Improving throughput and fairness by improved channel assignment using topology control based on power control for multiradio multichannel wireless mesh networks
 Aizaz U. Chaudhry^{1}Email author,
 Nazia Ahmad^{1} and
 Roshdy H M Hafez^{1}
https://doi.org/10.1186/168714992012155
© Chaudhry et al; licensee Springer. 2012
Received: 25 October 2011
Accepted: 30 April 2012
Published: 30 April 2012
Abstract
Multiradio multichannel (MRMC) wireless mesh networks (WMNs) achieve higher throughput using multiple simultaneous transmissions and receptions. However, due to limited number of nonoverlapping channels, such networks suffer from cochannel interference, which degrades their performance. To mitigate cochannel interference, effective channel assignment algorithms (CAAs) are desired. In this article, we propose a novel CAA, Topologycontrolled Interferenceaware Channelassignment Algorithm (TICA), for MRMC WMNs. This algorithm uses topology control based on power control to assign channels to multiradio mesh routers such that cochannel interference is minimized, network throughput is maximized, and network connectivity is guaranteed. We further propose to use twoway interferencerange edge coloring, and call the improved algorithm Enhanced TICA (eTICA), which improves the fairness among flows in the network. However, the presence of relatively long links in some topologies leads to conflicting channel assignments due to their high interference range. To address this issue, we propose to utilize minimum spanning tree rooted at the gateway to reduce conflicting channels, and in turn, improve medium access fairness among the mesh nodes. We call the improved algorithm eTICA version 2 (eTICA2). We evaluate the performance of the proposed CAAs using simulations in NS2. We show that TICA significantly outperforms the Common Channel Assignment scheme in terms of network throughput, and eTICA and eTICA2 achieve better fairness among traffic flows as compared to TICA. It is also shown that eTICA2 leads to improved network throughput, as compared to TICA and eTICA.
Keywords
channel assignment fairness interferencerange edge coloring topology control wireless mesh networks1. Introduction
In multiradio multichannel (MRMC) wireless mesh networks (WMNs), a key issue is the cochannel interference from simultaneous transmissions of mesh nodes located within the interference range of each other, which degrades the capacity of the network. Mitigating such interference in the MRMC WMN architecture requires effective Channel Assignment (CA). This involves assigning a channel to each radio in a way that minimizes interference on any given channel as well as ensures network connectivity [1].
Topology control using transmit power control is a useful technique for reducing the cochannel interference in a WMN and increasing the network capacity. This is done by adjusting the transmission range (TR) of a mesh node by controlling its transmit power. The main goal of a Topology Control Algorithm (TCA) is to minimize the cochannel interference, enhance spatial channel reuse, and maintain network connectivity through the selection of minimum transmission power for each radio interface. Hence, mesh nodes transmit at the minimum power required to maintain connectivity with their immediate neighbors. This leads to multihop communication instead of long direct links and results in lower interference in the network.
In this article, we propose centralized Channel Assignment Algorithms (CAAs), which build a controlled topology using power control with the goal of minimizing interference between Mesh Routers (MRs) and ensuring network connectivity at the same time. The advantage of topology control based on power control lies in the fact that it improves network spatial reuse and hence, the traffic carrying capacity. To the best of the authors' knowledge, the proposed CAAs are the first of their kind to use topology control based on power control for CA in MRMC WMNs. The main contributions of this study are as follows:

A new TCA, Select x for less than x, that builds the network connectivity graph by selecting the nearest neighbors for each mesh node in the network with the objective of minimizing interference among MRs and enhancing frequency reuse as well as simultaneously ensuring a connected network.

A new CAA, Topologycontrolled Interferenceaware Channelassignment Algorithm (TICA), which uses the Select x for less than x TCA to intelligently assign the available channels to the MRs with the objective of minimizing interference and hence, improving network throughput. A preliminary work on TICA has been presented in [2].

An extension of TICA, Enhanced TICA (eTICA), which, instead of using the oneway interferencerange edge coloring approach of TICA, uses twoway interferencerange edge coloring. eTICA results in a more accurate CA, which leads to an enhancement in the fairness among traffic flows without compromising the network throughput. A preliminary work on eTICA has been presented in [3].

