Accurate available bandwidth estimation in IEEE 802.11-based ad hoc networks

Quality of Service (QoS) support in IEEE 802.11-based ad hoc networks relies on the networks’ ability to estimate the available bandwidth on a given link. However, no mechanism has been standardized to accurately evaluate this resource. This remains one of the main issues open to research in this field. This paper proposes an available bandwidth estimation approach which achieves more accurate estimation when compared to existing research. The proposed approach differentiates the channel busy caused by transmitting or receiving from that caused by carrier sensing, and thus improves the accuracy of estimating the overlap probability of two adjacent nodes’ idle time. Simulation results testify the improvement of this approach when compared with well known bandwidth estimation methods in the literature.

Rethinking available bandwidth estimation in IEEE 802.11-based ad hoc networks

This Letter rethinks the problems of available bandwidth estimation in IEEE 802.11-based ad hoc networks. The estimation accuracy is increased by improving the calculation accuracy that the probability for two adjacent nodes' idle periods toverlap (a key issue when estimating the available bandwidth in 802.11 networks)

Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Classifying Network Protocols: A ‘2 Way’ Flow Approach

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as 'two-way' classification. The 'two-way' classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A 'two-way' classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.