IEEE 802.11e standard is proposed to support Quality of Service (QoS) in WIRELESS local area networks (WLANS). The distributed access method of 802.11e, Enhanced Distributed Channel Access (EDCA), prioritizes traffic generated by delay-sensitive applications by increasing probability to access medium for this type of traffic. EDCA resets the Contention Window (CW) of mobile stations to a minimum value after each successful transmission. This static behavior is not adapted to dynamic conditions of WIRELESS network as it degrades network performance when channel access requests increases. Several mechanisms focusing on network adaptation have been proposed to improve the performance of IEEE 802.11e standard. The main limitation for these approaches is slow adaptation to the network state when the channel experiences bursty traffic.In this paper, we propose a new dynamic approach for contention window adjustement for each Access Category (AC) according to the traffic and network conditions. We compare our proposal with the IEEE 802.11e EDCA standard as well as some other mechanisms in the literature.Simulation results show that our proposed approach outperforms EDCA of 802.11e as well as some other enhancement to the standard present in the literature.

Nowadays WIRELESS networks are becoming important in personal and public communication and growing very rapidly. Similarly, Software De ned Network (SDN) is an emerging approach to over-come challenges of traditional networks. In this paper, a new SDN-based framework is proposed to  ne-grained control of 802. 11 WIRELESS LANS. This work describes the bene ts of programmable Access Points. The proposed framework is evaluated on a prototype. We show that this architecture allows us to react to application needs, adjust network con guration for a priority application, detect network problems, and improve the user experience of the network. Our architecture relies on the IEEE 802. 11 standard, and exploits it to collect information from the stations.

MIMO technology offers a wide range of transmission rates for modern WIRELESS LANS. In order to improve the performance of the rate control module, statistical information on the history of state and usage of each transmission rate is maintained at the MAC layer to help determine the rate at which future packets are sent. However, the great diversity of transmission rates in the 802. 11n and 802. 11ac standards imposes an overhead for updating this information. In this article, to reduce the state space of transmission rates while keeping statistics approximately up to date for each rate, a method for clustering rates is presented so that when sending a packet over a transmission rate, statistical information relating to all the rates belonging to the same cluster is updated. As a result, statistics for a greater number of rates can be updated even when sending a fewer number of packets. We implemented our proposed mechanism in the Linux kernel environment and evaluated its performance under different conditions on an experimental testbed deployed in our research laboratory. The results show that the proposed method outperforms the de-facto Minstrel-HT rate control mechanism in terms of throughput and number of successful transmissions.

Land use/cover change map production is one of the basic needs for environmental monitoring and management. Since the change maps are usually used in planning and decision-making, certainty and reliability of these maps can be very important in many applications. Unfortunately in many studies only probability values as obtained from MLC approach have been used for uncertainty estimation. Here a new approach has been developed which is based on the probability information as well as spatial parameters including distance, neighborhood, extent and the type of change. In this study, two Landsat TM images of Isfahan urban area provided in 1990 and 1998 have been co-registered using first order polynomial and nearest neighbor resampling approach. The registered images have been then classified to ten different land use/land cover classes using Maximum Likelihood Classification algorithm. Probabilistic measures generated by the MLC have been used for modeling uncertainty. Using different spatial analysis functions for modeling the change of agricultural areas to residential areas, the relevant spatial parameters have been extracted. Based on logistic regression approach, probabilistic parameters and spatial parameters have been integrated to generate a layer, which shows uncertainty of change of agricultural areas to residential areas. The Relative Operating Characteristics (ROC) index has been used for validation of the model and it has been estimated to be 0.9944, which is an indicative of very good model fitting. As a final conclusion, development of this model is suggested for quantitative evaluation of uncertainty in change detection.