by temperature fluctuations, connectivity gaps that corrupted or lost data entirely, and schema changes that broke preprocessing pipelines. To address these, the team deployed local data caching, automated sanity checks via n8n workflows, and a schema versioning system, collectively reducing data errors by around 30%. Edge computing on Raspberry Pi devices, aided by lightweight LangChain algorithms, helped manage limited processing power and energy constraints. Environmental factors such as dust, humidity, and rain further degraded hardware performance, underscoring the unique infrastructure challenges faced in emerging-market IoT deployments.