N. Mohamed, Latifa Al-Muhairi, J. Al-Jaroodi, I. Jawhar
{"title":"用于水下管道监测的容错声学传感器网络","authors":"N. Mohamed, Latifa Al-Muhairi, J. Al-Jaroodi, I. Jawhar","doi":"10.1109/HPCSim.2014.6903782","DOIUrl":null,"url":null,"abstract":"Underwater Acoustic Sensor Networks (UASNs) can be used to monitor long underwater pipeline structures for oil, gas, and water. In this case, a special type of UASNs, UASN-P (UASN for long pipelines) is used. One of the main challenges of using UASN-P is the reliability of the connections among the nodes. Faults in a few contiguous nodes may cause the creation of holes which will result in dividing the network into multiple disconnected segments. As a result, sensor nodes that are located between holes may not be able to deliver their sensed information which negativity affects the network sensing coverage. This paper provides an analysis of the different types of faults in UASN-P and studies their negative impact on the sensing coverage. We utilize Autonomous Underwater Vehicles (AUVs) and develop two models to overcome these faults and enhance coverage. The first model utilizes AUVs to function as mobile sensor nodes to cover the network holes while the second model uses the AUVs to deliver and deploy fixed sensor nodes in the network holes to replace faulty nodes. In both models, placed nodes can provide additional sensing coverage as well as enable connectivity among disconnected segments in the UASN-P. A strategy for best allocation using a limited number of sensors or sensing vehicles is developed. In addition, evaluations and comparison between both models are provided.","PeriodicalId":6469,"journal":{"name":"2014 International Conference on High Performance Computing & Simulation (HPCS)","volume":"02 1","pages":"877-884"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A fault-tolerant acoustic sensor network for monitoring underwater pipelines\",\"authors\":\"N. Mohamed, Latifa Al-Muhairi, J. Al-Jaroodi, I. Jawhar\",\"doi\":\"10.1109/HPCSim.2014.6903782\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Underwater Acoustic Sensor Networks (UASNs) can be used to monitor long underwater pipeline structures for oil, gas, and water. In this case, a special type of UASNs, UASN-P (UASN for long pipelines) is used. One of the main challenges of using UASN-P is the reliability of the connections among the nodes. Faults in a few contiguous nodes may cause the creation of holes which will result in dividing the network into multiple disconnected segments. As a result, sensor nodes that are located between holes may not be able to deliver their sensed information which negativity affects the network sensing coverage. This paper provides an analysis of the different types of faults in UASN-P and studies their negative impact on the sensing coverage. We utilize Autonomous Underwater Vehicles (AUVs) and develop two models to overcome these faults and enhance coverage. The first model utilizes AUVs to function as mobile sensor nodes to cover the network holes while the second model uses the AUVs to deliver and deploy fixed sensor nodes in the network holes to replace faulty nodes. In both models, placed nodes can provide additional sensing coverage as well as enable connectivity among disconnected segments in the UASN-P. A strategy for best allocation using a limited number of sensors or sensing vehicles is developed. In addition, evaluations and comparison between both models are provided.\",\"PeriodicalId\":6469,\"journal\":{\"name\":\"2014 International Conference on High Performance Computing & Simulation (HPCS)\",\"volume\":\"02 1\",\"pages\":\"877-884\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on High Performance Computing & Simulation (HPCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPCSim.2014.6903782\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCSim.2014.6903782","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fault-tolerant acoustic sensor network for monitoring underwater pipelines
Underwater Acoustic Sensor Networks (UASNs) can be used to monitor long underwater pipeline structures for oil, gas, and water. In this case, a special type of UASNs, UASN-P (UASN for long pipelines) is used. One of the main challenges of using UASN-P is the reliability of the connections among the nodes. Faults in a few contiguous nodes may cause the creation of holes which will result in dividing the network into multiple disconnected segments. As a result, sensor nodes that are located between holes may not be able to deliver their sensed information which negativity affects the network sensing coverage. This paper provides an analysis of the different types of faults in UASN-P and studies their negative impact on the sensing coverage. We utilize Autonomous Underwater Vehicles (AUVs) and develop two models to overcome these faults and enhance coverage. The first model utilizes AUVs to function as mobile sensor nodes to cover the network holes while the second model uses the AUVs to deliver and deploy fixed sensor nodes in the network holes to replace faulty nodes. In both models, placed nodes can provide additional sensing coverage as well as enable connectivity among disconnected segments in the UASN-P. A strategy for best allocation using a limited number of sensors or sensing vehicles is developed. In addition, evaluations and comparison between both models are provided.