ABSTRACT
Global pandemics such as Covid-19 have led to massive loss of human lives and strict lockdown measures worldwide. To return to a certain level of normalcy, community awareness on avoiding high population density areas is significantly important for infection prevention and control. With the availability of new telecommunication technologies, it is possible to provide highly informative population clustering data back to people using wireless aerial agents (WAAs) placed in a local area. Hence, a service architecture that allows users to access the localization of population clusters is proposed. Further, a convex hull-based clustering method, enhanced population clustering (E-PC), is proposed. This method refined the result of conventional clustering methods such as K-means and Gaussian mixture model (GMM). Moreover, the potential in E-PC to achieve the same or higher results compared to the original K-means and GMM, while consuming lesser data points, is demonstrated. On average, E-PC improved the cluster detection performance in both K-means and GMM by 18.93% under different environments such as remote, rural, suburban, and urban in terms of silhouette score. Further, E-PC allows a 15% data reduction which results in decreasing the computational cost and energy consumption of the WAAs.