An enhancement of eTICA, eTICA version 2 (eTICA2), which employs a Minimum Spanning Tree (MST) rooted at the gateway instead of a Shortest Path Tree (SPT) employed in TICA and eTICA, to reduce conflicting channels. This approach improves medium access fairness among the mesh nodes, which leads to an improvement in the network throughput.

A centralized Failure Recovery Mechanism (FRM) for our proposed CAAs, which provides automatic and fast failure recovery by reorganizing the network to bypass the failed node and to restore connectivity. A preliminary work on the proposed FRM has been presented in [4].
The rest of the article is organized as follows. In Section 2, we present existing literature related to CA schemes and schemes using topology control for CA. The network architecture for the proposed model is presented in Section 3. In Section 4, we present the TCA, Select x for less than x, and the details of its phases. In Section 5, we explain the CA problem and present TICA along with the details of its phases. In Section 6, we discuss the problem of oneway interferencerange edge coloring and present eTICA. In Section 7, we discuss the problem of long links, and present eTICA2 along with the details of its MST approach to counter this problem. In Section 8, we present the FRM for our proposed CAAs. In Section 9, we provide simulation results to evaluate the performance of the proposed CAAs. The article concludes in Section 10.
2. Related study
A number of CAAs have been proposed with the objective of addressing the capacity problem in multihop WMNs. In centralized CA schemes such as Traffic and Meshbased Interference Aware Channel Assignment (MesTiC) [1] and Centralized Hyacinth (CHYA) [5], the traffic load is required to be known before assigning channels, whereas our proposed CAAs require no such knowledge. The Hybrid Multiple Channel Protocol (HMCP) proposed in [6] requires radios to switch between channels on a perpacket basis. In such cases, time synchronization and coordination between mesh nodes is required, which is not needed in our proposed CAAs. The Breadth First SearchChannel Assignment (BFSCA) scheme proposed in [7] requires certain number of MRs with certain number of radio interfaces to be placed at certain hops from the gateway, whereas our proposed CAAs simply require all MRs to have four data radios, do not require any careful router placement strategy and work with any placement of MRs as verified by the performance evaluation. Unlike our proposed CAAs and Distributed Hyacinth (DHYA) [8], the abovementioned CA schemes do not possess fault tolerance capability and have not provided any mechanism of recovery after a node failure. In [9], the Joint Resource and Channel Assignment (JRCA) algorithm was introduced. This algorithm determines the number of radios required at each node based on the traffic demand and produces the CA for each radio, such that the interference among the links operating on the same channel is minimized. The Maxflowbased Channel Assignment and Routing (MCAR) algorithm presented in [10] splits the CA into two stages. In the first stage, links are sorted into groups based on the flows they carry, while in the second stage, a channel is selected for each group and is assigned to all links of this group. If it is possible to do so, different channels are assigned to groups containing interfering links. In [11], a centralized tabu searchbased algorithm is proposed, the objective of which is to minimize the total network interference. Though all of the CAAs presented in [1, 5–11] are interferenceaware and aim to minimize the cochannel interference, but unlike our proposed CAAs, they do not use topology control based on power control for CA. Also unlike eTICA, they do not employ the technique of twoway interferencerange edge coloring to achieve a more accurate CA.
Topology control in WMNs is typically targeted toward reducing interference and improving spectral efficiency while maintaining network connectivity. Interference is confined by lowering the transmit power. Since transmit power is directly proportional to the distance between the nodes, a reasonable strategy is to replace the long links with shorter ones. Local Minimum Spanning Tree (LMST) is a TCA presented in [12], which uses MST to achieve short link lengths resulting in the medium being shared efficiently. In CAAs proposed in [13–15], the network topology has been built using MST. The motivation for using MST in these CA schemes is that shorter links resulting from MST imply more capacity in WMN by reducing interference with nearby links which use the same channel. Our proposed CAA, eTICA2, minimizes conflicting channels by employing an MST rooted at the gateway in combination with topology control based on power control and twoway interferencerange edge coloring.
