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.
Highlights
Flying ad hoc sensor network (FASNET) for Internet of ings (IoT) consists of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs) and ground segments (GSs) [1,2,3]. e flying nodes/sensors may play the role of a router or a sensor or both in a network. e characteristics of the sensor fly are different from the sensors in conventional wireless sensor network (WSN). e high mobility, communication range, and memory of the nodes are limited
Due to the dynamic nature of flying ad hoc sensor network, the UAVs may have the issue of mobility, energy, etc. e advantages of multi-UAV network are that it works in coordination and collaboration, which does not affect the overall performance of network
Bioinspired Mobility Prediction Clustering (BIMPC) [40] enhances the link and cluster head (CH) availability in terms of energy to perform stable cluster formation. is algorithm shows stability for dynamic structure of UAV networks with less communication overhead. e mobility considered in bioinspired mobility prediction clustering protocol is moderate, and UAVs in mission-oriented task are with very high mobility and required QoS communication
Summary
Flying ad hoc sensor network (FASNET) for Internet of ings (IoT) consists of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs) and ground segments (GSs) [1,2,3]. e flying nodes/sensors may play the role of a router or a sensor or both in a network. e characteristics of the sensor fly are different from the sensors in conventional wireless sensor network (WSN). e high mobility, communication range, and memory of the nodes are limited. Flying ad hoc sensor network (FASNET) for Internet of ings (IoT) consists of multiple flying nodes, i.e., unmanned aerial vehicles (UAVs) and ground segments (GSs) [1,2,3]. E characteristics of the sensor fly are different from the sensors in conventional wireless sensor network (WSN). E conventional MANET, VANET, and WSN routing protocols may not be applied directly to flying ad hoc sensor networks [4]. In early stages of flying ad hoc sensor networks, a single UAV was used to monitor, control, observe, and sense the objects or environment, but due to failure of a single UAV, no other UAV was available to maintain communication. Due to the dynamic nature of flying ad hoc sensor network, the UAVs may have the issue of mobility, energy, etc. In multiUAV system, the base station or controller or server receives information from root UAVs. e root UAVs have more
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