Energy-Efficient and Fast Data Collection in UAV-Aided Wireless Sensor Networks for Hilly Terrains

Abstract

Energy-constrained sensor nodes are often deployed in remote, hilly, and hard-to-reach areas for civilian and military purposes. In such wireless sensor networks (WSNs), an unmanned aerial vehicle (UAV) can be used to collect data from the sensor nodes. Low-altitude UAVs can be utilized to reduce the energy consumption of WSNs by optimizing the data collection position. In this study, we designed an energy-efficient and fast data collection (EFDC) scheme in UAV-aided WSNs for hilly areas with the help of a UAV as a data mule. First, we proposed a central bias hybrid energy-efficient distributed clustering algorithm for grouping the sensors. Then, we applied a modified tabu search algorithm to optimize the UAV position for collecting data from a cluster. To achieve fast data collection, we developed the traveling salesman problem with the derived data collection positions and solved it by applying a modified genetic algorithm. Based on our simulation results, the proposed EFDC scheme outperforms the conventional ones in terms of energy consumption, scalability, control overhead, delay, and load balancing.

Keywords

• Clustering
• data collection
• genetic algorithm
• HEED
• unmanned aerial vehicle
• drone