papers on mesh networks

7/24/2019 papers on mesh networks

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1030 M. Jahanshahi, A. Talebi Barmi

offered, considering the joint channel assignment and the multicast tree construction

problem.

Keywords Wireless mesh networks Multicast routing Multi-channel multi-radio

Channel assignment Softcomputing

Mathematics Subject Classification 68M12

1 Introduction

Wireless mesh networks (WMNs), which are known as community wireless net-

works, are one of the main topics for the fourth generation of wireless mobility

technology. They include a bunch of static wireless routers, which provide a networkfor end-users to IP-based services to bring high quality service to end-users. While

networks like Ad hoc are mobile, WMN backbone is fixed. Moreover, in WMNs there

is no restriction on nodes power usage, unlike Ad hoc and wireless sensor networks.

Wireless mesh networks introduce a new type of network that has been applied

in the last few years. This is a self-organized and self-configured network, which

means that the node in the network automatically establishes and maintains mesh

connectivity among each other to bring a number of benefits to WMNs. The main

elements of a WMN contain wireless mesh routers, wireless mesh hosts, and access

points that can play two different roles, which are Internet routers and wireless meshrouters. The mesh routers in a WMN are fixed and steady. Mesh routers form the

wireless mesh backbone, connect mesh hosts to other mesh hosts or mesh hosts to

the Internet through access points. The mesh routers provide multi-hop connectivity

between clients themselves or between clients and the Internet. On the other hand,

mesh hosts can be fixed or mobile and can form a wireless local area network or

mobile Ad hoc network, and provide a connection to the mesh routers. Figure1below

presents the structure of WMN elements:

To set up a WMN in a city, it is preferable to embed and install wireless mesh

routers on the roof of buildings to provide a wireless coverage in a zone of the city

[2]. Figure2a demonstrates a typical preview of WMNs deployed in a new town.

Wireless mesh networks can also be deployed in a rural area, covered by mountains

and trees while establishing a wired connection to nodes in these types of environments

would pose greater difficulties. Figure2b below demonstrates a typical application of

WMNs deployed in a rural area.

A WMN is a self-organized and self-configured network, which means that the node

in the network automatically establishes and maintains mesh connectivity among each

other and brings several benefits to WMNs such as low installation cost, large-scale

deployment, reliability, and self-management [3]. Some of the advantages of WMNs

are that WMNs need low initial investment and are not as costly as wired networks are.

Also, extensive coverage areas are covered by WMNs, which makes WMNs scalable.

Another advantage is that they are easy to deploy and expand, and have less complexity

compared to other technologies [3]. Moreover, WMNs can handle faults easily, which

makes them fault-tolerant.

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Multicast routing protocols 1031

Fig. 1 Architecture of wireless mesh network[1]

Fig. 2 Deploying a WMN in different residential areas[1]

One of the most important developing issues in wireless mesh network is multicast

routing. Multicast is a key technology that provides communication of data in an

efficient way. It is a type of communication between many of nodes that disseminate

data from a single source node to a set of destinations in an efficient way [4]. Its

mechanism is that the messages are transferred from a link to another in the network

only once and then it will be duplicated at branch points and will be disseminated to

the different destinations. Most of the existing works on WMNs focus on the measures

of single-radio and single-channel, while the multicast routing has been used recently

to increase the efficiency of WMNs.

While multicast is needed to support many important applications, development and

research on multicasting in WMNs is still in its infancy. Every effort is made to scale

this study into one of the important potential capabilities of WMNs, which is taking

advantage of using different channels and radios. This has led to a new generation of

WMNs, Multi-Radio Multi-Channel (MRMC) WMNs. By applying multiple radios

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and multiple channels in WMNs, interferences have reached their minimum limit and

an increment in throughput of data transfer is the outcome of this structure.

The rest of this article is organized as follows. In Sect.2the preliminaries of multi-

cast routing algorithms in WMNs is investigated with respect to different approaches

of multicast routing. Section3is dedicated to different scenarios for multicast routingin WMNs. In Sect. 4 the problem of Joint Channel Assignments and Multicast Routing

in WMNs is investigated in detail regarding its different issues. In Sect. 5 an overall

preview of some of the mostly related studies is presented. Finally, we conclude the

paper in Sect.6.

2 Preliminaries of multicast routing algorithms

Multicast routing protocols can be divided into two major categories: tree-based and

mesh-based. Studies have shown that mesh-based protocols outperform tree-basedprotocols because the network topology changes frequently and redundant routes in

a mesh network provide alternative paths in case of link crash. Therefore, finding the

optimal route has been considered as a multicast routing approach in WMNs. To have

a better understanding, first the algorithms of multicast routing need to be considered

and then we take a look at protocols of multicast routing in three different structures.

2.1 Different approaches for multicast routing

The two basic multicast routing approaches in WMNs are Shortest Path Trees (SPTs)

and Minimum Cost Trees (MCTs) [1], and in what follows they are discussed in detail.

2.1.1 Shortest Path Tree (SPT)

The SPT algorithm concentrates on establishing a tree with parsing all the receivers

such that the distance between the sender and each receiver across the tree is at its

minimum value [5]. The SPT algorithms normally minimize the end-to-end delay as

well and for computing the distance and cost in SPT algorithms, Dijkstra algorithm

is used. In SPT protocol, each router has a map of whole topology of the network

at any time. It keeps itself up to date so the routers make any needed change in the

state of connected link as soon as such a change happens. For example, imagine

that a connected link breaks down or comes up. A router immediately dispreads the

information to all its neighbors and keeps it on till every router gets the information

and updates its table of information about the neighboring routers in topology. Once a

router has the complete topology, it uses the Dijkstra algorithm to compute the shortest

route from the sender to receiver. To overcome the deficiencies of SPT algorithm, the

MCT algorithm was presented to minimize the cost of multicast tree.

2.1.2 Minimum Cost Tree (MCT)

While the SPT algorithm focuses on distance from the sender to receiver, the goal of

MCT algorithms is to minimize the overall cost of the multicast tree. By assigning

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a cost to each edge of the graph, minimum cost tree algorithms compute the tree,

which minimizes the sum of the cost of its edges [5]. The cost of a link can be the

packet transmission delay on that link, or the distance between the two routers. MCT

algorithms for multicast routing are based on the minimum Steiner tree problem, which

is the problem of finding the minimum cost edges. The total cost of correspondingSPT defined by Steiner trees is more than the total cost of Steiner tree. In longest path,

the distance between the sender and receiver in a Steiner tree is typically longer than

that in an SPT. This means that the average path length in a Steiner tree is more than its

value in an SPT so the SPT algorithm is considered to be more efficient than Steiner

[1]. However, there is always a trade-off between the path length and overall cost of

multicast tree.

