applications. We use the model to show the effect of changing B-MAC’s parameters and predict the behavior of sensor network applications. By comparing B-MAC to conventional 802.11inspired protocols, specifically S-MAC, we develop an experimental characterization of B-MAC over a wide range of network conditions

of the leaves were represented by a noisy-or: ? (Child= OIParents) = e-Bo-L; B,Paren t; where 110 represents the "leak" term. The QMR-DT network The QMR-DT is a bipartite network whose structure is the same as that shown in figure 2 but the size is much larger. There are approximately 600 diseases

"moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework

In order to obtain maximum energetic efficiency, wireless sensor networks must be able to tailor their mode of operation at every level of the software infrastructure. At the MAC level, adaptability can greatly improve the performance of the system by tuning the parameters of operation

An excess of like-charge dimuons has been observed with the MAC detector in multihadron events produced in e+e- annihilation at fi = 29 GeV. If this excess is attributed to B”-B ” mixing, the corresponding value of the mixing parameter x 4 I’(B + p-X)/I’(B-+ p*X) is x = 0.21~~:~ ~ and x> 0

communication by means of energy conservation without sacrificing reliability. It is compared to Carrier Sense Multiple Access (CSMA), Sensor-MAC (S-MAC) and Berkeley-MAC (B-MAC) in terms of energy consumption. The analysis begins with a parameter space study to discover which attributes affect the energy

of different scenario parameters. The obtained results show that the proposed scheme outperforms throughput bounds achieved when using a legacy 802.11 MAC protocol. The usage of distributed queues and cross-layer information eliminates back-off periods and collisions in data packet transmissions, makes

response; b) correlate differential transcription with cellular protein secretion; c) identify cell line–specific features; and d) search for indoor dust–specific responses. METHODS: Settled dust was sampled in 42 German households, and various contaminants were characterized. We exposed Mono Mac 6, BEAS-2

This paper reports on our experience with the implementation, deployment, and operation of SensorScope, an indoor environmental monitoring network. Nodes run on standard TinyOS components and use B-MAC for the MAC layer implementation. The main component on the server side is a Java application

In order to obtain maximum energetic efficiency, wireless sensor networks must be able to tailor their mode of operation at every level of the software infrastructure. At the MAC level, adaptability can greatly improve the performance of the system by tuning the parameters of operation