African GIS Applications (Technology/Methodology) | 26 February 2007
Replicating IoT Solutions for Environmental Monitoring in Urban Slums of Kenya: A Cost-Efficient Approach
N, j, o, r, o, g, e, M, u, t, h, a, m, b, i, ,, O, l, u, o, c, h, M, a, s, u, d, i
Abstract
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 $\hat{\theta}=argmin<em>{\theta}\sum</em>i\ell(y<em>i,f</em>\theta(x<em>i))+\lambda\lVert\theta\rVert</em>2^2$, with performance evaluated using out-of-sample error.