African Security Studies (Political Science focus) | 18 July 2011
Low-Cost IoT Networks for Urban Slum Environmental Monitoring in Côte d'Ivoire
C, y, r, i, l, G, u, i, g, u, é, ,, J, u, l, i, e, n, S, o, g, u, é, ,, V, a, l, e, n, t, i, n, Z, a, n, b, é, ,, A, b, r, a, h, a, m, B, o, u, n, o, u
Abstract
Urban slums in Côte d'Ivoire face significant environmental challenges due to inadequate infrastructure and resource management. A mixed-method approach combining IoT sensor deployment, data collection through a distributed network, and predictive modelling using machine learning algorithms to optimise resource allocation. Initial deployments have shown that the IoT networks can monitor air quality with an accuracy of ±5% relative humidity (RH) in urban slums, providing actionable insights for environmental management. The proposed solution demonstrates potential for sustainable and cost-effective monitoring solutions in underserved urban environments. Scale-up pilot projects should be considered to validate the model's effectiveness on a larger scale before full-scale implementation. Urban slums, IoT networks, Environmental monitoring, Côte d'Ivoire, Machine learning 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.