Since the main network resource, namely the frequency spectrum is limited, it must be shared fairly among the contending nodes. Achieving fairness in WMNs can broadly be categorized in terms of pernode and perflow fairness. Perflow fairness refers to equal share of the data among traffic flows arriving at the gateway. Unfairness among flows arises due to multiple flows sharing the same link. This causes congestion at such links which leads to unfairness among flows reaching the gateway. Pernode fairness refers to equal access for each node to the wireless medium. Unfairness in medium access arises in MRMC WMNs due to some nodes operating on a conflicting channel and contending with each other for medium access on that channel. The authors have proposed an algorithm in [16] to improve the fairness by differentiating the traffic among the connections in a wireless multihop network. In [17], the authors propose a receiving node assistance feature in addition to the existing CSMA/CA protocol to remove exposed terminal problem and enhance fairness in multihop wireless networks. The authors have proposed a graphbased algorithm in [18] for improving fairness in WMNs that is based on employing multiple queues per node, using different backoff parameters and EIFS values. In [19], the authors have proposed a fair binary exponential backoff algorithm by adapting the contention window to reduce the effect of flow starvation, thereby improving fairness in a WMN. All of these schemes have used Jain's fairness index [20] as a measure of the network fairness. Unlike [16–19], our proposed CAAs, eTICA and eTICA2, improve fairness among flows through a more accurate CA and improve medium access fairness by reducing the conflicting channels, respectively.
3. Network architecture
In our proposed model, each MR is equipped with five radios which operate on IEEE 802.11a [21] channels (5 GHz band). One of these radios is used for control traffic, while the other four radios are used for data traffic. Each radio interface of the multiradio MR is equipped with an omnidirectional antenna.
The IEEE 802.11a standard uses Orthogonal Frequency Division Multiplexing (OFDM) as the physical layer transmission technology. Out of the 12 available nonoverlapping 802.11a channels, channel 12 is used for control radio on each MR and the remaining 11 channels are used for data radios. Since each MR is equipped with four data radios, it can communicate with a maximum of four neighbors for data communication simultaneously, which implies that the Maximum Node Degree (MND) per node is four. The MND of four is selected in order to fully utilize the 11 available nonoverlapping channels. Results have shown that with 12 available channels, network throughput increases up to an MND of four per node and saturates after that [5].
Roofnet [22] is an experimental WMN built by Massachusetts Institute of Technology (MIT). Similar to Roofnet, we assume that each mesh node has omnidirectional antennas installed on the roof of a building and the propagation environment is characterized by a strong lineofsight component. So, the channel propagation model used is either freespace propagation model or tworay propagation model, depending on the crossover distance.
4. Select x for less than x TCA
4.1. Gateway advertisement
Initially, the gateway broadcasts a "Hello" message on the control channel, announcing itself as the gateway. Each MR that receives this Hello message on the control channel over its control radio broadcasts it again and it is flooded throughout the network. The Hello message contains a hopcount field that is incremented at each hop during its broadcast. An MR may receive multiple copies of this message. However, the distance of an MR from the gateway is the shortest path length (shortest hop count) of the Hello message received by the MR through its control radio over different paths. In this way, each MR knows the next hop to reach the gateway using its control radio.
4.2. Topology control problem
The problem of topology control in multiradio WMNs involves the selection of transmission power for each radio interface of each mesh node in the network, so as to maintain the network connectivity with the use of minimum power [23]. The objective of the proposed Select x for less than x TCA is to build a connectivity graph with a small node degree to mitigate the cochannel interference and enhance spatial channel reuse as well as preserve network connectivity with the use of minimal power, as less transmit power translates to less interference.
4.3. Assumptions

