DCM-D2X: An Effective Communication Mobility Model for Decentralized Cooperative Multi-Layer Drone to Everything

Abstract

Communication among drones and diverse devices, known as D2X (Drone-to-Everything) communication, faces challenges within traditional drone setups, including routing complexities, interference issues, and susceptibility to single points of failure. These shortcomings hinder network scalability and overall performance. This paper introduces the decentralized cooperative multi-layer drone to everything (DCM-D2X) architecture with integrated hybrid bioinspired grey wolf optimization-waypoint tracking (GWO-WPT) mobility model. DCM-D2X architecture incorporates GWO-WPT mobility patterns that are explicitly integrated for decentralized cooperative multi-layer for effective communication in D2X environments. This model is purposefully integrated and designed to enhance communication efficacy in D2X environments by mitigating single points of failure, optimizing resource allocation, managing interference, and improving cooperative routing. Extensive simulations have been conducted using the optimized link state routing protocol (OLSR) within the network simulator (NS2) to evaluate the proposed architecture and mobility model. Performance metrics, including network diameter, average clustering coefficient, energy consumption, delay, throughput, and packet delivery ratio (PDR), have been assessed. Compared to the latest literature, the proposed model demonstrated an average percentage difference of 19.195% reduction in routing delay, 27.335% reduction in energy consumption, 22.18% increase in packet delivery ratio, 21.25% increase in throughput, and reducing interference up to 23% in high mobility scenarios. The DCM-D2X model demonstrates robustness against node failures in large-scale drone networks, significantly improving interference mitigation, routing efficiency, and network connectivity. These advancements increase D2X communication network performance. INDEX TERMS Drone to Everything (D2X), Flying ad-hoc network (FANET), Grey wolf optimization (GWO), Performance, Single point of failure (SOPF), Unmanned aerial vehicles (UAVs).

A Critical Review of Information Provision for U-Space Traffic Autonomous Guidance

“…The trends of the presented graph in [68] show significant improvements in terms of connection loss rate, latency, data rate, and getting updates of location information. Similarly, in a decentralized system where the drone-to-everything concept is utilized in a multiple-layer cooperative architecture in combination with hybrid bioinspired grey wolf optimization, waypoint traceability has been proposed [69], resulting in significant improvements in latency and reliability.…”