{"title":"相位相关概率竞争故障下无线传感器网络的可靠性","authors":"Yujie Wang, L. Xing","doi":"10.1109/ICRSE.2017.8030753","DOIUrl":null,"url":null,"abstract":"This paper considers phase-dependent probabilistic failure competitions in reliability analysis of wireless sensor network (WSN) applications. Particularly, a WSN can experience probabilistic functional dependence (PFD) behavior where operations of some sensors (referred to as probabilistic-dependent components) rely on functions of relay nodes (referred to as trigger components) with certain probabilities. Failure competitions exist in the time-domain between the local failure (e.g., transmission unit failure) of a relay node and propagated failures of its dependent sensors (e.g., jamming attacks), causing distinct system statuses. Further, a WSN may be subject to phased-mission requirements, incurring dynamics in system configuration and component failure behavior, as well as statistical dependencies across phases for a given component, which makes the reliability analysis more challenging. This paper models effects of phase-dependent probabilistic competing failures, and suggests a multi-valued decision diagram (MDD) based combinatorial procedure for reliability analysis of non-repairable WSN systems. The proposed method is applicable to arbitrary types of time-to-failure distributions for WSN components and probabilistic isolation factors. A case study of a WSN system for lab condition monitoring is presented to illustrate application and advantages of the proposed method.","PeriodicalId":317626,"journal":{"name":"2017 Second International Conference on Reliability Systems Engineering (ICRSE)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Reliability of wireless sensor networks subject to phase-dependent probabilistic competing failures\",\"authors\":\"Yujie Wang, L. Xing\",\"doi\":\"10.1109/ICRSE.2017.8030753\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers phase-dependent probabilistic failure competitions in reliability analysis of wireless sensor network (WSN) applications. Particularly, a WSN can experience probabilistic functional dependence (PFD) behavior where operations of some sensors (referred to as probabilistic-dependent components) rely on functions of relay nodes (referred to as trigger components) with certain probabilities. Failure competitions exist in the time-domain between the local failure (e.g., transmission unit failure) of a relay node and propagated failures of its dependent sensors (e.g., jamming attacks), causing distinct system statuses. Further, a WSN may be subject to phased-mission requirements, incurring dynamics in system configuration and component failure behavior, as well as statistical dependencies across phases for a given component, which makes the reliability analysis more challenging. This paper models effects of phase-dependent probabilistic competing failures, and suggests a multi-valued decision diagram (MDD) based combinatorial procedure for reliability analysis of non-repairable WSN systems. The proposed method is applicable to arbitrary types of time-to-failure distributions for WSN components and probabilistic isolation factors. A case study of a WSN system for lab condition monitoring is presented to illustrate application and advantages of the proposed method.\",\"PeriodicalId\":317626,\"journal\":{\"name\":\"2017 Second International Conference on Reliability Systems Engineering (ICRSE)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Second International Conference on Reliability Systems Engineering (ICRSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICRSE.2017.8030753\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Second International Conference on Reliability Systems Engineering (ICRSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRSE.2017.8030753","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Reliability of wireless sensor networks subject to phase-dependent probabilistic competing failures
This paper considers phase-dependent probabilistic failure competitions in reliability analysis of wireless sensor network (WSN) applications. Particularly, a WSN can experience probabilistic functional dependence (PFD) behavior where operations of some sensors (referred to as probabilistic-dependent components) rely on functions of relay nodes (referred to as trigger components) with certain probabilities. Failure competitions exist in the time-domain between the local failure (e.g., transmission unit failure) of a relay node and propagated failures of its dependent sensors (e.g., jamming attacks), causing distinct system statuses. Further, a WSN may be subject to phased-mission requirements, incurring dynamics in system configuration and component failure behavior, as well as statistical dependencies across phases for a given component, which makes the reliability analysis more challenging. This paper models effects of phase-dependent probabilistic competing failures, and suggests a multi-valued decision diagram (MDD) based combinatorial procedure for reliability analysis of non-repairable WSN systems. The proposed method is applicable to arbitrary types of time-to-failure distributions for WSN components and probabilistic isolation factors. A case study of a WSN system for lab condition monitoring is presented to illustrate application and advantages of the proposed method.