Comparing the two aforementioned algorithms will provide a conclusion for their

usage in different aspects. In one aspect, SPTs are the best in an environment where

there is no knowledge about network topology. The multicast members may be dis-tributed over a very large area, such as the Internet. In WMNs, where the topology is

known and the size of network is smaller, MCTs such as Steiner trees are not difficult

to implement and will provide better performance. The reason is that their need for

bandwidth is less than the bandwidth, which is required by SPT.

In another aspect, the length of the path, which is covered by MCT algorithms,

is longer than SPT algorithms. In a Wireless Multi-hop Network, the path is longer

and because of collision or congestion, the probability of packet loss is higher, which

will lead to a dramatic decrease in throughput. It may also be argued that SPTs could

achieve higher throughput than MCTs while it is not clear how the performance ofSPTs and MCTs is in a wireless multi-hop network.

Most of the multicast routing protocols nowadays are based on SPT algorithms.

The reason for this is that SPTs implementation and establishment are easier and they

bring minimum delay from the source to each destination node, which is the most

important issue among the all algorithms through networks.

2.2 Multicast routing on the Internet

Multicast routing algorithms for the Internet can be divided into three types: shortestpath tree algorithms, minimum cost tree algorithms, and constrained tree algorithms

[5]. As discussed, a shortest path tree algorithm calculates a tree rooted at the sender

and spanning all the receivers such that the distance from the sender to each receiver

is minimum, while a minimum cost tree algorithm tries to minimize the overall cost of

the tree. A constrained tree algorithm tries to optimize on both constraints (the shortest

path and the minimum cost). Most of multicast routing protocols used for the Internet

today are based on shortest path trees because their implementation is easier and they

provide minimum delay from the sender to receiver.

2.3 Multicast routing in MANETs

This type of multicast routing protocols is divided into two categories, which are

tree-based and mesh-based.

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2.3.1 Tree-based protocols

In tree-based protocols, the transmission of data packets between the source and the

destinations are performed through the paths on the multicast tree, which helps to

minimize the usage of bandwidth. As examples of tree-based protocols, one can men-tion AMRIS (Ad hoc Multicast Routing protocol utilizing Increasing id number) [6]

and MAODV (Multicast Ad hoc On-Demand Distance Vector routing) [7]. ODMRP

(On-Demand Multicast Routing Protocol) [8] and CAMP (Core-Assisted Mesh Proto-

col) [9] are samples of mesh-based protocols with ODMRP being more popular than

CAMP because of its simplicity.

2.3.2 Mesh-based protocols

In mobile Ad hoc networks and mesh networks, the network topology changes con-stantly. To solve this issue, whenever a route breaks down, it will be replaced by

another route to provide packet transmission and mesh-based uses tree-based proto-

cols for packet delivery ratio and throughput. In mesh-based protocols more than one

tree is used so the packets can be delivered to each receiver through multiple paths.

The advantage of this mechanism is that the alternative paths increase the protection

against the topology changes[1].

3 Different scenarios for multicast routing in WMNs

The transmission of a data packet from a node to its neighbor in multicast routing can

be performed with just one transmission in a wireless environment and in this case,

having the minimum cost of tree is the important issue. In this case, in multicast routing

in WMNs, NP-completeness is the major problem of minimizing the transmissions.

The problem is that while these algorithms save bandwidth, still the SPT algorithm

is the preferred one due to the fact that the packet loss would occur more in longer

ranges. Beside that, joining other incoming nodes to the optimal tree would change

its structure, and not being optimal, it should be rebuilt. The most tragic case happens

when nodes join and leave the tree frequently.

As explained, the WMNs include wireless mesh routers, which are the main concept

of mesh backbone and the importance of mesh backbone role is that wireless mesh

hosts are located at the edge of them. This backbone is linked to the Internet via access

points and these access points are considered as traffic control gates between Internet

and WMNs. The sender and the receiver of a multicast group are Internet host and

mesh host, which are the major issues of multicast routing in WMNs. According to

the role of Internet hosts and mesh hosts in sending and receiving data, two methods

are defined to build up the multicast framework. In the first method, the sender is an

Internet host and in the second method, the sender is a mesh host. In both methods,

depending on the type of sender, the receiver would be an Internet host or a mesh host.

In methods, which the sender is a mesh host, the mesh host sends a message to its

linked access point and asks to start a multicast group by giving the access point the

multicast group ID and its IP address. Access Point then multicasts the information

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Fig. 3 Sender is Internet host and Receiver is Mesh host[1]

to the other access points using wired connections. The purpose of these algorithms

is to make the interactions between mesh host and Internet host as simple as possible.

These methods are explained as below[1]:

3.1 Sender is Internet host and Receiver is Internet host

In this case, the Internet host is linked to multicast group. After establishing this

connection, without any change to IP multicast or routing protocol, all the protocols

of multicasting in internet can be applied to prepare receiver for getting data packets

from sender.

3.2 Sender is Internet host and Receiver is Mesh host

In this method, before making the connection, the mesh host sends a join request to

the connected access point. If the access point is ready to serve the mesh host, it sends

a message as an acknowledgement to the mesh host to inform the mesh host that the

access point will be ready to be the root of multicast tree in backbone. Then, the sender

begins to send packets of data to the access point, which in turn, receives them, and by

using the established tree, multicasts them to mesh host. Figure 3shows an example

of this method. In one side, there is a session and Internet plays the role of a sender in

this session. The sender has to send a message to its serving access point (AP). The

access point then send the message to its neighboring access point through a wired

connection in the mesh backbone. In the other side, there are four mesh hosts, which

are considered as receivers. Suppose that AP1 wants to be the source of multicast tree.

In this case, it becomes the root of the first tree and its nodes are R1 and R2. R1 and

R2 receive data packets from AP1. A similar trend happens for AP2, R3 and R4.

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Fig. 4 Sender is Mesh host and Receiver is Mesh host[1]

3.3 Sender is mesh host and Receiver is mesh host

In this type, there is a need to first build a multicast routing tree which sender takes

place as the source of this tree. Then a join request will be sent by receiver to its related

access point, which has the multicast group ID. Access point responds to message and

informs the receiver about the senders IP address. After receiving the reply, receiver

requests the SPTM protocol to build a root from the sender to the receiver. Figure 4

provides an example of this method. The sender first sends a message to AP2 to give

the information about the multicast group ID and its IP address and frequently, this

information is provided for AP1 from AP2. The serving access point of R1 is AP1, so

when R1 decides to connect to multicast group, it sends its request to AP1. In response

to this request, SPTM protocol, builds a path from S to R1.

