In the NEtwork MObility （NEMO） environment, mobile networks can form a nested structure. In nested mobile networks that use the NEMO Basic Support （NBS） protocol, pinball routing problems occur because packets are...In the NEtwork MObility （NEMO） environment, mobile networks can form a nested structure. In nested mobile networks that use the NEMO Basic Support （NBS） protocol, pinball routing problems occur because packets are routed to all the home agents of the mobile routers using nested tunneling. In addition, the nodes in the same mobile networks can communicate with each other regardless of Internet connectivity. However, the nodes in some mobile networks that are based on NBS cannot communicate when the network is disconnected from the Internet. In this paper, we propose a route optimization scheme to solve these problems. We introduce a new IPv6 routing header named ＂destination-information header＂ （DH）, which uses DH instead of routing header type 2 to optimize the route in the nested mobile network. The proposed scheme shows at least 30% better performance than ROTIO and similar performance improvement as DBU in inter-route optimization. With respect to intra-route optimization, the proposed scheme always uses the optimal routing path. In addition, the handover mechanism in ROAD＋ outperforms existing schemes and is less sensitive to network size than other existing schemes.展开更多
Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation...Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit （GPU） to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.展开更多
基金supported by MKE,Korea,under ITRC NIPA-2009-(C1090-0902-0046)by MEST,Korea under WCU Program supervised by the KOSEF(No.R31-2008-000-10062-0).
文摘In the NEtwork MObility （NEMO） environment, mobile networks can form a nested structure. In nested mobile networks that use the NEMO Basic Support （NBS） protocol, pinball routing problems occur because packets are routed to all the home agents of the mobile routers using nested tunneling. In addition, the nodes in the same mobile networks can communicate with each other regardless of Internet connectivity. However, the nodes in some mobile networks that are based on NBS cannot communicate when the network is disconnected from the Internet. In this paper, we propose a route optimization scheme to solve these problems. We introduce a new IPv6 routing header named ＂destination-information header＂ （DH）, which uses DH instead of routing header type 2 to optimize the route in the nested mobile network. The proposed scheme shows at least 30% better performance than ROTIO and similar performance improvement as DBU in inter-route optimization. With respect to intra-route optimization, the proposed scheme always uses the optimal routing path. In addition, the handover mechanism in ROAD＋ outperforms existing schemes and is less sensitive to network size than other existing schemes.
文摘Acquiring a set of features that emphasize the differences between normal data points and outliers can drastically facilitate the task of identifying outliers. In our work, we present a novel non-parametric evaluation criterion for filter-based feature selection which has an eye towards the final goal of outlier detection. The proposed method seeks the subset of features that represent the inherent characteristics of the normal dataset while forcing outliers to stand out, making them more easily distinguished by outlier detection algorithms. Experimental results on real datasets show the advantage of our feature selection algorithm compared with popular and state-of-the-art methods. We also show that the proposed algorithm is able to overcome the small sample space problem and perform well on highly imbalanced datasets. Furthermore, due to the highly parallelizable nature of the feature selection, we implement the algorithm on a graphics processing unit （GPU） to gain significant speedup over the serial version. The benefits of the GPU implementation are two-fold, as its performance scales very well in terms of the number of features, as well as the number of data points.
