Trust- and energy-aware cluster head selection in a UAV-based wireless sensor network using Fit-FCM

Due to the emerging applications of unmanned aerial vehicle (UAV)-based technologies, UAV-based wireless communication techniques, such as UAV-based coverage extension, UAV-based data distribution and UAV-based relaying, are being used to collect information in different processing sectors. In particular, UAV-based data gathering and distribution can be executed using a UAV-based wireless sensor network (WSN). In UAV-based WSNs, the cluster heads (CHs) serve important functions in both data gathering and data transfer between members and UAVs. Due to the important functions of CHs, many attackers attempt hack CH nodes. Typically, a hacked CH utilizes excess energy compared to a normal CH since it performs the CH function of delivering information to a sink greedily. To resolve this, this paper develops a novel UAV-based CH selection (CHS) algorithm for use in WSNs, namely, the Fitness-based Fuzzy C-Means (Fit-FCM) algorithm, which gathers the remaining energy of nodes and utilizes the energy for selecting new CHs while neglecting the nodes with the lowest energy. Initially, UAV-based WSN nodes are simulated, and then, CHS is performed using the developed Fit-FCM algorithm, in which fitness functions such as energy, distance and trust are considered. After CHS, information is transmitted through the selected CHs. Experimental results demonstrate that the developed Fit-FCM achieves better results in terms of distance, energy, and trust, with values of 51.9076 m, 0.4882 J, and 0.536439, respectively.

Automated summary

This paper develops a novel UAV-based CH selection algorithm, Fit-FCM, for use in WSNs, considering energy, distance, and trust functions. The algorithm achieves better results in CH selection, effectively addressing issues caused by hacked CH nodes.