An analytical model of the virtual collision handler of 802.11e

A number of analytical models have been proposed to describe the priority schemes of the Enhanced Distributed Channel Access (EDCA) mechanism of the IEEE 802.11e standard. EDCA provides a class-based differentiated Quality of Service (QoS) to IEEE 802.11 WLANs. Many have used a multiple number of nodes to study the differentiation behaviour of the model. However, in many real-life usage scenarios Internet traffic is often asymmetric with much downlink traffic from the access point to the stations and little traffic in the reverse direction. Hence, most of the overall traffic differentiation will happen in the Virtual Collision Handler (VCH) of the access point. If the access point uses EDCA, it should know the characteristics of the VCH to be able to control the differentiation of the downlink traffic. The main contribution of this paper opposed to other works is that it demonstrates how a generic channel model of 802.11e can be modified to predict the behaviour of the VCH with a remarkably good accuracy. In doing so, we first introduce virtual collision handling into the generic model. We observe good match between the analytical model and simulations

A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

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Improving Classification of Tweets Using Linguistic Information from a Large External Corpus

The bag of words representation of documents is often unsat- isfactory as it ignores relationships between important terms that do not co-occur literally. Improvements might be achieved by expanding the vocabulary with other relevant word, like synonyms. In this paper we use word-word co-occurence information from a large corpus to expand the vocabulary of another corpus consisting of tweets. Several different methods on how to include the co-occurence information are constructed and tested out on the classification of real twitter data. Our results show that we are able to reduce the number of erroneous classifications by 14% using co-occurence information

Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles

Author's accepted manuscriptProviding full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function.acceptedVersio

Security Challenges with Cross-Domain Information Exchange: Integrity and Guessing Attacks

Current research on cross-domain information ex- change is advocating to move away from the inflexible Bell-La Padula (BLP) model, into a more complex policy-driven security model where information objects and end-users are characterized in terms of complex meta-data. It will lead to higher flexibility but will also rely not only on guards, but also on automatic or semi-automatic tools for forming and processing the meta- data. In this paper, we point out some potential pitfalls with this approach. The paper focuses specifically on the relaxation of the BLP security model for confidentiality and discusses security concerns that arise from the use of such tools in combination with guards

Delay and Throughput Analysis of IEEE 802.11e EDCA under Varying Traffic Loads

Abstract — An analytical model is proposed to describe the priority schemes of the Enhanced Distributed Channel Access (EDCA) mechanism of the IEEE 802.11e standard. EDCA provides class-based differentiated Quality of Service (QoS) to IEEE 802.11 WLANs. The main contribution of this paper opposed to other works is that the model predicts the full delay distribution through its z-transform. Furthermore, the mean delay, throughput and frame dropping probabilities of the different traffic classes are found in the whole range from a lightly loaded, non-saturated channel to a heavily congested, saturated medium. Moreover, the model describes differentiation based on different Arbitration Inter-Frame Space (AIFS) values, in addition to the other adjustable parameters (i.e. window-sizes, retransmission limits, transmission opportunity (TXOP) lengths etc.) also encompassed by previous models. AIFS differentiation is described by a simple equation that enables access points to predict at which traffic loads starvation of a traffic class will occur. The model is calculated numerically and validated against simulation results. We observed a good match between the analytical model and simulations