Energy-efficient cluster-based unmanned aerial vehicle networks with deep learning-based scene classification model

International Journal of Communication Systems. 2021. Vol. 34. No. 8. Article e4786.

Pustokhina, I.V., Pustokhin D. A., Kumar Pareek P., Gupta D., Khanna A., Shankar K.

In present days, unmanned aerial vehicles (UAVs) have gained significant interest among researchers and academicians. The UAVs were found useful in diverse application areas, namely, intelligent transportation system, disaster management, surveillance, and wildlife monitoring. Clustering is a well-known energy-efficient technique, which elects a cluster head (CH) among other nodes. At the same time, scene classification from the high-resolution remote sensing images captured by UAV is also a major issue in the UAV networks. In order to resolve these problems, this paper projects novel energy-efficient cluster-based UAV networks with deep learning (DL)-based scene classification method. The proposed model involves a clustering with parameter tuned residual network (C-PTRN) model, which operates on two major phases such as cluster construction and scene classification. Initially, the UAVs are clustered using the type II fuzzy logic (T2FL) technique on the basis of residual energy, distance to nearby UAVs, and UAV degree. Next, the chosen CHs transmit the captured images to the base station (BS). At the second level, a DL-based ResNet50 technique is employed for scene classification. To tune the hyperparameters of the ResNet50 model, water wave optimization (WWO) algorithm is used. At last, kernel extreme learning machine (KELM) model is used to perform the scene classification process. In order to ensure the performance of the proposed method, a detailed set of simulations takes place under different dimensions. The obtained results ensured that the C-PTRN model has showcased supreme outcome with the maximum precision of 95.89%, recall of 98.91%, and F score of 96.54%. 

Keywords: clustering, deep learning, energy efficiency, unmanned aerial networks