Abstract-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.

SummaryQuality of service (QoS) is a key problem of today's IP networks. Many frameworks (IntServ, DiffServ, MPLS, etc.) have been proposed to provide service differentiation in the Internet. At the same time, the Internet is becoming more and more heterogeneous due to the recent explosion of wireless networks. In wireless environments, bandwidth is scarce and channel conditions are time-varying and sometimes highly lossy. Many previous research works show that what works well in a wired network cannot be directly applied in the wireless environment. Although IEEE 802.11 wireless LAN (WLAN) is the most widely used WLAN standard today, it cannot provide QoS support for the increasing number of multimedia applications. Thus, a large number of 802.11 QoS enhancement schemes have been proposed, each one focusing on a particular mode. This paper summarizes all these schemes and presents a survey of current research activities. First, we analyze the QoS limitations of IEEE 802.11 wireless MAC layers. Then, different QoS enhancement techniques proposed for 802.11 WLAN are described and classified along with their advantages/drawbacks. Finally, the upcoming IEEE 802.11e QoS enhancement standard is introduced and studied in detail.

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Non-orthogonal multiple access (NOMA) is emerging as a promising, yet challenging, multiple access technology to improve spectrum utilization for the fifth generation (5G) wireless networks. In this paper, the application of NOMA to multicast cognitive radio networks (termed as MCR-NOMA) is investigated. A dynamic cooperative MCR-NOMA scheme is proposed, where the multicast secondary users serve as relays to improve the performance of both primary and secondary networks. Based on the available channel state information (CSI), three different secondary user scheduling strategies for the cooperative MCR-NOMA scheme are presented. To evaluate the system performance, we derive the closed-form expressions of the outage probability and diversity order for both networks. Furthermore, we introduce a new metric, referred to as mutual outage probability to characterize the cooperation benefit compared to non cooperative MCR-NOMA scheme. Simulation results demonstrate significant performance gains are obtained for both networks, thanks to the use of our proposed cooperative MCR-NOMA scheme. It is also demonstrated that higher spatial diversity order can be achieved by opportunistically utilizing the CSI available for the secondary user scheduling.

Abstract-To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing (LSH), i.e., SerRec distri-LSH , is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM dataset validate the feasibility of our approach in terms of service recommendation accuracy, scalability and privacy preservation.