Abstract

Timing synchronization has a vital role in swarm drones' network (SDN) or a swarm of unmanned aerial vehicle (UAV) network. Current timing synchronization methods focus on enhancing single-hop skews which remarkably improve timing synchronization precision at this level. The improper clock of the drone system can cause interference, affect spectrum precision and interrupt the operation of the transceiver. In the drones' network, master drones' (MD) neighbor drone's timing synchronization approaches like Reference Broadcast System (RBS) realize a good performance. However, the requirement of one super drone with a large number of broadcasts for RBS makes it unrealistic to use in some situations like SDN network situation. Appropriate study and adjustments are needed to have real timing synchronization by eliminating the clocks drift and enhancing the timing synchronization precision. Therefore, a new self-timing synchronization approach is proposed in this paper where several MD drones can autonomously generate swarm clusters. The cluster head (CH) instigates a timing synchronization procedure starting with intra-Swarm cluster timing synchronization. The intermediate drones (ID) are elected between two swarm clusters to synchronize all drones in line with the inter-swarm cluster timing synchronization approach. The proposed approach is distributed and flexible to achieve high timing synchronization precision. The paper proposes a novel self-timing synchronization approach for in large scale semi-flat SND network architecture. Self-timing synchronization is swarm cluster-based and applicable for a huge number of master drones in SDN. One is the intra-Swarm cluster where the timing synchronization procedure starts with the CH to synchronize all CM. Secondly, in the inter-swarm cluster timing synchronization, two clusters are synchronized via intermediate drone (ID). However, the simulations demonstrated that in many cases all CHs are synchronized by the synchronized CHs from intra-swarm cluster timing synchronizations; this increased the system throughput and synchronization delay to about 75% compared to what we planned to achieve. Moreover, the simulation results also proved that the achieved synchronization precision can be used for position estimation and prediction with high accuracy.

Highlights

  • INTRODUCTIONInspiring and markable progress in the use of swarm drones’ network (SDN) or swarm

  • In recent few years, and inspiring and markable progress in the use of swarm drones’ network (SDN) or swarmThe associate editor coordinating the review of this manuscript and approving it for publication was Xi Peng .of the unmanned aerial vehicle (UAV) network for quite widespread domains of deployments and applications, for civilian, commercial and military

  • This paper presents a detailed analytical evaluation along with the derivation of a mathematical model, MAC layer sync frame structure, and timeline of the proposed scheme which fulfills the purposes through simulation verification of the precise timing synchronization for the whole network

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Summary

INTRODUCTION

Inspiring and markable progress in the use of swarm drones’ network (SDN) or swarm. The clustering procedure can be generated by CHs with faster clock timing [1]–[5] This happens when such drones start broadcasting beacon packets to their neighbors’ cluster members (CM) for exchanging information to initiate the clustering procedure. The proposed method is quite different from the previous swarm drone’s timing synchronization techniques invented by IEEE 1588, Reference Broadcast System (RBS), and even synchronized single-hop methods by forming swarm clusters among random MD drones’ number and implementing hybrid (one way and two ways) messaging system for both inter-swarm cluster and intra-swarm cluster timing synchronization approaches. The approach includes apprising and synchronizing the timing of the clock through transmitting a beacon packet for the entire SDN. The proposed self-timing synchronization approach is cluster-based and can be applied for a large number of swarm drones’ or master drones (MD) in SDN.

RELATED WORKS
29. Output
RESULT
AVERAGE CLOCK OFFSET ANALYSIS
Findings
CONCLUSION
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