SYNDICATE: Software Platform for Distributed Real-Time System

Pak, Eunji; Ha, Youngmok; Park, Jeman; Kim, Yongyeon; Song, Moonsub; Kim, Sang Woo

doi:10.1109/prdc.2015.39

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…'Qplus-AIR' [3]- [6] is a real-time operating system for avionics, which has been developed as a part of the SYNDI-CATE [13] project. It complies with the 'ARINC-653' standard [7], which is a software specification for standard interfaces of avionics.…”

Section: Qplus-airmentioning

confidence: 99%

Timed Model-Based Formal Analysis of a Scheduler of Qplus-AIR, an ARINC-653 Compliance RTOS

Yoon

Lee

Pak

et al. 2017

IEICE Trans. Inf. & Syst.

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Section: Qplus-airmentioning

confidence: 99%

Timed Model-Based Formal Analysis of a Scheduler of Qplus-AIR, an ARINC-653 Compliance RTOS

Yoon

Lee

Pak

et al. 2017

IEICE Trans. Inf. & Syst.

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Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

et al. 2021

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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 intraswarm 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.INDEX TERMS Drones' Network, Timing synchronization, unmanned aerial vehicle (UAV), Cluster, Swarm.

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Relative Time Synchronization of Distributed Applications for Software-in-the-Loop Simulation

Lee

Hwang²,

Seo³

et al. 2016

2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Co

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SYNDICATE: Software Platform for Distributed Real-Time System

Cited by 4 publications

References 2 publications

Timed Model-Based Formal Analysis of a Scheduler of Qplus-AIR, an ARINC-653 Compliance RTOS

Timed Model-Based Formal Analysis of a Scheduler of Qplus-AIR, an ARINC-653 Compliance RTOS

Development of Self-Synchronized Drones’ Network Using Cluster-Based Swarm Intelligence Approach

Relative Time Synchronization of Distributed Applications for Software-in-the-Loop Simulation

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