scholarly journals Group Mobility Model for Complex Multimission Cooperation of UAV Swarm

2022 ◽  
Vol 2022 ◽  
pp. 1-22
Author(s):  
Xiaoyan Gu ◽  
Feng He ◽  
Rongwei Wang ◽  
Liang Chen
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Network Connectivity ◽  
Mobility Model ◽  
Reference Node ◽  
Class Mobility ◽  
Time Performance ◽  
Model Based ◽  
Uav Swarm ◽  
And Task

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.

scholarly journals Aero-Engine Real-Time Models and Their Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiajie Chen ◽  
Zhongzhi Hu ◽  
Jiqiang Wang
Keyword(s):  
Real Time ◽  
Future Research ◽  
Component Level ◽  
Time Model ◽  
Modeling Methods ◽  
Time Performance ◽  
Model Based ◽  
Time Modeling ◽  
Aero Engine ◽  
Flight Envelope

Aero-engine real-time models are widely used in control system design, integration, and testing. They can be used as the basis for model-based engine intelligent controls and health management, which is critical to improve engine safety, reliability, economy, and other performance indicators. This article provides an up-to-date review on aero-engine real-time modeling methods, model adaptation techniques, and applications for the last several decades. Besides, future research directions are also discussed, mainly focusing on the following four areas:(1) verification of the aero-engine real-time model over the full flight envelope; (2) better balance between real-time performance and accuracy in simplified methods for the aero-thermodynamic component level models; (3) further improvement in the real-time performance for the identified nonlinear models over the full flight envelope; (4) improvement of hybrid on-board adaptive real-time models combining the advantages of both model-based and data-based on-board adaptive real-time modeling methods.

Task Scheduling under Uncertain Timing Constraints in Real-Time Embedded Systems

2013 ◽  
pp. 211-235 ◽  
Author(s):  
Pranab K. Muhuri ◽  
K. K. Shukla
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Resource Constraints ◽  
Systems Design ◽  
Task Completion ◽  
Timing Constraints ◽  
Timing Constraint ◽  
Processing Times ◽  
And Task

In real-time embedded systems, timeliness of task completion is a very important factor. In such systems, correctness of the output depends on the timely production of results in addition to the logical outcome of computation. Thus, tasks have explicit timing constraints besides other characteristics of general systems, and task scheduling aims towards devising a feasible schedule of the tasks such that timing constraints, resource constraints, precedence constraints, etc. are complied. In real-time embedded systems, the most important timing constraint of a task is the deadline, as tasks must be completed within this time. The next important timing constraint is the processing time, because a task occupies a processor only for this duration of time. However, in the early phase of real-time embedded systems design only an approximate idea of the tasks and their characteristics are known. As a result, uncertainty or impreciseness is associated with the task deadlines and processing times; hence, it is appropriate to use fuzzy numbers to model deadlines and processing times in real-time embedded systems. The chapter introduces a new method using mixed cubic-exponential Hermite interpolation technique for intuitively defining smooth Membership Functions (MFs) for fuzzy deadlines and processing times. The effect of changes in parameterized MFs on the task schedulability and task priorities are explained. Examples are given to demonstrate the significant features and better performance of the new technique.

scholarly journals Research on Embedded Multi-task Balancing Scheduling Algorithm

2018 ◽  
Vol 176 ◽  
pp. 01025
Author(s):  
Han Zhuangzhi ◽  
Ma Tianlin
Keyword(s):  
Operating System ◽  
Embedded Systems ◽  
Embedded System ◽  
Real Time ◽  
Scheduling Algorithm ◽  
Task Decomposition ◽  
The Real ◽  
Time Performance ◽  
The Embedded System ◽  
And Task

For embedded systems, there are two cases of using an operating system and not using an operating system. When the real-time task is accomplished by the embedded system of the operating system, the task needs to meet certain conditions and occupy part of the processor's resources. Therefore, based on the method of event interruption, timed interruption and task decomposition, the real-time performance of the completion of the task of the embedded system is achieved. Finally, an embedded radar track compression scheduling algorithm is designed. It is proved through experiment that the track data can be compressed and transmitted in real time.

Simulation on Task Scheduling for Multiprocessors Based on Improved Neural Network

2014 ◽  
Vol 513-517 ◽  
pp. 2293-2296
Author(s):  
Xiao Fang Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
The Real ◽  
Time Performance ◽  
Task Scheduling Algorithm ◽  
Time Requirements ◽  
Neural Network Algorithm ◽  
Feasible Solutions

This paper mainly discusses task scheduling for multiprocessors. Application requires higher performance of the multiprocessors task scheduling systems. The traditional algorithms majorly consider the accuracy and neglect the real-time performance. In order to improve the real-time performance while maintaining the accuracy, the paper proposes a task scheduling algorithm (GA-ACO) for multiprocessors based on improved neural network. It first builds mathematical models for task scheduling of multiprocessor systems, and then introduces genetic algorithms to quickly find feasible solutions. The simulation results show that the improved neural network algorithm not only has the global optimization ability of genetic algorithm, but also has both local search and the positive feedback capabilities of neural networks; compared with single optimization algorithm, it can quickly find the task scheduling solutions to meet real-time requirements, accelerate the speed of execution of the task, furthermore achieve reasonable, effective task allocation and scheduling for multi-processor.

scholarly journals Real-time edge framework (RTEF): task scheduling and realisation

2021 ◽  
Author(s):  
Volkan Gezer ◽  
Achim Wagner
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Resource Planning ◽  
Use Case ◽  
Reference Architecture ◽  
Computing Power ◽  
Research Areas ◽  
New Research ◽  
And Task

AbstractWith the big success of the Cloud Computing, or the Cloud, new research areas appeared. Edge Computing (EC) is one of the recent paradigms that is expected to overcome the Quality of Service (QoS) and latency issues caused by the best-effort behaviour of the Cloud. EC aims to bring the computation power close to the end devices as much as possible and reduce the dependency to the Cloud. Bringing computing power close to the source also enables real-time applications. In this paper, we propose a novel software reference architecture for Edge Servers, which is operating system (OS) and hardware-agnostic. Edge Servers can collaborate and execute (near) real-time tasks on time, either by downscaling or scheduling them according to their deadlines or offloading them to other Edge Servers in the network. Decision making for offloading, resource planning, and task scheduling are challenging problems in decentralized systems. The paper explains how resource planning and task scheduling can be overcome with software approach. Finally, the article realises the architecture as a framework, called Real-Time Edge Framework (RTEF) and validates its correctness with a use case.

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