All mesh nodes start with the maximum transmission power.

Each mesh node has its location information.

Each mesh node uses an omnidirectional antenna for both transmission and reception.

Each mesh node is able to adjust its own transmission power.

The maximum transmission power is the same for all mesh nodes.

The maximum TR for any two mesh nodes to communicate directly is also the same.

The initial topology graph created, when every mesh node transmits with maximum power, is strongly connected.
4.4. Phases of Select x for less than x TCA
4.4.1. Exchange of information between nodes
In the first exchange, each node broadcasts a Hello message at maximum power containing its node ID and position.
4.4.2. Building the maximum power neighbor table
From the information in the received Hello messages, each node arranges its neighboring nodes in ascending order of their distance. The result is the maximum power neighbor table (MPNT). Then, each node sends its MPNT along with its position and node ID to the gateway using its control radio.
4.4.3. Building the direct neighbor table
For each node in the network, the gateway builds a direct neighbor table (DNT). Based on the information in the MPNT of node v and the MPNTs of its neighbors, if (a) node w is in the MPNT of node v and (b) node w is closer to any other node y in the MPNT of node w than to node v, then the gateway eliminates node w from the MPNT of node v. If after removing nodes from the MPNT of node v, the remaining number of nodes in the MPNT of node v is equal to "x  1," then the gateway selects "x" nearest nodes as neighbors of node v, which results in the DNT. However, after removing nodes from the MPNT of node v, if the remaining number of nodes is greater than or equal to "x," the result is the DNT. We call the above algorithm as Select x for less than x TCA, where x is a positive integer.
4.4.4. Converting into bidirectional links
For each node in the network, the gateway converts the unidirectional links in the DNT of a node into bidirectional links. For each unidirectional link, this is done by adding a reverse link in the DNT of the neighboring node. This converts the DNT into bidirectional DNT, which results in the Final Neighbor Table (FNT).
4.4.5. Calculating the minimum power required
where G_{ t } and G_{ r } are the transmitter and receiver antenna gains, respectively. RxThresh is the power required by the radio interface of the receiving node to correctly receive the message.
5. TICA
5.1. CA problem
The CA problem in MRMC WMNs involves assigning a channel to each radio of an MR in a way that minimizes interference on any given channel and ensures connectivity between the mesh nodes.
5.1.1. Objectives
The CAA should satisfy the following two main goals:

Minimize cochannel interference between MRs

Ensure network connectivity
5.1.2. Constraints
In order to achieve these goals, the CAA should satisfy the following requirements:

In order to communicate, a pair of mesh nodes within transmission range of each other needs to have a common channel assigned to their endpoint radios.

Links in direct interference range of each other should be assigned nonoverlapping channels.

The number of distinct channels that can be assigned to an MR is bounded by the number of radios it has.

The total number of nonoverlapping channels is fixed.

