Methods to Assign UAVs for K-Coverage and Recharging in IoT Networks

This article studies a coverage problem in Internet of things (IoT) networks using unmanned aerial vehicles (UAVs) supported by solar-powered charging platforms. The problem at hand is to determine an assignment of UAVs to either a charging station or a monitoring point over a planning horizon. A key constraint is
$K$
-coverage, where given a set of
$\mathcal {M}$
points,
$K$
of these points must be monitored by a UAV in each time slot. In this respect, the paper aims to design UAVs assignment solutions that yield the longest
$K$
-coverage lifetime. We formulate a novel mixed integer linear program (MILP) to jointly optimize UAVs assignments over a given planning horizon. The problem is challenging as the energy level of charging platforms and UAVs are coupled across time slots. Moreover, the formulated MILP requires non-causal energy arrivals information at charging platforms. To this end, we outline a model predictive control (MPC) and a Monte Carlo tree search (MCTS) based solution that use non-causal energy arrivals information. The simulation results show that MPC and MCTS achieve approximately 81.04% and 67.07% of the optimal results computed by MILP.