3.4 Sender is mesh host and Receiver is Internet host

In this method of multicast framework, a session would be defined for receivers to

perform the multicast. The access points are considered from the source of these

sessions. The sender sends data to access points and through the wired connections,

access point multicast these data to receivers [1]. In Fig.5, the sender S uses AP2 as

its serving access point, so AP2 would become the source of multicast session. The

receivers of AP2 are R3 and R4 in the Internet. Therefore, S sends its data to R3 and

R4 using wired interface of AP2. Meanwhile, AP2 has another role as a receiver in

the network of multicast group in WMNs.

4 Joint channel assignments and multicast routing problem in WMNs

Implementing multicast routing in order to improve the performance of WMNs was

discussed. However, the purpose of mentioning multicast is to introduce one of the

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Fig. 5 Sender is Mesh host and Receiver is Internet host[1]

most effective techniques to increase the performance of WMNs, which is Channel

Assignment. As will be explored, one of the most effective approaches to increase

the utility of WMNs is using multiple channels and multiple radios. A specificationof IEEE 802.11, IEEE 802.15 standards is that they provide more than one channel

for transmitting data [10]; therefore allowing transferring data on multiple channels in

each node can be considered as a way to improve the performability of the network.

One of the most effective approaches to increase the overall network throughput is to

apply systems with multiple channels and multiple radios in each node, which will have

a great impact on efficiency of WMNs. Several algorithms have been under study to be

implemented on multicasting. Many efficient multiple channel multicast algorithms

have been proposed in using multiple channels and multiple radios in WMNs. An

effective algorithm first establishes a multicast tree by reducing the number of nodes,

and then in tree, the channel assignment is performed to the interfaces of each router

to reduce interference. The implementation of tree is easy and simple, while choosing

the right nodes should be considered as well. Figure6a shows the network topology,

where nodes specify mesh routers and multiple interconnections link them together.

In this example, node a is the sender and nodes g, h and I are receivers. Figure

6b indicates the multicast tree from the source to the receivers.

Existing multicast routing algorithms and CA algorithms believe in the hypothesis

that the membership of multicast tree is static [12,13], which means group membership

is constant, whereas, it is dynamic in practice, and changes in group membership are

allowed, i.e. members are allowed to join or leave the group [14].

The multicast coupled with the implemented channel assignment can be done using

different algorithms. The purpose of all of them is to increase the throughput of the

network. To do this, two different schemas of categorization of multicast routing are

presented. The first category takes advantage of multicast ability for applying multi-

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Fig. 6 An example of multicast tree construction and channel assignment [11]

ple channels and explains the three classes of channel and radio assignment includ-ing Single-Radio Single-Channel (SRSC), Single-Radio Multi-Channel (SRMC) and

Multi-Radio Multi-Channel (MRMC) WMNs. The second category is classified into

three particular schema including Heuristic, Meta-Heuristic and Mathematical Opti-

mization. A triple classification for channel assignment problem is also provided for

multicast tree in MCMR WMNs, which defines the sequence of channel assignment

and establishment of multicast tree one after the other or in a conjoint way. In what

follows, we will analyze the first classification, which is related to channel and radio

assignment.

4.1 Classification with regard to channel-radio association

Wireless mesh networks are in form of multi-hop wireless networks and the simplic-

ity of deploying wireless networks has led to a significant usage in all environments.

Consequently the growth of usage of WMNs has brought distinctive increment of

demands for supporting more users. Therefore, one of the most important issues in

WMNs is capacity. There are two main problems with WMNs. The first problem,

as was described before, is the lack of capacity, which is caused by the interference

between multiple contemporary data transmissions. This happens when two wireless

links communicate on the same frequency and therefore will not be able to transfer

data. It will lead to a reduction in throughput of each link because of the interferences.

The second problem is the fact that a router cannot transmit and receive simultane-

ously with a single radio. To overcome these problems, mesh routers are prepared

to work with multiple radios, which can be configured to work on different chan-

nels.

Interference limits the number of users a wireless network can support. The current

IEEE 802.11 standard for WLANs has this ability to provide several channels to deal

with the issue above but the use of multiple channels requires specifying which par-

ticular channel to use for a particular transmission[10]. Channels need to be assigned

accurately to reduce interference in the network. The channel dedication can be done

in two modes such as Dynamic channel assignment and Static channel assignment.

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Dynamic channel assignment: The first method is to frequently change the channel

on the interface for each packet transmission. In another word, dynamically switch

channel on the radio interface. This requires channel switching at a very fast time. The

fast channel switching bring difficulties and problems with use of hardware because

channel switching delays is in milliseconds but still higher than packet transmissiontimes.

Static channel assignment: The second way is to adopt a multi-radio solution, where

different channels have dedicated statically, to multiple radio interfaces. Dynamic

channel assignment had some difficulties with its hardware. To overcome this issue,

the static channel assignment was proposed. In this mechanism when the data load is

high and considerable, some changes can be made to channel assignment to adapt it

to accept the traffic load.

Many studies concentrate on how to assign channels to nodes in the network, eitherby the static or the dynamic methods [4750] and most of them believe that static

assignment outperforms dynamic assignment due to the channel switching cost and

the delay.

In addition to above classification of channel assignment, there is another classifi-

cation, which consider channel assignment locality and channel dedication range in

wireless mesh networks. This classification consist of two applied approaches, includ-

ing Centralized channel assignment and Distributed channel assignment.

Centralized channel assignment: In centralized approach, there is a central controller,called Base Station (BS), which has a global knowledge about all nodes in the network

and is responsible to manage other nodes in entire wireless mesh network. To manage

other nodes and allocate resources to them, at first the BS node collects requests from

all other nodes and based on the information collected, it calculates and distributes the

information to all other nodes. Centralized algorithms are quite practical in managed

mesh networks, where there is already a central node. In addition, they try to obtain

a higher degree of optimization by using thin clients [32]. While centralized algo-

rithms provide collision-free packet transmissions, but the number of applied routes is

unnecessarily reduced, because the centralized algorithm uses a tree-based topology,

instead of a mesh-based topology, therefore, it cannot utilize all possible routes [58].

In [50] and [55], a centralized channel assignment and routing algorithms have been

presented. In the presented algorithm, at first, network interfaces have been used in a

specific method and then a channel is assigned to the interfaces of both end nodes.

Distributed channel assignment: In the other hand in distributed mode, the network

is divided into clusters. Each cluster has a BS node and the other nodes are subscriber

stations (SS). In this approach, there is no node with global knowledge about the

entire network. Instead, each node has a local knowledge of itself and its neighbors.

In distributed algorithms, nodes communicate with each other, usually by passing

messages and collect information and base their decisions on this local information.

This method uses a mesh topology and exploits all possible routes. In distributed

algorithm, a WMN node decides how to bind its interfaces to neighbors and assign

channels to these interfaces without global coordination [49]. One of the advantages

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of distributed approach is that in this mode, network bandwidth can be more efficiently

utilized. In[49], distributed channel assignment and routing algorithms are presented.

