Worst Case Analysis of Packet Delay in Avionics Systems for Environmental Monitoring
- Authors
- Kang, Kyungtae; Nam, Min-Young; Sha, Lui
- Issue Date
- Dec-2015
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Avionics systems; environmental monitoring; real-time switch; system composition; worst case time analysis
- Citation
- IEEE SYSTEMS JOURNAL, v.9, no.4, pp.1354 - 1362
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE SYSTEMS JOURNAL
- Volume
- 9
- Number
- 4
- Start Page
- 1354
- End Page
- 1362
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/16483
- DOI
- 10.1109/JSYST.2014.2336872
- ISSN
- 1932-8184
- Abstract
- Early analysis of timing is essential in the design of reliable avionics systems. We consider an environmental monitoring system that allows the surroundings of an aircraft to be observed continuously in real time. We analyze timing aspects of the partitions within the sensor and monitoring nodes, and of the intermediate switches that connect them. We use the application-specific I/O integration support tool (ASIIST) to evaluate the worst case delay in the peripheral component interconnect buses of the end nodes. We describe a novel switching algorithm that guarantees a bounded delay for any feasible traffic through each switch, and then derive the worst case delay incurred in a switched network that contains switches operating with the proposed algorithm. By composing these delays, we are able to determine the end-to-end delay over the internal buses and network comprising the entire system, and show how it can be bounded by using our switching algorithm. Our worst case end-to-end delay analysis contributes to more reliable and better verified environmental monitoring services over packet-switched networks in avionics systems. We expect that our work will help reduce the cost of designing and implementing environmental monitoring avionics systems, by making it easier to identify unsatisfactory designs at an early stage.
- Files in This Item
-
Go to Link
- Appears in
Collections - COLLEGE OF COMPUTING > DEPARTMENT OF ARTIFICIAL INTELLIGENCE > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/16483)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.