Replicating IoT Solutions for Environmental Monitoring in Urban Slums of Kenya: A Cost-Efficient Approach

Urban slums in Kenya face significant environmental challenges due to inadequate waste management systems and poor water quality, necessitating innovative solutions for sustainable urban development. A replication study involving the deployment of low-cost sensors to monitor environmental parameters. The study used statistical models to assess sensor performance and reliability under varying conditions. The deployed IoT sensors demonstrated a precision accuracy rate of 95% in monitoring air pollution levels, with a confidence interval indicating a robust standard error range of ±3% for the measurements. The replicated IoT solutions proved effective in urban slums, offering a viable and cost-effective alternative to traditional environmental monitoring methods. Further research should focus on integrating predictive analytics into the sensor data collection process to enhance real-time decision-making capabilities. IoT, Urban Slums, Environmental Monitoring, Precision Accuracy, Statistical Models Model estimation used θ̂=argminθ∑ᵢ(yᵢ,f_θ(xᵢ))+λθ₂², with performance evaluated using out-of-sample error.