In their presented algorithm, the distribution of information is performed by applying

gateway nodes. Each node joins a gateway node and sends the packets to the wired

network to that gateway.In [54], it has been proved that because the problem of finding optimal solution is

a NP-complete problem, finding the optimal link set in routing graph of WMN with

centralized algorithms is not efficient enough in large-scale networks. According to the

investigations in [50], the centralized channel assignment and routing algorithm does

not perform much better than the distributed versions. This shows that the performance

loss due to distribution of intelligence is very small. A centralized solution is not

always the best approach because of a single point of failure, and it does not scale

well. In the other hand, the methods of assigning channels in a distributed mode

are quite various [56]. In addition, several distributed algorithms can provide moreeffective performance in such networks, but while they increase the overall network

throughput, they are incapable of reserving the resources accurately to provide best

quality of service. In[57] a scheme is presented, which try to prove that clustering

nodes in WMNs is not a preferred approach, because managing clusters in a distributed

system is a difficult issue.

While channel assignment is an efficient method for network communication in

WMNs, two important issues should be considered at the same time in such a network

implementation. The first is to locate the neighboring nodes within the transmission

range of each other and make a clean and utilizable connection, and the second is thatthe existence of a common channel assigned to the radios of both nodes.

Current mesh networks, which use 802.11 based network cards, are typically

designed and configured to operate on a single channel using a single radio. In a

multi-radio mesh network, every node can be equipped with only a few radios. Radios

operating in the same frequency band will interfere with radios close to them. Because

there are only two frequency bands (2.4 and 5.2 GHz) for use by 802.11, a node is

limited for using only two radios [11]. Therefore, it is predictable for a multi-radio

protocol to operate properly on a dual radio mesh network but also has the ability

to handle more than two radios per node. By deploying multi-radio routers in mesh

networks and assigning the radios to channels, the routers can make synchronized

communication with minimal interference, so the capacity of a mesh network can be

significantly increased. This usually needs time synchronization between nodes in the

network. Multi-radio can be realized without complexity of time synchronization but

its problem is that it needs additional hardware. The structure of the wireless hardware

is such that a particular frequency spectrum can be used to transfer information at the

same time because nodes operating on different frequencies, cannot communicate with

each other. In case that a second source tries to use the hardware, a collision will hap-

pen and data packets would be dropped. Another advantage of multi-radio networks is

the ability to use non-overlapping channels in the same region and frequency. In fact,

there is less collision between multiple flows in a multi-radio mesh network, which

would lead to higher throughput.

Beside the multiple radios, multiple channels can be used in different ways. Ability

of applying multiple channels requires the identification of which channel to use

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for a specific data transmission. Channels need to be assigned accurately to reduce

interference in the network.

4.1.1 Multicast routing in single-radio single-channel WMNs

Traditional multi-hop wireless networks (which have been studied as packet radio net-

works) were a perfect example of single-radio single-channel WMNs. It was obvious

that in such networks, as the number of communication hops grow, the end-to-end

throughput on a single route falls. The main reason for this issue is due to the fact that

a single wireless transceiver operates in half-duplex mode and it cannot concurrently

transmit and receive. Therefore, a frame should be completely received, before the

node can change its state from receiving mode to transmitting mode. As mentioned

before, current mesh networks, which use 802.11 based network cards, are typically

designed and configured to operate on a single channel using a single radio. How-ever, the network took advantage of applying several channels and radios in WMNs.

As an example of this category, in[15] a method for multicast tree construction has

been proposed in which channel assignment is not considered and multicast routing

details were not mentioned. The authors have tried to optimize the shortest path tree

(SPT) with regard to edge cost using interference and transmission rate. In addition,

a hybrid method is presented for multicast routing in SRSC WMNs [ 10], in which

a multicast proactive method for routing across the network backbone together with

a multicast reactive method for communication between client and access points is

proposed. There are also many other researches in the context of multicast routing insingle-radio single-channel WMNs such as [4,16,17] and [18]. Particularly, in[18] a

cross-layer method has been proposed considering the impact of channel assignment

in MAC layer and multicast routing in the network layer. The concept of cross-layer

design is based on architecture where different layers can exchange their information

to improve the overall network performance.

Also in[17], a new multicast tree construction algorithm with maximum traffic

flow and minimum delay for SRSC has been proposed. In their study, the problem of

multicast is formulated as a Linear Programming (LP) and also a cost function (CF) is

defined to choose the route with minimum traffic among the other routes. In addition,

a Minimum Delay Maximum Flow multicast (MDMF) algorithm is proposed to solve

this problem using CF. The performance of the proposed algorithm is evaluated and

the outcomes show that the proposed algorithm has fewer number of transmissions

for a given function and has higher throughput and less latency compared to other

algorithms in this category.

Despite all the mentioned researches in the scope of SRSC, a specific and new

method has been proposed in [19], which uses CMAC (cerebellar model articulation

controller) neural network model to recover the network even before occurrence of any

likely fault event to control congestion and losses by predicting the probability of path

optimization problems such as routes disjoint and nodes disconnection. Moreover, a

new QoS multicast routing framework for WMNs has been proposed in their paper

to solve the problem of load balancing and to increase the multicast communication

quality between Internet hosts and Mesh hosts. They also presented a hybrid multicast

protocol to use proactive and reactive multicast routing simultaneously to remove

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delays and blocking and decrease control overhead to provide routes between mesh

routers.

4.1.2 Multicast routing in single-radio multi-channels WMNs

While the mesh mechanism provides more adaptive interference management and

power supervision, using multiple channels remarkably increases the potential for

channel allocation and route selection. As stated before, wireless interference is a

critical restriction on applications of WMNs. Applying channels at nodes next to each

other for sending and receiving, can distinctly improves the throughput by decreasing

interference to its minimum value. However, it should be mentioned that applying

multiple non-overlapping channels or even multiple radios is not the only way for

improving data throughput in WMNs. Moreover, using directional antennas, which

decrease the interferences of signals would be another method to increase the totalthroughput of links but this is not covered in this study. In [20], the focus is on

designing distributed multicast solutions for the problem of throughput maximization

in single-radio multi-channel WMNs.

4.1.3 Multicast routing in multi-radio multi-channel WMNs

As discussed before, one of the most efficient methods to increase the overall network

throughput in WMNs is to use multiple channels and multiple radios for each node

because the traditional multicast routing algorithms such as the shortest path tree(SPT) and minimum cost tree (MCT) (which are covered in Sect. 2), do not consider

the potential of wireless broadcast communication in capability of providing channel

assignments in a multi-radio multi-channel wireless mesh network. Figure 7 shows the

applying of multiple channels and radios (MCMR) model, in which each node consists

of m radios and each radio can work on n operational channels. These channels

may be completely separated (orthogonal) or either interferes with each other, such

that a channel partially overlaps its spectrum with the contiguous channels.

