• Proceedings of the KSR Conference
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• 1998.11a
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• pp.280-287
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• 1998

In this paper, Dynamic Fault Tree algorithm(DFT algorithm) is presented. This new algorithm provides a concise representation of dynamic fault tolerance system structure with redundancy, dynamic redundancy management and complex fault ＆ error recovery techniques. And it allows the modeler to define a dynamic fault tree model with the relative advantages of both fault tree and Markov models that captures the system structure and dynamic behavior. This algorithm applies to TMR and Dual-Duplex systems with the dynamic behavior and show that this algorithm captured the dynamic behavior in these systems with fault ＆ error recovery technique, sequence-dependent failures and the use dynamic spare. The DFT algorithm for solving the problems of the systems is more effective than the Markov and Fault tree analysis model.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.733-740
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• 2005

In this paper, we propose an approach to transport layer mobility support leveraging the SCTP extension dubbed dynamic address reconfiguration in IPv6 networks. Timing issues related to the end-to-end address management, and a novel error recovery mechanism associated with a handover are discussed. The proposed error recovery mechanism is analyzed and compared to that of the plain SCTP to show that it reduces the handover latency and error recovery time.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.5
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• pp.243-248
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• 2013

Reliability evaluation is important task in embedded system. It can avoid potential failures and manage the vulnerable components of embedded system effectively. Dynamic fault tree analysis is one of the reliability evaluation methods. It can represent dynamic characteristics of a system such as fault & error recovery, sequence-dependent failures. In this paper, the steering system, which is embedded system in vehicles, is represented using dynamic fault tree. We evaluate the steering system using approximation algorithm based on Simpson's rule. A set of simulation results shows that proposed method overcomes the low accuracy of classic approximation method without requiring no excessive calculation time of the Markov chain method.

• Journal of KIISE:Information Networking
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• v.30 no.6
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• pp.755-763
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• 2003

To improve performance over noisy wireless channels, mobile wireless networks employ forward error correction(FEC) techniques. The performance of static FEC algorithms, however, degrades by poorly matching the overhead of their correction code to the degree of the fluctuating underlying channel error. This paper proposes an adaptive FEC technique called FECA(FEC-level Adaptation), which dynamically tunes FEC strength to the currently estimated channel error rate at the data link layer. FECA is suitable for wireless networks whose error rate is high and slowly changing compared to the round-trip time between two communicating nodes. One such example network would be a sensor network in which the average bit error rate is higher than $10^{-6}$ and the detected error rate at one time lasts a few hundred milliseconds on average. Our experiments show that FECA performs 15% in simulations with theoretically modeled wireless channels and in trace-driven simulations based on the data collected from real sensor networks better than any other static FEC algorithms.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.563-572
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• 2002

Wireless mobile networks tend to drop a large portion of packets due to propagation errors rather than congestion. To Improve reliability over noisy wireless channels, wireless networks can employ forward error correction (FEC) techniques. Static FEC algorithms, however, can degrade the performance by poorly matching their overhead to the degree of the underlying channel error, especially when the channel path loss rate fluctuates widely. This paper investigates the benefits of an adaptable FEC mechanism for wireless networks with severe packet loss by analytical analysis or measurements over a real wireless network called sensor network. We show that our adaptive FEC named FECA (FEC-level Adaptation) technique improves the performance by dynamically tuning FEC strength to the current amount of wireless channel loss. We quantify these benefits through a hybrid simulation integrating packet-level simulation with bit-level details and validate that FECA keeps selecting the appropriate FEC-level for a constantly changing wireless channel.