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- W4328007641 abstract "This paper 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 <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$K$</tex-math></inline-formula> -coverage, where given a set of <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$mathcal {M}$</tex-math></inline-formula> points, <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$K$</tex-math></inline-formula> 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 <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$K$</tex-math></inline-formula> -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 <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$81.04%$</tex-math></inline-formula> and <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$67.07%$</tex-math></inline-formula> of the optimal results computed by MILP." @default.
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- W4328007641 date "2023-01-01" @default.
- W4328007641 modified "2023-09-26" @default.
- W4328007641 title "Methods to Assign UAVs for K-Coverage and Recharging in IoT Networks" @default.
- W4328007641 doi "https://doi.org/10.1109/tmc.2023.3259461" @default.
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