A Fairness-aware Coverage Algorithm for Drone Swarms

Drones have evolved over the past years to a level that they have become more efficient and, at the same time, have miniature sizes and advanced environment sensing capabilities. As a result, drone swarms are being applied in various applications nowadays, as they can sense and process information from the surrounding environment to achieve higher level of functionality. For example, drone swarms are employed in disaster control and large area monitoring to prevent catastrophic events such as wild fires. For drone swarms to operate properly, a critical aspect is their remote command and control capabilities. Since swarms can possibly consist of an increased number of drones, it is important that they hold a certain degree of autonomy. In the current work, a Coverage Path Planning (CPP) algorithm is proposed for unknown area exploration. The proposed algorithm discovers the Wireless Sensor Network (WSN) nodes within the area and, then, it collects their data under a data collection scheme being used. For evaluating the proposed algorithm, a simulated environment is developed to assess its effectiveness in terms of fairness in data collection from the WSN nodes. Fairness, in the current case, measures the percentage of data collected from the WSN nodes and is calculated multiple times during the experiments. Two coverage paths are considered here, the parallel lines and the spiral lines. The results showcase that the coverage path affects the fairness index while the parallel lines seem to provide more consistent data collection than the spiral, the latter increasing the fairness index.