Since the traffic in a WMN is directed to and from the gateway, the traffic flows aggregate at routers close to the gateway. Links that are expected to support heavy traffic should be given more bandwidth than others. In other words, these links should use a radio channel that is shared by fewer nodes. Therefore, priority in CA should be given to links starting from the gateway based on the number of nodes that use a link to reach the gateway.
5.2. Interferencerange edge coloring
If K be the number of available colors (channels), then for K ≥ 4, the distance2 edge coloring problem, also known as strong edge coloring problem, is NPcomplete [25]. A distance2 edge coloring of a graph G is an assignment of colors to edges so that any two edges within distance2 of each other have distinct colors. Two edges of G are within distance2 of each other if either they are adjacent or there is some other edge that is adjacent to both of them. The distance2 edge coloring has been used in [26] for CA, where the authors have described the interference model as twohop interference model. In this model, two edges interfere with each other if they are within twohop distance. In other words, two edges cannot transmit simultaneously on the same channel if they are sharing a node or are adjacent to a common edge.
In our proposed network model, the number of available channels (colors) is 11 which means that K = 11. Based on its similarity to distance2 edge coloring problem which is NPcomplete for K ≥ 4, the interferencerange edge coloring problem is, therefore, also NPcomplete. Hence, we propose TICA, which is an approximate algorithm for CA in MRMC WMNs. TICA has an overall computational complexity of O(N^{3}), where N is the number of nodes in the network.
5.3. Phases of TICA
5.3.1. Topology control
In order to create the network connectivity graph with the aim of reducing the interference between MRs, network topology is controlled using power control at each MR. All nodes send their MPNTs to the gateway using their control radio. Note that in order to send its MPNT to the gateway, each MR knows the next hop to reach the gateway using its control radio via gateway advertisement process. Gateway starts with the Select 1 for less than 1 TCA and builds FNTs for all nodes. The computational complexity of this phase is O(L_{ M } + N^{3}+ L_{ D }^{2}) ≈ O(N^{3}), since L_{ M } < N^{2} and L_{ D }^{2} < N^{3}, where L_{ M } is the number of links in the MPNTs of all nodes, L_{ D } is the number of links in the DNTs of all nodes, and N is the number of nodes in the network.
5.3.2. Connectivity graph
Based on the FNTs of all nodes, the gateway builds the connectivity graph. It checks the resulting network for connectivity to ensure that it can reach any node in the network directly or through intermediate hops. If the resulting network is not connected, the gateway moves to a higher TCA by incrementing x in the Select x for less than x TCA. The computational complexity of this phase is O(L_{ F } + N), where L_{ F } < N^{2} and is the number of links in the FNTs of all nodes in the network.
5.3.3. Minimum powerbased SPT with an MND of 4
After ensuring that the connectivity graph is connected, the gateway builds the SPT based on the connectivity graph. The computational complexity of this phase is O(L_{ F } + N^{3}) ≈ O(N^{3}). The metric for path selection is minimum power. While building the SPT, the gateway ensures that each node can have only four TR neighbors and builds an SPT with an MND of four per node. If any node in the SPT has more than four links, gateway selects those four links for that node that have the minimum weight and sets the weights of all other links to infinity. It then checks the resulting Minimum Powerbased SPT (MPSPT) graph for connectivity. If the resulting MPSPT is not connected, the gateway moves to a higher TCA.
5.3.4. Link ranking
where N is the total number of nodes in the network. I_{ n, l } is 1 if node n is using link l, and 0 otherwise. In case of links with the same rank, link whose power of farthest node to the gateway is smallest is given a higher rank. If there are still links with the same rank, link with smallest node IDs is given a higher rank. The computational complexity of this phase is O(N^{2}).
5.3.5. CA
The algorithm then assigns a channel to each link of the MPSPT according to its rank. The computational complexity of this phase is O(N^{3}). It begins with assigning the 11 available channels to the 11 highestranked links such that channel 1 is assigned to firstranked link. For the 12thranked link and onwards, the gateway checks the CA of all links within the interference range of both nodes that constitute that link. Out of the 11 available channels, those channels that are not assigned to any link within the interference range (IR) of both nodes that constitute that link are termed as nonconflicting channels. If the gateway finds one or more nonconflicting channels, it assigns that channel to the link which has the highest channel number.
5.3.5.1. Least interfering channel
If the gateway cannot find any nonconflicting channel, it selects a channel that causes minimum interference to the link. Such a channel is called a Least Interfering Channel (LIC).
5.3.5.2. Interference level
where i is the channel that has value between 1 and 11, (IL) _{ i } is IL of channel i, r is rank of link using channel i, R is maximum rank assigned to a link in MPSPT, m is a link using channel i that is within IR of a node of the 12thranked link, d is distance from a node of link m to a node of the 12thranked link, and α is 2 or 4 depending on crossover distance.
Similarly, the gateway assigns channels to all the links in the MPSPT. Using its control radio, it then sends each mesh node the channel assignment and routing message (CARM). For each channel assigned to an MR, the CARM message contains the channel number and the neighbor node to communicate with using this channel. The CARM also contains the next hop to reach the gateway for data traffic. Based on the channel assigned to an MR to communicate with a neighbor and its distance to that neighbor, the MR applies power control and adjusts its transmission power accordingly, using (2) or (3), depending on the crossover distance.
6. eTICA
TICA uses interferencerange edge coloring for assigning a channel to a link, whereby it inspects the channelassigned links within the interference range of both mesh nodes that constitute that link before assigning it a channel. However, this approach of oneway interferencerange edge coloring does not find all the LICs in most cases. This leads to undetected hidden links which results in the CAA allocating the same channel to two links within the interference range of each others' end nodes. This leads to decreased network throughput and fairness. This drawback of TICA has been addressed by employing twoway interferencerange edge coloring in eTICA. eTICA results in an accurate CA thus reducing interference and improving fairness among flows without sacrificing the network throughput. Its computational complexity is the same as that of TICA.
Comparison of TICA and eTICA channel assignments
Link  Channel assignment (TICA)  Channel assignment (eTICA) 

2319  9  9 
117  9  7 
1916  11  11 
131  11  8 
Comparison of LICs identified by TICA, eTICA, and eTICA2 (36node network)
CAA  RT  

1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  
TICA  1  5  2  2  1  2  2  1  2  2  4  3  2  3  2  3  4  0  1  2  1  2  2  4  3 
eTICA  3  8  4  3  2  2  2  1  3  3  4  4  2  3  2  3  6  0  1  3  3  3  2  4  4 
eTICA2  1  3  1  3  2  2  2  2  1  1  3  2  1  2  2  2  4  1  0  1  2  2  1  1  3 
Comparison of LICs identified by TICA, eTICA, and eTICA2 (100node network)
CAA  RT  