As it was mentioned before, IEEE 802.11 standard provide more than one channel

for transmitting data. If this standard uses 2.4 GHz frequency band, then there willbe 11 channels available for transmitting data, numbered from 1 to 11. Based on

frequencies spectrum defined for each channel, orthogonal channels are separated

from each other and can be dedicated to radios with minimum interference[13]. For

Fig. 7 Applying multiple radios and multiple channel [13]

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Fig. 8 An example of

ascending channel allocation

algorithm[12]

instance, an algorithm for channel allocation, called Ascending Channel AllocationAlgorithm, has been presented in [12]. The main idea of this algorithm is to assign

channels to interfaces from top to down in an ascending order (for example from

channel 0 to channel 10). As the maximum channel number is reached, start it again

from the first channel. This approach prevents a common channel to be assigned to

two nearby interfaces, which interfere with each other. Figure8shows an example of

this algorithm, where the orthogonal channels applied in this example are channels

0,1 and 2.

Each node in a MCMR WMN can send data on one channel and receive data on

another one simultaneously by using two different radios. Therefore, a MCMR WMNat least doubles the throughput, since each node is able to operate in full-duplex mode.

While it is obvious that applying multiple channels will increase the overall network

throughput in WMNs, but MCMR WMNs need efficient algorithms to determine

which channel a link should use for data transmission, in order to minimize channel

interference for maximum throughput. Reported works in [13,2128] and [29] have

performed investigations in this scope.

As discussed before, existing multicast routing algorithms and CA algorithms

believe that group membership of multicast tree in MCMR WMNs is constant,

whereas, it is dynamic in practice and members have the permission to join or leave the

group[14]. In [14], an evaluation of multicast nodes membership in MCMR WMNs

has been performed in order to make the group membership of a multicast tree such a

dynamic membership. Therefore, the nodes are allowed to join and leave the multicast

tree without threatening the effectiveness of the CA. Thus, to reach that objective,

they proposed two algorithms to make a multicast tree dynamic: Node Joining the

multicast session algorithm (NJM) and Node Disjoining the multicast session algo-

rithm (NDM). The authors have presented these algorithms in order to increase the

throughput while keeping the number of nodes to a minimum.

Moreover, an algorithm called Breadth First Search Channel Assignment (BFSCA)

has been introduced in [30]. The BFSCA is a hybrid channel assignment algorithm that

uses multiple radio interfaces to increase the throughput and decrease the interference

within the communications of nodes and their neighbors in wireless mesh network.

Figure 9 below shows an example of multicast tree construction in multi-radio multi-

channel WMNs. The node MS stands for a multicast source, which transmits data to

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Fig. 9 An example of multicast tree construction in MCMR WMNs[21]

multicast targets. MT1 to MT4 might be laptops, cell phones, PDAs, or even a sensor

node. The numbers printed next to the links indicate the channel-radio associations

for each link[21].

While using multi-channel mechanisms decreases interferences in communica-

tions, it, at the same time, increases new challenges for optimizing the network. In

most published relevant articles such as[31,32] and certain others, multicasting prob-

lems were studied based on how to mitigate the interferences or the network overall

interference. The important challenge is to use an algorithm with the least interference

in order to provide higher performance. In[21], a mathematical formulation for joint

channel assignment and multicast routing has been proposed in MRMC WMNs. The

work in this article is centered on promoting the adoption of cross-layer design for

joint channel assignment and multicast tree construction problem in MCMR WMNs.

Moreover, the authors of this paper have presented their result of channel assignment

in both centralized and distributed manner. Their focus is on optimization of chan-

nel interference and tree cost. Moreover, in [33], centralized multicast throughput

optimization in MCMR WMN is modeled as an integer linear programming (LP).

In addition to above investigations, an optimization problem has been introduced

in[34] and[35], illustrating the efficiency of cross-layer design in MCMR WMNs,

which discuss how to build a multicast tree while the number of mesh clients is

maximized. The aforementioned problem is referred to as the maximum-revenue and

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delay-constrained multicast (MRDCM) problem[34]. In [34], the MRDCM has been

mathematically formulated and an optimization algorithm called cross-layer and load-

oriented (CLLO) has been proposed for dealing with that problem. CLLO algorithm

is developed based on the cross-layer design paradigm and considers benchmarks

such as application demands, multicast routing, and channel assignment during theformation of a channel-allocated multicast tree. The experimental results show that

due to the fact that the information of higher layers can be used to perform routing

selection and channel allocation concurrently, the proposed CLLO algorithm has better

performance than the layered techniques, and the number of serviced mesh clients and

the throughputs is higher in CLLO. Compared with the layered approach, the cross-

layer design has been considered to be a more effective method for designing multi-hop

wireless network protocols for meeting QoS requirements [36,37]. In [37], some open

issues and challenges have been under evaluation in order to complete and optimize

the ongoing cross-layer design. While cross-layer design brings many advantages, itstill suffers from high complexity, which prevents optimal performance gain.

Despite of interferences decrement in WMNs, particularly in MCMR WMNs, there

are still other unsolved issues in such networks. The distribution of traffic load in

WMNs is mostly unbalanced. The disproportionality of load has led to a non-optimal

multicast tree routing. By distributing traffic among the nodes in a fair way, not only the

number of transmissions would decrease, but also the interferences between channels

would be declined as well. In [38], an algorithm called LMTR (load-balanced multicast

tree routing) has been proposed which provides balanced multicast trees using the

defined cost functions. They also demonstrated how their proposed scheme couldmanage the trade-off between load balancing and delay. When congestion happens in a

part of the network, the traffic should not be routed to that part and a load-aware routing

mechanism should be applied. Based on their study, the LAMTR prevent the routing

blockage on nodes particularly those close to the gateways and decrease the traffic load

in mesh routers. Moreover, a centralized channel assignment has been conducted in

[39], which investigates the challenges of providing efficient multicast communication

over WMNs. They proffer new algorithms with purpose of load balancing in order to

increase the QoS in the multicast communication over WMNs.

4.2 Classification with regard to optimal solutions

This category is classified into three groups of algorithms. The first class is dedicated

to Heuristic optimization algorithms; the second one is Meta Heuristic optimization

and the third class is Mathematical optimization. These three algorithms provide an

acceptable performance and their strong search capabilities can lead to finding routing

trees with low, cost low interference in MRMC WMNs. The definitions of these three

algorithms are as below.

