Adaptive EDCF: Enhanced service differentiation for IEEE 802.11 wireless ad-hoc networks

This paper describes an adaptive service differentiation scheme for QoS enhancement in IEEE 802.11 wireless ad-hoc networks. Our approach, called adaptive enhanced distributed coordination function (AEDCF), is derived from the new EDCF introduced in the upcoming IEEE 802.11e standard. Our scheme aims to share the transmission channel efficiently. Relative priorities are provisioned by adjusting the size of the contention window (CW) of each traffic class taking into account both applications requirements and network conditions. We evaluate through simulations the performance of AEDCF and compare it with the EDCF scheme proposed in the 802.11e. Results show that AEDCF outperforms the basic EDCF, especially at high traffic load conditions. Indeed, our scheme increases the medium utilization ratio and reduces for more than 50% the collision rate. While achieving delay differentiation, the overall goodput obtained is up to 25% higher than EDCF. Moreover, the complexity of AEDCF remains similar to the EDCF scheme, enabling the design of cheap implementations

Adaptive fair channel allocation for QoS enhancement in IEEE 802.11 wireless LANs

The emerging widespread use of real-time multimedia applications over wireless networks makes the support of quality of service (QoS) a key problem. In this paper, we focus on QoS support mechanisms for IEEE 802.11 wireless ad-hoc networks. First, we review limitations of the upcoming IEEE 802.11e enhanced DCF (EDCF) and other enhanced MAC schemes that have been proposed to support QoS for 802.11 ad-hoc networks. Then, we describe a new scheme called adaptive fair EDCF that extends EDCF, by increasing the contention window during deferring periods when the channel is busy, and by using an adaptive fast backoff mechanism when the channel is idle. Our scheme computes an adaptive backoff threshold for each priority level by taking into account the channel load. The new scheme significantly improves the quality of multimedia applications. Moreover, it increases the overall throughput obtained both in medium and high load cases. Simulution results show that our new scheme outperforms EDCF and other enhanced schemes. Finally, we show that the adaptive fair EDCF scheme achieves a high degree of fairness among applications of the same priority level

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Breaking the Legend: Maxmin Fairness notion is no longer effective

In this paper we analytically propose an alternative approach to achieve better fairness in scheduling mechanisms which could provide better quality of service particularly for real time application. Our proposal oppose the allocation of the bandwidth which adopted by all previous scheduling mechanism. It rather adopt the opposition approach be proposing the notion of Maxmin-charge which fairly distribute the congestion. Furthermore, analytical proposition of novel mechanism named as Just Queueing is been demonstrated.Comment: 8 Page