Cyber-Secure UAV Communications Using Heuristically Inferred Stochastic Grammars and Hard Real-Time Adaptive Waveform Synthesis and Evolution

2017 ◽  
Author(s):  
Stuart H. Rubin ◽  
William K. Grefe ◽  
Thouraya Bouabana-Tebibel ◽  
Shu-Ching Chen ◽  
Mei-Ling Shyu ◽  
...  
Keyword(s):  
Real Time ◽  
Waveform Synthesis ◽  
Hard Real Time ◽  
Stochastic Grammars

The Goal-Setting Model in a Multiagent Hard Real-Time System

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 73-78
Author(s):  
Igor В. Fominykh ◽  
◽  
Sergey V. Romanchuk ◽  
Nikolay Р. Alekseev ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Goal Setting ◽  
Time System ◽  
Real Time System ◽  
Hard Real Time

Efficient Scheduling Algorithm for Hard Real-Time Tasks in Primary-Backup Based Multiprocessor Systems

2009 ◽  
Vol 20 (10) ◽  
pp. 2628-2636 ◽  
Author(s):  
Jian WANG ◽  
Jian-Ling SUN ◽  
Xin-Yu WANG ◽  
Shen-Kang WANG ◽  
Jun-Bo CHEN
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Multiprocessor Systems ◽  
Hard Real Time

A Hybrid Static/Dynamic Energy-Aware Weakly-Hard Real-Time Scheduling Algorithm Based on Simple Feedback

2009 ◽  
Vol 32 (6) ◽  
pp. 1140-1146
Author(s):  
Tong WU ◽  
Dong-Song ZHANG ◽  
Shi-Yao JIN
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Hard Real Time ◽  
Simple Feedback

Systematic Development of Hard Real-Time Software: A Comparative Study of Three Methods

1992 ◽  
Author(s):  
Yuh-Jeng Lee ◽  
Valdis Berzins
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Systematic Development ◽  
Hard Real Time

SPREAD - A Routing Protocol to Meet End-to-end Adjusted Deadline in Real Time Wireless Sensor Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 63-78
Author(s):  
Neetika Jain ◽  
Sangeeta Mittal
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Routing Protocol ◽  
Resource Constraints ◽  
Wireless Sensor ◽  
Network Simulator ◽  
Packet Delivery ◽  
High Data ◽  
Hard Real Time

Background: Real Time Wireless Sensor Networks (RT-WSN) have hard real time packet delivery requirements. Due to resource constraints of sensors, these networks need to trade-off energy and latency. Objective: In this paper, a routing protocol for RT-WSN named “SPREAD” has been proposed. The underlying idea is to reserve laxity by assuming tighter packet deadline than actual. This reserved laxity is used when no deadline-meeting next hop is available. Objective: As a result, if due to repeated transmissions, energy of nodes on shortest path is drained out, then time is still left to route the packet dynamically through other path without missing the deadline. Results: Congestion scenarios have been addressed by dynamically assessing 1-hop delays and avoiding traffic on congested paths. Conclusion: Through extensive simulations in Network Simulator NS2, it has been observed that SPREAD algorithm not only significantly reduces miss ratio as compared to other similar protocols but also keeps energy consumption under control. It also shows more resilience towards high data rate and tight deadlines than existing popular protocols.

scholarly journals Software Architecture of a Fog Computing Node for Industrial Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3715
Author(s):  
Ioan Ungurean ◽  
Nicoleta Cristina Gaitan
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Development Process ◽  
Fog Computing ◽  
Low Latency ◽  
Practical Implementation ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Starting Point ◽  
Hard Real Time

In the design and development process of fog computing solutions for the Industrial Internet of Things (IIoT), we need to take into consideration the characteristics of the industrial environment that must be met. These include low latency, predictability, response time, and operating with hard real-time compiling. A starting point may be the reference fog architecture released by the OpenFog Consortium (now part of the Industrial Internet Consortium), but it has a high abstraction level and does not define how to integrate the fieldbuses and devices into the fog system. Therefore, the biggest challenges in the design and implementation of fog solutions for IIoT is the diversity of fieldbuses and devices used in the industrial field and ensuring compliance with all constraints in terms of real-time compiling, low latency, and predictability. Thus, this paper proposes a solution for a fog node that addresses these issues and integrates industrial fieldbuses. For practical implementation, there are specialized systems on chips (SoCs) that provides support for real-time communication with the fieldbuses through specialized coprocessors and peripherals. In this paper, we describe the implementation of the fog node on a system based on Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 SoC.

Exploring Task and Channel Mapping Strategies in Fail-Operational and Hard Real-Time NoCs

2020 ◽  
Author(s):  
Max Koenen ◽  
Nguyen Anh Vu Doan ◽  
Thomas Wild ◽  
Andreas Herkersdorf
Keyword(s):  
Real Time ◽  
Hard Real Time

Implementing analysable hard real-time sporadic tasks in Ada 9X

1994 ◽  
Vol XIV (1) ◽  
pp. 38-49 ◽  
Author(s):  
A. Burns ◽  
A. J. Wellings
Keyword(s):  
Real Time ◽  
Sporadic Tasks ◽  
Hard Real Time
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