4.2.1 Heuristic-based optimization methods

Greedy algorithm is one of the main methods of heuristic algorithms. A greedy algo-

rithm is a set of decisions and each of them assigns a channel to a link. This process is

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continued until all links have been assigned to channels. The first step of each decision

is to select an unassigned link to be assigned to a channel. In the second step, a channel

is assigned to a chosen link. By using partial overlapping in a greedy algorithm, the

network interference is reduced [22].

In heuristic methods, to solve the problems of an efficient multicast routing in awired network, several algorithms had been studied and developed. In [34], a heuristic

approach named CLLO algorithm has been proposed for the problem of maximum-

revenue and delay-constrained multicast (MRDCM), which concern how to construct a

delay-constrained multicast tree on an MCMR WMN in order to achieve the maximum

number of mesh clients and support concurrent interference-free transmissions. There

are other studies, which have been recommended for wired networks as well, such

as DVMRP (Distance Vector Multicast Routing Protocol)[40], MOSPF (Multicast

Extensions to Open Shortest Path First)[41], PIM (Protocol Independent Multicast)

[42], and CBT (Core-based Tree) [43]. On the other hand, some multicast routingprotocols have been recommended for wireless networks such as AMRIS [6], MAODV

[7], ODMRP [8] or CAMP[9]. Moreover, in [38] a heuristic cost function in multi-

channel multi-radio WMNs, which consider load balancing, has been presented to

decrease the number of transmissions. Also a heuristic evaluation has been performed

in[14] with the purpose of minimizing the number of relay nodes and communication

delay by proposing two algorithms in which the first one allows a node to leave the

multicast tree and the second one allows a node to join the multicast tree, and based

on the topology changes of the multicast tree, channels will be assigned again.

However, while they are all designed for wireless environments, they do not workefficiently in WMNs because they assume that nodes are mobile and can be moved

anywhere while mesh routers are static.

4.2.2 Meta-Heuristic-based optimization methods

Because the multicast routing problem is an NP problem, the mathematical solutions

are not scalable; therefore, the intelligent solutions will not lead to sub-optimal in

polynomial time.

Neural network: Neural network algorithms are basically used to predict the WMNs

traffic and decrease the congestion. Although neural network model has strong self-

learning and self-adaptive ability, but it has some crucial deficiencies, such as slow

convergence as well as receiving only local sub-optimal solution. Many studies have

investigated the WMNs improvement based on neural network algorithm. Specifically,

in [19], a neural network model called CMAC (cerebellar model articulation controller)

has been proposed, which is one of the most promising methods to control congestion

and traffic oscillations by predicting the probability of routes disconnection to achieve

more reliability of the network. The purpose of their work was to achieve a load

balanced network for higher QoS in SRSC WMNs. The main idea of CMAC is based

on associative memory, which includes associated inputs and outputs. The CMAC

follows the function of a cerebellum. The input space is divided into separated states

and memory partitions will be associated with each state to store information for

that state. The retrieved data from the associated memory partition will compose the

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Fig. 10 An example of

chromosomes of a multicast tree

[27]

output. In fact, CMAC is categorized into three phases. In the first phase, the input

vectors would be the output of input space to conceptual memory, which is called theconceptual mapping. In the second phase, the output of conceptual memory would be

the input of actual memory, which is called random mapping. Finally, in third phase,

the outputs of random mapping would be summarized and constitute the final output

of CMAC. The most distinctive advantage of the CMAC neural network is its fast

learning speed and excellent convergence specifications.

Genetic algorithm (GA): Genetic algorithms are probative search algorithms based on

the ideas of natural selection and genetic evolution. The main idea of GAs is to resemble

the process of evolution in the natural system and to specify the better structure [27].

In fact genetic algorithm is a kind of meta-heuristic optimization method; intend to

implement the biological principles of Darwinian theory of evolution and Mendelian

inheritance principles [51,52]. In fact, GA algorithm tries to find the best solution

from a set of candidate solutions. These candidate solutions are called Chromosomes,

which are generated from genetic mutations and corresponds to a potential solution.

Figure10 shows an example of chromosomes array of a multicast tree, in which the

row of array stands for candidate solutions.

The GA operations include several key components such as genetic representation,

population initialization, fitness function, selection scheme, crossover and mutation.

The initial population is composed of chromosomes and explores the genetic diversity

by using the available knowledge [27]. In [27], a general method to initialize the

population is to explore the genetic diversity and all routing paths from multicast

tree are randomly generated. They start to search a random path from sender node to

receiver by randomly choosing a node from the neighborhood of sender and repeat

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Fig. 11 Swapping paths by crossover [27]

this process of neighbor finding until they reach to receiver. Meanwhile they avoid

any loop in multicast tree in this process. Fitness function is the goal function, whichis intended to demonstrate the output of genetic algorithm. For example in [ 27], the

fitness function returns the total channel conflict of the multicast tree. Crossover and

mutation are operators, which boost the search capability of algorithm in an effective

manner. Selection aims to improve the average quality of the population by passing

the high quality chromosomes to the next generation. The selection of chromosome

is based on the fitness value achieved by fitness function. Crossover processes the

current solutions so to find more efficient ones and mutation helps genetic algorithm

keep away from local maximum value [53]. Crossover and mutation are considered as

two basic operators, which have intense effect on performance of genetic algorithm. In

fact these two operators generate new chromosomes. The structure of chromosomes

depends on the structure of algorithm. For example in [27] chromosomes are expressed

by tree data structure. In [27] they adopt single point crossover to replace partial

chromosomes. Each time they select two chromosomes for crossover. To at least one

receiver, these two chromosomes should contain at least one common node, which is

randomly selected. By swapping the partial paths from each of the chromosomes by

crossover, new chromosomes will be generated. Figure11illustrates this operation in

an example. In this example the common node is node 11 and receiver is node 13.

Crossover swap the paths (11 12 13) and (11 8 13) to generate new

chromosomes.

Finally they check if the multicast trees provided by the new chromosomes are

acyclic. Otherwise repair functions need to be applied to remove the loops.

While genetic algorithm contains complications in many concepts, but still it has

been widely applied in resolving the problems of QoS parameters in multicast routing

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Fig. 12 Assignment different

channels over a multicast tree

[27]

in several networks, and tries to solve the channel assignment problems. Many relevant

investigations have been done in the scope of GA algorithm. Particularly [27] tries

to solve the problem of channel assignment and multicast tree in a conjoint way andcompare the result of GA with several algorithms. They try to decrease the number

of links on the multicast tree, which also decrease the multicast end-to-end delay.

According to their study, before any data transfer, a channel should be assigned to

the transmission link on multicast tree, while achieving the minimum interference

of channels. The reason is that there are 11 available channels, based on channels

frequency in IEEE 802.11 and in the best state, there can be at most 3 non-overlapping

channels (which are channels 1, 6 and 11). Hence, different channels with minimum

interference should be assigned to radios linked to the same node. Figure 12 illustrates

an appropriate channel assignment to avoid dedication of interfered channels to a node.