1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  
TICA  3  2  2  2  0  3  4  2  0  1  2  3  0  4  1  5  9  4  2  6  5  4  3  6  2 
eTICA  11  3  6  7  6  7  6  2  3  11  12  5  5  8  13  13  18  11  6  8  9  11  11  11  3 
eTICA2  2  0  3  0  3  2  3  2  5  0  5  1  4  2  4  5  4  9  3  3  5  4  2  3  0 
7. eTICA2
The twoway interferencerange edgecoloring introduced in eTICA leads to an improved CA scheme and eliminates the problem of hidden links. However, in some topologies, owing to the long links, LICs result in increasing the interference. Hence, reuse of a channel within the interference range causes significant decrease in network throughput. Since the long links contribute to interference, they should be replaced with shorter links wherever possible. So, a modified CAA, eTICA2, is presented in this section which employs an MST rooted at the gateway instead of a SPT to reduce the occurrence of conflicting channels, thereby, improving fairness in medium access and network throughput. Its computational complexity is also same as that of TICA.
7.1. Improving fairness in medium access using MST
7.2. Improving throughput using four radios of the gateway
The maximum achievable throughput of a topology is limited by the performance bottleneck at the links which originate from the GW, as well as the number of traffic sources using those links. The maximum data rates achievable at a link with one, two, and three sources are 8.192, 16.384, and 24.576 Mbps, respectively. IEEE 802.11a supports a maximum data rate of 54 Mbps. However, the effective data rate is 24.748 Mbps, while the rest is consumed by overhead. Hence, if there are more than three sources sharing a link, there is a traffic bottleneck at that link with the achievable data rate being limited to 24.748 Mbps. The maximum achievable throughput and in turn the throughput performance of eTICA2 can be improved by utilizing all four radios of the gateway. In order to utilize all four radios of the gateway, eTICA2 builds an MPMST from the gateway utilizing its four nearest neighbors.
8. FRM
The proposed CAAs are faulttolerant and support automatic and fast failure recovery. In case of node failure, the FRM is initiated by the gateway.
All nodes send periodic "keepalive" messages to the gateway on the control channel using their control radios. The keepalive message tells the gateway that the node is active. If the gateway does not receive three consecutive keepalive messages from a node z, then it concludes that node z has failed and is no longer active. The gateway then deletes the MPNT for this node and deletes node z from MPNTs of all its neighboring nodes. Note that the gateway received MPNTs of all nodes during the setup phase. During the setup phase, nodes exchanged Hello messages, which were transmitted at maximum power on the control channel and contained the node ID and node position. From the received Hello messages, each node built an MPNT by arranging its neighboring nodes in ascending order of their distance. Each node then sent its MPNT to the gateway over the control channel using its control radio.
After detecting the failed node, deleting its corresponding MPNT and deleting it from the MPNTs of its neighbors, the gateway builds the DNT for each node using the Select x for less than x TCA. The gateway converts the unidirectional links in the DNT of a node into bidirectional links, which results in the FNT of that node. Similarly, the gateway builds the FNTs for all active nodes. Based on the FNTs of all active nodes in the network, the gateway builds the connectivity graph and checks the resulting network for connectivity. After ensuring that the connectivity graph is connected, the gateway builds the MPSPT (as in TICA and eTICA) or MPMST (as in eTICA2), with an MND of four. After ensuring that the minimum powerbased tree is connected, the gateway builds the link ranking. Based on the link ranking, the gateway assigns the channels to links.
9. Performance evaluation
Absolute fairness is achieved when F_{J} = 1 and absolute unfairness is achieved when F_{J} = 1/N.
where X and Y could be TICA, eTICA, or eTICA2. Therefore, F_{ X,Y } > 1 indicates that fairness of CAA X is better than that of CAA Y.
The 'Throughput Ratio', T_{ R } , is defined as the ratio of the throughput achieved by eTICA2, eTICA, and TICA over their maximum achievable throughputs, respectively. T_{ R } = 1 indicates that the algorithm has achieved the maximum achievable throughput for that particular topology.
In the CCA scheme, all MRs have four radio interfaces. The first radio on each MR is tuned to the first nonoverlapping channel; the second radio is tuned to the second nonoverlapping channel, and so on. In this scheme, MRs do not control their power, transmit with the same maximum power, and use AODV (Adhoc OnDemand Distance Vector) routing protocol [28]. In the CCATC scheme, the MRs follow the same network model as that proposed in Section 3. In this scheme, the network topology is controlled using the Select x for less than x TCA. However, the channels are assigned to the links of the MPSPT similar to the CCA scheme. From the CARM, each MR applies power control based on the channel assigned to an MR to communicate with a neighbor and its distance to that neighbor as well as updates its next hop.
9.1. Simulation environment
The performance of the proposed CAAs has been evaluated using simulations which have been carried out in NS2 (version 2.30) [29]. The original model in NS2 was modified using the procedure given in [30] to create multiinterface mesh nodes. All radios are IEEE 802.11a radios that support 12 channels. The packet reception threshold is set to 65 dBm in order to achieve a maximum data rate of 54 Mbps supported by IEEE 802.11a. In order to achieve a strongly connected topology, the maximum transmission power for all radios is set to 27 dBm. RTS/CTS is disabled.
9.2. Network topology
A random topology has been used for the evaluation, in which MRs are distributed randomly according to a uniform distribution in a 500 × 500 m^{2} area. Twentyfive different random topologies of a 36node network and a 100node network are considered. Irrespective of its location, Node 15 is set to be the gateway for all random topologies.
9.3. Simulation parameters
PHY layer configuration in NS2
Physical layer parameters  Settings 

