A framework for optimal agent deployment and opportunistic routing in flying Ad-Hoc networks for precision weather forecasting

Abstract

Drone based precision weather prediction and forecasting is a prevailing area of research in recent times. Modern flying ad-hoc network (FANET) leverages advanced low latency communication protocol between small flying drones and base stations. In this work, a FANET framework for efficient and precision weather prediction and forecasting that leverages ultra low latency opportunistic message transfer protocol (MTP) is proposed. An, edge empowered intelligent computing quasi-stationary sink nodes are employed to develop the FANET ecosystem further. Flying sensor nodes and edge computing quasi-stationary sink nodes are deployed in the target area in order to achieve optimal sensor coverage minimizing the cost of data transmission power thus ensuring long endurance operation of drones. An ensemble model is deployed in edge level for localized weather prediction. Opportunistic routing strategies are utilized for converge casting. The experimental results show the maximum of 0.98 message delivery ratio and a minimum of 820 ms latency is achieved through opportunistic MTP. An accuracy of 96.5% is achieved in predicting the temperature and humidity data metric.