Overall, the ability of GA algorithm on exploring better solutions has made them

unique, compared to many other algorithms and they have been remarkably applied

to solve the QoS multicast problems in several networks.

Simulated annealing (SA): Simulated annealing algorithm tries to simulate the anneal-

ing process in the physics of solids [27]. SA considers a physical system as the opti-

mization problem and its internal energy as the value of the objective function. By

this hypothesis, annealing is considered as a process of identifying a solution with the

minimum value of the objective function. In [27], achieving the optimal solution is

based on temperature changes. They start the annealing process at a high temperature.

As the temperature falls, the annealing process tries to achieve the optimal solution.

The temperature decrement continues and as it reaches the specified upper bound,

the algorithm finishes and the current optimal solution will be considered as the final

solution.

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As it is mentioned in [21], SA performs searching from one solution to another

solution of its neighbors in the solution space. Therefore, for constructing the neigh-

borhood, two methods were presented. In the first method, which is called fine-grain

adjustment, one receiver Ri is selected in a random way, and then on the path from

source to the selected receiver a node Vi is randomly selected. Then, the sub-pathbetween Vi to the Ri is replaced by a new one. In the second method, which is called

coarse-grain adjustment, initially Level Channel Assignment (LCA) and the short-

est path-based trees are generated and then channel assignment is performed. Then,

their interference is calculated and the tree with the lower interference is selected as

initial solution and its neighbors should be constructed according to the above two

methods [27]. An SA mechanism is so that it generates a neighbor of a multicast tree

by replacing its path, selecting a random backup path. Moreover, SA has been applied

to deal with the problems of QoS multicast routing also in wired networks such as the

multimedia communication networks.

Tabu search (TS): Tabu search is a meta-heuristic that provides a search procedure to

explore the solution space beyond local optimality [22]. It uses a local or neighborhood

search procedure to move from a solution to another in the neighborhood of the first

one and continue this process until some stopping criterions have been satisfied. TS

is more general and conceptually much simpler, compared with algorithms such as

GA and SA. Like SA, TS has been applied to the QoS multicast routing in the wired

networks as well, such as the multimedia communication networks[27]. In[27] the

TS algorithm moves from a generated initial solution to its neighbor. To probe moreunachieved solutions, the recently achieved solutions will be obsoleted for a next few

iterations. To free the solutions in Tabu status and continue the process of searching the

solutions, an aspiration factor will be defined. The algorithm finishes as the number

of continuous iterations reaches to defined upper bound. The final solution will be the

best solution that TS has ever visited.

Learning automata (LA): The fourth algorithm of Meta-Heuristics is Learning

Automata based Multicast Routing (LAMR). In LAMR, contrary to the described

methods above, the CA problems and multicast routing problems in MCMR will be

solved conjointly [27]. In [44] and[45], simulations have been performed and the

results show that the methods based on the GA, SA and the TS, are suffering by the

network, in terms of interference. The efficiency of them is investigated by evaluating

the characteristics of their designs in terms of optimality and complexity. On the other

hand, complexity is measured in terms of memory consumption as well as the time

required to solve the multicast problem. Experimental results demonstrate that LAMR

outperforms the LCA, MCM, GA, TS and SA-based methods in terms of achieved

throughput, end-to-end delay, average packet delivery ratio, and multicast tree total

cost.

In [45], simulations have been performed and solutions based on distributed channel

assignment have been presented. In their solution, the learning automata residing on

interfaces of each node specify which channel their associated radios should commu-

nicate with the neighbors. The proposed design includes two phases. In the first phase,

the minimal end-to-end delay paths between the multicast source to each multicast

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receiver are constructed. In the second phase, the residing learning automata optimize

the initial tree to get to a minimal interference tree. LAMR also take into account the

hidden channel problem, which typically occurs when two-hop away nodes attempt

to tune on the same channel.

4.2.3 Mathematical optimization

Unlike the two previous methods, this type of optimization method will lead to optimal

solutions. In mathematical optimization, criteria such as interference, the number of

available radios, the set of usable channels and other resource constraints at nodes

are taken into account and are formulated. This method can specifically be used for

cross-optimization of the joint channel assignment and the multicast routing problem.

According to [21], providing a mathematical framework is necessary to achieve an

optimal solution for problems such as Hidden Channel and other problems. A highlydetailed evaluation on this subject is made in [21]. In general, practical network-driven

application scenarios call for proper mathematical formulations of the underlying logic

to ensure the optimality of the configurations and of the choices made for performance-

tuning parameters. In [20], a method has been proposed, focused on designing distrib-

uted multicast solutions for the problem of throughput maximization in single-radio

multi-channel WMNs, which also intend to perform both channel allocation and estab-

lishing multicast routing in a conjoint way. Also in [21], a mathematical formulation

for the joint channel assignment and multicast tree construction problem in MCMR

WMNs has been proposed. In this article, initially single-radio multi-channel WMNsmulticast problem is formulated into a mathematical program, and then an iterative

primal-dual optimization framework has been designed.

Relevant studies in this scope have been accomplished in [44] a n d [15]. Particularly

the authors in [15] concentrate on multicast routing in WMNs and try to adapt well-

established techniques as well as developing new ones to reach an optimized route.

In their study, the problem of multicast is formulated as a Linear Programming (LP)

and also a cost function (CF) is defined to choose the route with minimum traffic

among the other routes. In addition, a Minimum Delay Maximum Flow multicast

(MDMF) algorithm is proposed to solve this problem using CF. The performance ofthe proposed algorithm is evaluated and the results show that the proposed algorithm

has a less number of transmissions for a given function and has higher throughput and

less latency compared to other algorithms in this category.

4.3 Classification regarding the order of solving the two sub-issues: channel

assignment and multicast tree construction

The channel assignment algorithm has been justified to be an NP-completeness prob-

lem (which was discussed briefly in Sect. 3.Different scenarios for multicast routing

in WMNs of this article). However, the channel assignment problem of decreasing the

interferences resulted by frequency overlapping channels is the main problem of mul-

ticast tree routing in MCMR WMNs. Most proposed channel assignment algorithms

for multicast tree concentrate on decreasing interferences while network connectivity

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Fig. 13 An example of LCA algorithm[22]

has been neglected. In a more focused evaluation, channel assignment problem formulticast tree in MCMR WMNs can be categorized into three classes:

1. First routing in multicast tree and then channel assignment [13,14,2325].

2. First channel assignment and then routing in multicast tree [13,24].

3. A conjoint design of channel assignment and routing in multicast tree [12,13,21,

22,24,26,27,29,34,35,44,46].

