Fusion center

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network--oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network--aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network--oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and th...

In this paper we present a Cooperative Surveillance Multi-Agent System (CS-MAS) architecture extended to incorporate dynamic coalition formation. We illustrate specific coalition formation using fusion skills. In this case, the fusion process is divided into two layers:(i) a global layer in the fusion center, which initializes the coalitions and (ii) a local layer within coalitions, where a local fusion agent is dynamically instantiated. There are several types of autonomous agent: surveillance–sensor agents, a fusion center agent, a ...

We consider a small extent sensor network for event detection, in which nodes periodically take samples and then contend over a random access network to transmit their measurement packets to the fusion center. We consider two procedures at the fusion center for processing the measurements. The Bayesian setting, is assumed, that is, the fusion center has a prior distribution on the change time. In the first procedure, the decision algorithm at the fusion center is network--oblivious and makes a decision only when a complete vector of measurements taken at a sampling instant is available. In the second procedure, the decision algorithm at the fusion center is network--aware and processes measurements as they arrive, but in a time-causal order. In this case, the decision statistic depends on the network delays, whereas in the network--oblivious case, the decision statistic does not. This yields a Bayesian change-detection problem with a trade-off between the random network delay and th...

The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy c-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-tointerference plus noise ratio of secondary users as parameter. The weighted fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.