Wireless IoT sensors data collection reward maximization by leveraging multiple energy- and storage-constrained UAVs

We consider Internet of Things (IoT) sensors deployed inside an area to be monitored. Drones can be used to collect the data from the sensors, but they are constrained in energy and storage. Therefore, all drones need to select a subset of sensors whose data are the most relevant to be acquired, modeled by assigning a reward. We present an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) whose objective is to plan a set of drones' missions aimed at maximizing the overall reward from the collected data, and such that each individual drone's mission energy cost and total collected data are within the energy and storage limits, respectively. We optimally solve MDMP by proposing an Integer Linear Programming based algorithm. Since MDMP is NP-hard, we devise suboptimal algorithms for single- and multiple-drone scenarios. Finally, we thoroughly evaluate our algorithms on the basis of random generated synthetic data.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons .org /licenses /by-nc -nd /4 .0/).

Automated summary

This study discusses the deployment of IoT sensors in an area that needs to be monitored. Drones are used to collect data from the sensors, but they have energy and storage constraints. To maximize the overall reward from the collected data and ensure compliance with energy and storage limits, an optimization problem called Multiple-drone Data-collection Maximization Problem (MDMP) is proposed and solved using an Integer Linear Programming algorithm.