Community-Centric Smart Mosquito Control for Urban Malaria Management in African Cities Using Plant-Based Larvicides and IoT Nets

Malaria remains a persistent public health burden in urban and peri-urban African communities, where rapid population growth, informal settlements, and limited access to healthcare infrastructure constrain the effectiveness of conventional control measures. Widely used interventions such as insecticide-treated nets and chemical larvicides are often costly, environmentally challenging, and unevenly distributed. This study proposes an integrated malaria control framework tailored to African urban settings, combining locally sourced plant-based larvicides with AI-enabled Internet of Things (IoT) mosquito-net systems. Two indigenous botanical larvicides— Petiveria alliacea and Hyptis suaveolens— are evaluated using a fractal-fractional compartmental model that captures memory effects, spatial heterogeneity, and mosquito population dynamics characteristic of African urban environments. Simulation results demonstrate that coupling plant-based larvicidal treatment with continuous, IoT-driven monitoring substantially reduces mosquito density compared with conventional standalone interventions. The proposed smart mosquito-net system enables real-time collection of entomological and environmental data, which are analysed to support early warning, targeted vector control, and adaptive intervention strategies. This approach facilitates predictive modelling, efficient resource allocation, and evidence-based decision-making for local health authorities. By integrating AI, IoT, and culturally and ecologically appropriate larvicides, the framework offers a cost-effective, sustainable, and scalable pathway for strengthening malaria control in African communities, supporting resilient urban health systems and long-term malaria reduction goals.