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Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting
 In Proceedings of the International Conference on Machine Learning (ICML
, 1998
"... In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other stateoftheart methods. This classifier has several advantages including ..."
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Cited by 28 (2 self)
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In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other stateoftheart methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that it applies only to discrete attributes, and thus, continuous attributes must be prediscretized. In this paper, we extend TAN to deal with continuous attributes directly via parametric (e.g., Gaussians) and semiparametric (e.g., mixture of Gaussians) conditional probabilities. The result is a classifier that can represent and combine both discrete and continuous attributes. In addition, we propose a new method that takes advantage of the modeling language of Bayesian networks in order to represent attributes both in discrete and continuous form simultaneously, and use both versions in the classifi...
Effect of overhearing transmissions on energy efficiency in dense sensor networks
 In Proceedings of the Third International Symposium on Information Processing in Sensor Networks (IPSN’04
, 2004
"... Energy efficiency is an important design criteria for the development of sensor networking protocols involving data dissemination and gathering. Innetwork processing of sensor data, aggregation, transmission power control in radios, and periodic cycling of node wakeup schedules are some techniques ..."
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Cited by 10 (0 self)
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Energy efficiency is an important design criteria for the development of sensor networking protocols involving data dissemination and gathering. Innetwork processing of sensor data, aggregation, transmission power control in radios, and periodic cycling of node wakeup schedules are some techniques that have been proposed in the sensor networking literature for achieving energy efficiency. Owing to the broadcast nature of the wireless channel many nodes in the vicinity of a sender node may overhear its packet transmissions even if they are not the intended recipients of these transmissions. Reception of these transmissions can result in unnecessary expenditure of battery energy of the recipients. In this paper, we investigate the impact of overhearing transmissions on total energy costs during data gathering and dissemination and attempt to minimize them systematically. We model the minimum energy data gathering problem as a directed minimum energy spanning tree problem where the energy cost of each edge in the wireless connectivity graph is augmented by the overhearing cost of the corresponding transmission. We observe that in dense sensor networks, overhearing costs constitute a significant fraction of the total energy cost and that computing the minimum spanning tree on the augmented cost metric results in energy savings, especially in networks with nonuniform spatial node distribution. We also study the impact of this new metric on the well known energyefficient dissemination (also called broadcasting) algorithms for multihop wireless networks. We show via simulation that through this augmented cost metric, gains in energy efficiency of 10 % or more are possible without additional hardware and minimal additional complexity.
Distributed Optimal SelfOrganisation in Ad Hoc Wireless Sensor Networks
"... Abstract — The work in this paper is motivated by the idea of using randomly deployed wireless networks of miniature smart sensors to serve as distributed instrumentation. In such applications, often the objective of the sensor network is to repeatedly compute and, if required, deliver to an observe ..."
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Abstract — The work in this paper is motivated by the idea of using randomly deployed wireless networks of miniature smart sensors to serve as distributed instrumentation. In such applications, often the objective of the sensor network is to repeatedly compute and, if required, deliver to an observer some result based on the values measured at the sensors. We argue that in such applications it is important for the sensors to selforganise in a way that optimises network throughput. We identify and discuss two main problems of optimal selforganisation: (i) building an optimal topology, and (ii) tuning network access parameters, such as the transmission attempt rate. We consider a simple random access model for sensor networks and formulate these problems as optimisation problems. We then present centralized as well as distributed algorithms for solving them. Results show that the performance improvement is substantial and implementation of such optimal selforganisation techniques may be worth the additional complexity. I.
Distributed Optimal SelfOrganisation in
, 2004
"... The work in this paper is motivated by the idea of using randomly deployed, ad hoc wireless networks of miniature smart sensors to serve as distributed instrumentation. We argue that in such applications it is important for the sensors to selforganise in a way that optimizes network throughput. We ..."
Abstract
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The work in this paper is motivated by the idea of using randomly deployed, ad hoc wireless networks of miniature smart sensors to serve as distributed instrumentation. We argue that in such applications it is important for the sensors to selforganise in a way that optimizes network throughput. We then identify and discuss two main problems of optimal selforganisation: (i) building an optimal topology, and (ii) tuning network access parameters such as the transmission attempt rate. We consider a simple random access model for sensor networks and formulate these problems as optimisation problems. We then present centralized as well as distributed algorithms for solving them. Results show that the performance improvement is substantial and implementation of such optimal selforganisation techniques may be worth the additional complexity.