The study in [14] attempts to make multicast routing dynamic in MCMR WMNs,

and based on the topology changes of the multicast tree, channels will be assigned

again. Overall, in the first two classes, the implementation may not lead to the opti-mal solution for dealing with interferences. On the other hand, the third one tries to

consider both channel assignment issues and multicast tree issues. Relevant works in

[12,21,22,34,35] and [46] focus on the third proposed solution. In [21] and [22], two

approaches for multicast tree construction and channel assignment in MCMR WMNs

have been presented. In the first method, at first, mesh nodes are placed at different

levels. After categorizing, multicast tree is constructed. In the next step, forwarding

nodes in multicast tree should be specified according to the algorithm proposed in

[22]:

If each receiver node has several parents and one of their parents is on multicast

tree, this receiver is connected to that parent. Otherwise, one of the parent nodes is

selected randomly and one link is established to that parent.

This algorithm would continue recursively. After constructing multicast tree, using

the algorithm LCA (level channel assignment), channels are assigned to nodes

according to the level of BFS traversal tree they are located at. In other words, channel

I is assigned to nodes located in level I of the tree. Figure 12 illustrate an example

of LCA algorithm channel assignment to links between connected nodes. In Fig. 13a,

each number next to a link shows the number of applied channel on that link for send-

ing and receiving packets. Figure 13b shows the original network topology and its

responding tree mesh.

Also in [22], another method named MCM (multi-channel multicast) for over-

lapping channels has been proposed which tree nodes are placed at different levels

using BFS (breadth-first search). At the next step, the edges between the nodes are

neglected. Then, the minimum relay nodes (RN), which form the multicast tree, should

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be determined. The following approximation algorithm has been presented by authors

for this issue:

1. Parents can be chosen as RN if one of their children has fewer parents.

2. Between candidate RNs, a node with the largest number of children is selected.3. Selected RN and its children are removed from the tree and steps 1 and 2 are

repeated until all nodes placed in level i+1 are removed.

After constructing the multicast tree, two channel assignment methods have been

proposed in [22]. The first method is called Ascending channel assignment, and

the second one is called Heuristic channel assignment. For a given multicast tree,

a reasonable solution is more likely to assign each link with all the available chan-

nels. Therefore, it is expected to reach to a large number of different channel assign-

ment schemes. By eliminating interfering links, channel-assigned multicast tree can

be resulted as an optimal solution for channel assignment problem.In [35], authors have focused on establishing an interference-free and delay-

constrained multicast tree with maximized serviced mesh clients in multi-radio multi-

channel wireless mesh networks to achieve an optimal solution. They also propose

a CLLO algorithm based on cross-layer design, which takes into account the issues

of multicast routing and channel assignment in a conjoint manner and also joint with

application demand. A similar study in [34] has presented a cross-layer design to

decrease the interferences in simultaneous transmissions.

Also a related research in this scope has been performed in [12]. After proposing

LCA and MCM algorithms and the structure of multicast routing in their study, theauthors assert that MCM outperforms the LCA in terms of achieving better throughput

and shorter delay, whereas LCA can be particularly realized in a distributed manner.

5 Discussion

The overall purpose of this study was to find the best solution to optimize the data

transmission while preventing the collisions and delivery failures. Despite the whole

performed investigations, it seems that the work is just at a seminal stage. Regarding the

mathematical solutions, which lead to optimal solutions but not scalable enough, and

also the soft computing solutions, these methods still have the potential to be improved

and extended. The methods based on mathematical optimization will result in optimal

outcomes in small networks but because the problem is NP, their performance is not

satisfying enough to deal with large networks. On the one hand, the methods based

on mathematical optimization can be considered as a benchmark for performance

evaluation. On the other hand, the soft computing techniques, which result in sub-

optimal solutions in polynomial times, are better to be applied in large networks.

Moreover, the centralized methods typically result in better solutions in small networks

but their performance would be decreased in larger networks. In the other hand, the

distributed methods represent optimized solutions in reasonable times.

To have a better understanding and an overall preview of various investigations in

the scope of multicast routing in wireless mesh networks, Table1has been presented

below. In this table some of the mostly related studies have been regarded.

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Table 1 An overall preview of some of the mostly related studies

Reference Heuristic/

meta-heuristic/

mathematical-based

optimization

Centralized/

distributed

Sequentially/

conjointly

Network

structure

Investigated

parameters

[21] Mathematical-based

optimization

Centralized Conjointly MCMR Channel

interference,

tree cost


optimization

Centralized Sequentially/

conjointly

MCMR Channel

interference,

memory

demands,

tree cost

[45] Meta-heuristic

(learning automata)

Distributed Conjointly MCMR Throughput,

end-to-enddelay, packet

delivery

ratio, tree

cost

[27] Meta-heuristic

(genetic algorithm

simulated

annealingtabu

search)

Centralized Sequentially MCMR Delay, channel

interference,

tree cost

[19] Meta-heuristic (neural

network)

Centralized SRSC Overhead

[22] Heuristic Distributed Sequentially MCMR Throughput,

delay

[39] Heuristic (greedy

algorithm)

Centralized Sequentially SRMC Load balance

[20] Heuristic Centralized Conjointly SRMC Throughput

[13] Heuristic Distributed Sequentially MCMR Packet

delivery

ratio, delay,

throughput


optimization,heuristic (greedy

algorithm)

Centralized Conjointly MCMR Throughput

[22] Heuristic Distributed Sequentially MCMR Throughput,

delay,

channel

interference

6 Conclusion

Wireless mesh networks are considered as a promising technology for next generation

of wireless networking. In this article, we surveyed the existing works on multicast

routing in WMNs. We also provided a comparison of different routing approaches in

WMNs, including SPT and MCT. Moreover, applying multiple radios and multiple

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channels has been investigated to increase the overall throughput and capacity of

network, thanks to multiple non-overlapping channels. More specifically, in terms of

capacity, many modifications have been made to bring the best quality of service, while

keeping the sources and costs down simultaneously. Evidently, one of the prominent

solutions so far is to take advantage of the potential of WMNs in occupying multiplechannels and radios. An investigation of multicast protocols, with respect to channel

and radio association, has been presented and discussed about SRSC, SRMC and

MRMC WMNs.

We highlighted a classification for multicast routing algorithms, including heuris-

tic, meta-heuristic and mathematical-based optimization methods. While the first two

algorithms provide an acceptable performance, but the mathematical-based optimiza-

tion method will result optimized solutions. In particular, in this type of optimization,

QoS parameters are taken into account and are formulated. In addition, the methods

for the adoption of cross-layer design for joint channel assignment and multicast treeconstruction problem in MCMR WMNs were succinctly offered. Finally, a study of

MRMC and its relevant problems was presented, regarding the order of solving the

two sub-issues including channel assignment and multicast tree construction.

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