Using Markov Chains to Model Sensor Network Reliability
| Autor: | Edward C. Wiebe, Simon Diemert, Sudhakar Ganti, Chloe Lampman, Tom Arjannikov |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Markov chain
Computer science Reliability (computer networking) 020206 networking & telecommunications Fault tolerance Context (language use) 02 engineering and technology Network topology Fault (power engineering) Reliability engineering 0202 electrical engineering electronic engineering information engineering Systems design 020201 artificial intelligence & image processing Wireless sensor network Simulation |
| Zdroj: | ARES |
| DOI: | 10.1145/3098954.3098979 |
| Popis: | In the recent decades computing systems have become ubiquitous in our daily life. Due to wear and tear, limited component lifetime, and extraneous factors, among other reasons, all of the systems that we design and implement are subject to failure. One of the main areas in the field of fault tolerance, system evaluation, is concerned with the analysis of systems and faults as well as their operational environments. In the context of system evaluation, this paper is concerned with failure modeling and fault prediction. We propose a model for evaluating network systems in the context of failure and repair. Although the focus here is on sensor networks, it can surely be extended to other situations. A systems engineer can use the proposed model to estimate the longevity of a system and plan appropriate maintenance during the system design or maintenance phases. The approach makes use of Markov chains to model failure states of the system based on historical data. The effectiveness of this model is demonstrated through preliminary experiments and a case study, which also confirm intuitions about the effects of network topology on the network's reliability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|