Bioinspired Mobility-Aware Clustering Optimization in Flying Ad Hoc Sensor Network for Internet of Things: BIMAC-FASNET

Abstract

Flying ad hoc sensor network (FASNET) for Internet of Things (IoT) consists of multiple unmanned aerial vehicles (multi-UAVs) with high mobility, quick changes in topology, and diverse direction. The flying multi-UAVs were operated remotely by human beings or automatically by an onboard system. The applications of multi-UAVs are remote sensing, tracking, observing, and monitoring. It has a different nature compared to ordinary ad hoc network. The speed and diverse directions of multi-UAVs make it harder to route information in a desired way. Different issues may arise due to differences in unmanned aerial vehicle mobility, speed, diverse direction, and quick changes in topology. The researchers proposed conventional ad hoc routing protocols which has poor aspects for the flying ad hoc networks. They tried to tackle the issue by using the clustering approach that divides the network structure into multiple clusters, each with its own cluster head (CH). During the selection of CH and balance cluster formation, they consider only location awareness, neighborhood range, residual energy, and connection to the base station (BS) while ignoring the multi-UAVs distance, speed, direction, degree, and communication load. In this paper, we proposed bioinspired mobility-aware clustering optimization scheme based on bee intelligence foraging behavior for routing, considering relative mobility, residual energy, degree, and communication load during CH selection and balanced cluster formation. First, the clustering problem in network is formulated to dynamic optimization problem. An algorithm is designed based on bee intelligence, applied to select optimal UAVs CH and balanced cluster. The simulation results show that the proposed BIMAC-FASNET scheme performs better among existing clustering protocols in terms of link-connection lifetime, reaffiliation rate, communication load, number of UAVs per cluster, CH lifetime, and cluster formation time.

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

“…In 2020, Salam et al 22 have presented a “bio‐inspired mobility‐assisted clustering optimization approach” in which the foraging character of bees was utilized to perform the minimization of the multi‐objective constraints such as the transmission load, degree, residual energy, and relative mobility while optimal selection of CH and the formation of the balanced cluster. Initially, the clustering issue in the network was addressed through dynamic optimization.…”

“…Yet, it has reduced convergence speed and faces more computational complexity issues. Aware clustering 22 gains better link connection lifetime, cluster formation time, and reaffiliation rate. However, it has minimal operational efficacy and scalability rate.…”

Bio-inspired cluster–based optimal target identification using multiple unmanned aerial vehicles in smart precision agriculture

“…The honey bee clustering algorithm is applied for the optimal UAVs-CHs as in research. 31 In Figure 2, the images with L-tag represent the location of the affected crops area images. The UAVs-CHs communicate the information to the GS for further processing.…”

“…The grouping of UAVs in a network is performed with a clustering algorithm based on the honey bee as in research. 31 The flow chart in Figure 3 shows the process of optimal TAs identification based on the honey bee algorithm.…”

A Dynamic Clustering Mechanism With Load-Balancing for Flying Ad Hoc Networks

“…Previous studies have shown that these challenges lead to network instability and high energy and communication overheads [3]. Recent proposals aimed to tackle some of these challenges in isolation, such as trying to reduce energy consumption by predicting the trajectory of UAVs, or implementing bio-inspired routing protocols to improve the efficiency of data transmission [4]. Yet, such approaches fail to address the overall challenge of ensuring efficient data transmission in large scale FANETs, while ensuring low levels of delay, packet loss and power consumption.…”