Smart Mosquito-Nets: A Natural Approach to Controlling Malaria Using Larvicidal Plant Extracts and Internet of Things

Malaria mosquitoes, Anopheles, are well-known for carrying and spreading the malaria pathogens, known as Plasmodium. The public health challenge it brings has remained a global health challenge, of which the most robust control measures include mosquito-treated nets and electronic mosquito killer lamps. Due to health and cost problems, for example, in developing countries, these methods are not suitable for controlling mosquitoes and their plasmodiumic pathogens. In this study, we propose the use of two natural plant (e.g., Petiveria alliacea and Hyptis suavolens leaf) extracts that are cheap, ubiquitous, and effective for the control of mosquitoes, especially in temperate regions such as sub-Saharan Africa. On top of that, the study uses memory, non-locality, and fractal properties of fractal-fractional derivatives from compartmental modeling to capture susceptibility of infected persons, wider coverage, and heterogeneous breeding of mosquitoes, respectively, to evaluate the effectiveness of the two leaf extracts as natural larvicides against Anopheles mosquitoes. To measure the effectiveness of the two plant extracts in controlling malaria, this study develops a basic reproduction number model of Anopheles mosquitoes and evaluates the endemic points of the model. Comparing the results of larvicidal control with those of mosquito-treated nets, the proposed larvicidal control achieved 94.86% efficacy when applied alone and 96.83% efficacy when combined with mosquito nets, each outperforming mosquito nets (83.33%). These findings position compartmental fractal fractional-order modeling as an innovative tool for bioinformatic disease vector control. The study also presents a smart mosquito-net model where data collected from the host nodes on the performance of larvicides in mosquito and malaria control are transmitted via the Internet of Things infrastructure to the edge and cloud servers for computation, processing, artificial intelligence analytics, and policy-making.