Toward Intelligent Fuzzy QoS Model in Wireless Ad Hoc Networks

Introduction

The interest in wireless Mobile Ad hoc Networks (MANETs) has grown immensely over the last 10 years. Conventional wireless networks require as prerequisites some form of fixed network infrastructure and centralized administration for their operation. In contrast, the so-called MANETs, consisting of a collection of wireless nodes, all of which may be mobile, dynamically create a wireless network among themselves without using any such infrastructure or administrative support (Haas et al., 1999) . The developing world, where a higher proportion of people live in areas with limited infrastructure, could also benefit from MANET technology. In fact, the “One Laptop-Per-Child” project (laptop.org et al., 2008) is enabling the establishment of what are the largest real-world MANET-like networks to date (Lajos et al., 2009). Often-suggested applications of ad hoc networks include disaster recovery, highly mobile vehicle-to-vehicle networks (VANETs), and battlefield communications. Numerous challenges must be overcome to realize the practical benefits of ad hoc networking. These include mobility and power management, medium access, security, and, of principal interest here, Quality of Service (QoS) issue.

QoS requirements in MANETs mainly result from the rising popularity of end-to-end delay- and bandwidth- real time multimedia services. Different real time applications have various QoS requirements expressed in terms of end-to-end QoS metrics (e.g., delay, available bandwidth, jitter, loss rate). The ad hoc network is thereby required to provide better services than original best effort service, such as differentiated services and guaranteed services, for end-to-end user/applications. Providing QoS assurances to support real time traffic in MANET is difficult due to the unreliable wireless channel, the lack of centralized control, contention for channel access and node mobility. Various solutions have been proposed to provide QoS over ad hoc networks from different layers in the network protocol stack. However, the layered concept was primarily created for wired networks, and multi hop wireless networks operate better with layered design because of their dynamic nature, infrastructure-less architecture, and time varying unstable links and topology (Zhang et al., 2008).

In this chapter, we attempt a brief description to the new but rapidly growing area of research on QoS support in wireless mobile ad hoc networks. We focus mainly on the cross layer QoS models (i.e., frameworks for achieving QoS over multiple layers), thus we do not consider the works based on a single layer (e.g., network layer). We present two kinds of QoS models; “conventional QoS models” which are completely based either on traffic engineering approach or mathematical optimization modeling, and “intelligent QoS models” that exploit the efficiency of intelligent learning theories such as Fuzzy Logic control in wireless networks. Then, we propose a fuzzy cross layer QoS model for traffic regulation and buffer management in wireless ad hoc networks, named FuzzyCCG. The use of fuzzy logic theory in our proposal is justified by the fact that this theory is well adapted to systems characterized by imprecise states, dynamic nature, and uncertain information, as in the case of wireless mobile ad hoc networks.. Furthermore, the use of fuzzy logic can add more flexibility and capability for managing both the random traffic state and the arbitrary buffer change occupancy of mobile devices due to nodes mobility and dynamic topology of ad hoc networks. This theory has been successfully applied in many industrial systems, such as embedded systems and telecommunications control.

The reminder of the chapter is organized as follows: we first present a brief review of ad hoc networks and introduce some networking concepts pertinent to QoS. Following this, we list some relevant conventional QoS models that were found in the literature. Next we introduce the intelligent fuzzy logic theory and its applications in wireless networks; we describe some basic concepts related to this theory and QoS models based on this intelligent tool. Finally, we present our proposed intelligent fuzzy QoS model to support multimedia services in MANETs. The last section summaries the advantages and drawbacks of both convention and intelligent QoS models, discusses the trends in the field, and highlights potential areas of future work. Note that through this chapter, we use the terms “QoS cross layer models” and “QoS models” interchangeably.