**1. Introduction**

WMN (Wireless Mesh Network) is usually built on fixed stations and interconnected via wireless links to form a multi-hop network. Typical and inexpensive deployment of WMNs use some MSSs (Mesh Subscriber Station) and one MBS (Mesh Base Station), where their multi-hop feature can be utilized to increase their range of accessibility in rural areas effectively. Moreover, since they are dynamically self-organized and self-configuring, these networks turn to be more reliable.

TDMA-WMN (Time Division Multiple Access-WMN) is a special WMN which has some special features: TDM (Time Division Multiplexing) is adopted between MSSs and the MBS to access the air interface; frames are defined and divided into some equal duration subframes to provide better timing and synchronization to MSSs. As these subframes (called transmission opportunities) are taken by MSSs to transmit packets on unidirectional links, it's more preferable to schedule each link rather than each node connection.

Four sources of interference are defined in TDMA-WMNs:


The first three sources of interference are known as first hop conflict (primary conflicts) and the fourth one is knows as second hop conflict (secondary conflicts). Second hop conflict is disregarded in most of the presented works [1], [2].

© 2012 Naeini and Movahhedinia, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Application of Genetic Algorithms in Scheduling of TDMA-WMNs 5

To provide QoS support for delay sensitive traffic over WiFi, IEEE 802.11e introduces two types of channel access methods: EDCA (Enhanced Distributed Channel Access) and HCCA (Hybrid Coordination Channel Access). Since the HCCA function deployed in the MTs is essentially designed to meet the negotiated QoS requirements of admitted flows, we apply

The chapter is organized as follows: in sections 2, some research activities in scheduling mechanisms in WMNs and IEEE 802.11 are summarized. In section 3, we introduce an overview of IEEE 802.16 and IEEE 802.11(e) standards. QoS comparison between IEEE 802.11 and IEEE 802.16 mesh modes are described in this section. Fourth section is devoted to describe the system model. In this section we introduce the basic assumptions of the system and formally describe the system. In fifth section, the genetic algorithm is briefly described and its application to our problem is discussed. The proposed method is evaluated using simulation results in section 6. Finally, conclusions are drawn in seventh

Centralized scheduling mechanism in WMNs has been investigated in [1], [2], [5]-[10], [32- 33]. Most of the research activities in this area are not suitable for TDMA mesh networks (e.g., IEEE 802.16d). They consider only primary conflicts in which the connections share a neighbor, while TDMA-WMN is faced with secondary conflicts where the transmitter and

The main algorithm in IEEE 802.16d finds a link ranking during a breath-first traversal of the routing tree. This algorithm has no idea for spatial reuse in the network. Spatial reuse in these networks has been investigated in [5], [7]-[10]. Ref. [9] uses Transmission-Tree Scheduling (TTS) algorithm that is based on graph coloring. This algorithm don't consider the protocol overhead of TDMA scheduling. While [10] uses the load-balancing algorithm to increase spatial reuse, [8] considers Bellman-Ford method for both spatial reuse and minimum TDMA delay. These schemes don't take into account the underlying network behavior which can affect scheduling of traffic flows of other MSSs. On the other hand, these

algorithms shrink the link duration when the frame is short for scheduling the links.

Application of intelligent scheduling methods in wireless mesh networks has been inspired by the fact that finding a schedule in TDMA scheduling is *NP*-complete [11]. Ref. [12] uses fuzzy hopfield neural network technique to solve the TDMA broadcast scheduling problem in wireless sensor networks. Artificial neural network with reinforcement learning has been introduced in [13] to schedule downlink traffic of wireless networks. A genetic algorithm approach is used in [2] to find the schedule related to each link in a WMN. Here again, their scheduling method merely considers the traffic flown on the links; however, how these links

None of the above research activities, consider neither the underling network behavior nor the types of traffic streams flown on the links. Our system model is different from the

the receiver share a neighbor, which can hear both transmissions.

empty their queues has not been elaborated.

this function to the WiFi network [3], [4].

section.

**2. Related works** 

**Figure 1.** A TDMA-WMN with its conflicting links

A major challenge in WMNs is to provide QoS support and fair rate allocation among data flows. Almost all of the routing and scheduling algorithms presented in the literature have one common weak point: when the MBS collects requests larger than the frame length from all the MSSs, these algorithms shrink link durations to fit in the frames. Scaling down the link durations may cause some drawbacks in guaranteeing the QoS requirements of voice and video traffic. Two schemes can be exploited to overcome this problem: 1) A call admission control mechanism can be deployed to avoid link duration shrinkage; 2) A new scheduling method may be proposed to schedule the packets received from the underlying network. In this chapter we focus on the second solution with respect to the first solution.

Scheduling in WMNs is divided in two categories: centralized and distributed scheduling. In centralized scheduling, there exists one MBS and the other stations (MSSs) relay packets of other stations to/from end points (in this chapter we call these end points as MTs, while MSSs assume to be fixed). The main purpose of this chapter is related to centralized scheduling and admission control.

On the other hand, rapid growth of wireless networks has commenced challenging issues in co-deployment of various technologies including WiFi, WiMAX. While WiFi networks are very popular for providing data services to Internet users in LAN environments, WiMAX technology has been adopted for MAN networks to provide urban accessibility to hot spots or end users. These two technologies seem to be competitors; however, they can interwork to gain metro-networks performance, cost effectiveness and coverage area. This configuration can be used in TDMA-WMNs, however when the same frequency band is employed with different network elements (e.g., the U-NII frequency at 5GHz may be shared among IEEE 802.16d and IEEE 802.11a or IEEE 802.11n), more complex strategies are required for scheduling and packet translation from one technology to another.

In this chapter, with respect to the interoperability of WiFi and TDMA-WMNs networks, we develop a scheduling and admission control mechanism among data flows such that the QoS requirements of delay sensitive traffic types can be provisioned and elastic traffic types get a fair duration of bandwidth.

To provide QoS support for delay sensitive traffic over WiFi, IEEE 802.11e introduces two types of channel access methods: EDCA (Enhanced Distributed Channel Access) and HCCA (Hybrid Coordination Channel Access). Since the HCCA function deployed in the MTs is essentially designed to meet the negotiated QoS requirements of admitted flows, we apply this function to the WiFi network [3], [4].

The chapter is organized as follows: in sections 2, some research activities in scheduling mechanisms in WMNs and IEEE 802.11 are summarized. In section 3, we introduce an overview of IEEE 802.16 and IEEE 802.11(e) standards. QoS comparison between IEEE 802.11 and IEEE 802.16 mesh modes are described in this section. Fourth section is devoted to describe the system model. In this section we introduce the basic assumptions of the system and formally describe the system. In fifth section, the genetic algorithm is briefly described and its application to our problem is discussed. The proposed method is evaluated using simulation results in section 6. Finally, conclusions are drawn in seventh section.
