An Efficient Framework for Sensor Data Collection by UAV Based on Clustering, Two-Fold Ant Colony Optimization and Node Grouping

Abstract

Unmanned Aerial Vehicles (UAVs) are a promising solution for sensor data collection (DC) in large-scale area. The challenge is to minimize the DC route, which will reduce UAV energy consumption and data latency. The novelty of this paper lies in its innovative approach to optimizing sensor data collection by UAVs. It combines Ant Colony Optimization (ACO) and K-means algorithms to establish an initial shortest route and introduces a unique method for grouping sensor nodes (SNs) along the route based on the UAV's footprint, reducing data latency and energy consumption for both UAV and sensors. First, an initial shortest route that traverses all SNs is established based on the ACO and the K-means algorithms. Second, we group the sensor nodes (SNs) along the initial route using the footprint of the UAV, so that the latter can collect the data of the group in one stop, instead of stopping at each SN. By sequencing the hovering locations, we obtain a (shorter) intermediate route. Third, we shorten this route even further, by applying ACO to the set of hovering locations of the intermediate route. The solution has been implemented fully in Python. The results show that the route length gets shorter progressively with each phase. To evaluate the performance of the solution objectively, we have compared it with four states of the art solutions. The results show vividly that the proposed solution produces a DC route 19.28% shorter than the shortest route produced by the four competitive solutions. Moreover, it demonstrates a remarkable improvement by retaining 44% of energy in most SNs while over 99% energy depletion observed in the five state-of-the-art competitive solutions.