Performance and scalability evaluation of IEEE 802.11v/aa multicast transport

IEEE 802.11v and 802.11aa are two recent amendments that define new functionalities in order to support a reliable multicast transport over wireless networks. The first amendment introduces directed multicast service (DMS). On the other hand, 802.11aa defines the groupcast with retries (GCR) service, which proposes two retransmission policies: block acknowledgement (GCR‐BACK) and unsolicited retry (GCR‐UR). In this paper, we evaluate the throughput and the scalability of these new proposals using both analytical and simulation approaches. We show that DMS has the lowest scalability, while GCR‐BACK is not appropriate for groups with a large number of receivers. We conclude that GCR‐UR is the most appropriate for large groups. However, increasing the number of transmission retries reduces significantly the achieved throughput of the unsolicited retry policy. Copyright © 2016 John Wiley & Sons, Ltd.     

A distance-relatedness dynamic model for clustering high dimensional data of arbitrary shapes and densities

It is important to find the natural clusters in high dimensional data where visualization becomes difficult. A natural cluster is a cluster of any shape and density, and it should not be restricted to a globular shape as a wide number of algorithms assume, or to a specific user-defined density as some density-based algorithms require.In this work, it is proposed to solve the problem by maximizing the relatedness of distances between patterns in the same cluster. It is then possible to distinguish clusters based on their distance-based densities. A novel dynamic model is proposed based on new distance-relatedness measures and clustering criteria. The proposed algorithm “Mitosis” is able to discover clusters of arbitrary shapes and arbitrary densities in high dimensional data. It has a good computational complexity compared to related algorithms. It performs very well on high dimensional data, discovering clusters that cannot be found by known algorithms. It also identifies outliers in the data as a by-product of the cluster formation process. A validity measure that depends on the main clustering criterion is also proposed to tune the algorithm's parameters. The theoretical bases of the algorithm and its steps are presented. Its performance is illustrated by comparing it with related algorithms on several data sets.