Current Issue : October - December Volume : 2015 Issue Number : 4 Articles : 5 Articles
This paper mainly describes a fault diagnosis scheme for aircraft multi branches wiring networks. The background theory is based\non the matching algorithm with theoretical reflection waveform and realistic reflection waveform. Firstly, a numerical model\nwith a comprehensive database was built to create the theoretical waveform of the measured wiring network. On the other hand,\nthe realistic reflection waveform of the measured wiring network can be collected by reflectometry detection system. Then, the\nwave form matching algorithm and fault diagnosis algorithm were applied to detect the fault type and fault location in the measured\nwiring network. With all the steps above, the fault diagnosis scheme was designed and realized. Finally, a multi branches wiring\nnetwork, which includes two branches, and one of its branches contains two sub branches with different types of faults, was built to\nverify the fault diagnosis scheme and the results show that the scheme is an effective way to diagnose faults in multi branches wiring\nnetwork...
The paper addresses the problem of improving the accuracy of the measurements collected by a sensor network, where simplicity\nand cost-effectiveness are of utmost importance. An adaptive Bayesian approach is proposed to this aim,which allows improving the\naccuracy of the delivered estimates with no significant increase in computational complexity. Remarkably, the resulting cooperative\nalgorithm does not require prior knowledge of the (hyper)parameters and is able to provide a ââ?¬Å?denoisedââ?¬Â version of the monitored\nfield without losing accuracy in detecting extreme (less frequent) values, which can be very important for a number of applications.\nA novel performance metric is also introduced to suitably quantify the capability to both reduce the measurement error and retain\nhighly-informative characteristics at the same time. The performance assessment shows that the proposed approach is superior to\na low-complexity competitor that implements a conventional filtering approach....
Database applications in wireless sensor networks very often demand data collection from sensor nodes of specific target regions.\nDesign and development of spatial query expressions and energy-efficient query processing strategy are important issues for sensor\nnetwork database systems. The existing sensor network database systems lack the needed sophistication for the space calculation\nof the target sensor nodes; hence, unnecessary query/data transmissions are required between the sensor nodes and the server.\nThis paper describes our spatial operations and energy-efficient query processing methods that are designed and implemented\nin our sensor network database system called SNQL+????. With a set of spatial operators based on geometric parameters, such as\nEnvelope, NearBy, Distance, Direction, and set theoretic operators, SNQL+???? allows sensor network applications to easily specify the\ntarget space of interest. Our energy-efficient query processing strategy implements an in-network querymanagement based on the\nlowest common ancestor (LCA) algorithm, so that the query processing cost for calculating the target spaces is greatly reduced by\navoiding the need of heavy query/data transmissions between the base-station and target nodes. Performance evaluation shows that\nour proposed design and implementation of spatial query expressions and processing strategy achieve improved energy efficiency\nfor database operations in the wireless sensor network....
In a cognitive radio network (CRN), secondary users (SUs) utilize primary users (PUs) licensed spectrum in an opportunistic\nmanner. Spectrum sensing is of the utmost importance in CRN to find and use the available spectrum without harmful interference\nto the PUs. Conventionally, to implement spectrum utilization, SUs are required to sense the primary spectrum first and then\ntransmit data on the available spectrum. In this paper, we propose a dedicated wireless spectrum sensing network (WSSN),\neliminating sensing overhead from SUs with the aim of improving achievable throughput. With WSSN assistance, we eliminate\nsensing time fromthe SUs frame, hence increasing the transmission time,whichmaximizes the achievable throughput.Additionally,\nthe sensing duration is increased by deploying a dedicated WSSN, decreasing the probability of false alarm and achieving a\ntargeted high probability of detection. A low probability of false alarm increases the spectrumutilization, improving the achievable\nthroughput, while a high detection probability ensures PUs protection. Moreover, the proposed technique also addresses hidden\nand exposed terminal problems along with smooth spectrum mobility. Finally, we provide simulation results to demonstrate the\nproposed techniques, effectiveness. In the results, we have compared the achievable throughput of the proposed scheme with that\nof conventional CRN....
The main focus of this paper is the resilience of communication protocols for data gathering in distributed, large scale, and dense\nnetworks. In our previouswork,we have proposed the resilientmethods based on randombehavior and data replications to improve\nroute diversification, thus to take advantage of redundant network structure. Following these previous methods, we propose in this\npaper a new resilient method based on network coding techniques to improve resilience in Wireless Sensor Networks (WSNs)\nfor smart metering applications. More precisely, using our resilience metric based on a performance surface, we compare several\nvariants of a well-known gradient based routing protocol with the previous methods (randomrouting and packet replications) and\nthe new proposed methods (two network coding techniques). The proposed methods outperformed the previous methods in terms\nof data delivery success even in the presence of high attack intensity...
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