L. Magagni, D. Gennaretti, M. Nicolini, M. Sergio, R. Guerrieri, R. Canegallo
{"title":"Model for a Smart Network Monitoring a Wired Sensor Matrix","authors":"L. Magagni, D. Gennaretti, M. Nicolini, M. Sergio, R. Guerrieri, R. Canegallo","doi":"10.1109/SAM.2006.1706173","DOIUrl":null,"url":null,"abstract":"Monitoring harsh environments such as underwater scenarios or aircraft external surfaces pertains to important applications like assisted navigation and tactical surveillance; nevertheless, it poses additional challenges compared with standard applications. At Transducers2005 we presented a wired addressing architecture of distributed sensors for monitoring real-time in-situ pressure variations in underwater environment that faces the above-mentioned issues. This architecture consists in a double array of identical and interconnected smart nodes monitoring a matrix of passive sensors. In this paper, we present an analysis of the delay model related to the presented architecture and a calculation of the overall frame-rate of the system as a function of the geometrical topology of the arrays. The topology of the network, i.e. the length of each bus and the number of nodes, can be chosen according to the application, and directly affects the global capacitive load on the serial lines. Each serial line can be schematized with a distributed RC model for the flat cable plus a lumped capacitance for each smart node. Then, a 3rd-order pi-segmented model of O'Brien-Savarino is calculated for a 16-block line with block length equal to 0.3 m. Thanks to that, the global time per iteration is calculated on each bus as well as the scanning time of the whole matrix and the frame rate for the system as a function of sensor distribution and of the aspect ratio of the matrix. This model can be employed to identify the optimal arrangement for the sensor matrix and smart node arrays","PeriodicalId":272327,"journal":{"name":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","volume":"192 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fourth IEEE Workshop on Sensor Array and Multichannel Processing, 2006.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAM.2006.1706173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Monitoring harsh environments such as underwater scenarios or aircraft external surfaces pertains to important applications like assisted navigation and tactical surveillance; nevertheless, it poses additional challenges compared with standard applications. At Transducers2005 we presented a wired addressing architecture of distributed sensors for monitoring real-time in-situ pressure variations in underwater environment that faces the above-mentioned issues. This architecture consists in a double array of identical and interconnected smart nodes monitoring a matrix of passive sensors. In this paper, we present an analysis of the delay model related to the presented architecture and a calculation of the overall frame-rate of the system as a function of the geometrical topology of the arrays. The topology of the network, i.e. the length of each bus and the number of nodes, can be chosen according to the application, and directly affects the global capacitive load on the serial lines. Each serial line can be schematized with a distributed RC model for the flat cable plus a lumped capacitance for each smart node. Then, a 3rd-order pi-segmented model of O'Brien-Savarino is calculated for a 16-block line with block length equal to 0.3 m. Thanks to that, the global time per iteration is calculated on each bus as well as the scanning time of the whole matrix and the frame rate for the system as a function of sensor distribution and of the aspect ratio of the matrix. This model can be employed to identify the optimal arrangement for the sensor matrix and smart node arrays