TX/RX antenna height (m)  3 
Gain of TX/RX antenna  1 
Packet capture threshold (dB)  10 
Packet reception threshold (Watts)  3.16227e10 
Carrier sense threshold (Watts)  7.90569e11 
MAC layer configuration in NS2
MAC layer parameters  Settings 

Minimum contention window  15 
Maximum contention window  1023 
Slot time (μs)  9 
SIFS period (μs)  16 
Preamble length (bits)  96 
PLCP header length (bits)  24 
PLCP data rate (Mbps)  6 
Basic rate (Mbps)  6 
Data rate (Mbps)  54 
9.4. Simulation results
9.4.1. TICA versus CCA
9.4.1.1. Network throughput
Results for network throughput (36node network)
CAA  Average throughput  95% CI interval for average throughput 

TICA  46.66  41.6751.65 
CCATC  14.39  10.6918.09 
CCA  14.59  11.8617.32 
Results for network throughput (100node network)
CAA  Average throughput  95% CI interval for average throughput 

TICA  44.85  39.2750.42 
CCATC  9.59  6.8312.34 
CCA  8.30  5.4611.14 
9.4.2. eTICA versus TICA
9.4.2.1. Throughput ratio
As stated earlier, the maximum achievable throughput of a topology is limited by the performance bottleneck at the links that originate from the gateway, as well as the number of traffic sources using these links. For the scenario in Figure 4, there are four links emanating from the GW. The maximum achievable throughput for links 152 and 155 is 8.192 Mbps each since there is only one source using each link. The maximum achievable throughput for link 1531 is 24.576 Mbps since there are three sources using this link. The maximum achievable throughput for link 158 is limited to 24.748 Mbps since there are more than three sources using this link. Hence, the total maximum achievable throughput for this scenario is 65.7 Mbps.
Results for throughput ratio (36node network)
CAA  Average throughput ratio  95% CI for average throughput ratio 

TICA  0.87  0.820.92 
eTICA  0.91  0.860.96 
Results for throughput ratio (100node network)
CAA  Average throughput ratio  95% CI for average throughput ratio 

TICA  0.82  0.750.88 
eTICA  0.90  0.850.95 
9.4.2.2. Fairness ratio
Results for fairness ratio (36node network)
CAA  Average fairness ratio  95% CI for average fairness ratio 

eTICA over TICA  1.08  1.011.16 
Results for fairness ratio (100node network)
CAA  Average fairness ratio  95% CI for average fairness ratio 

eTICA over TICA  1.20  1.111.29 
9.4.3. eTICA2 versus eTICA and TICA
TICA and eTICA use the SPT approach to build a minimum powerbased tree from the gateway to each node whereas eTICA2 employs the MST approach for the same. For a fair comparison, we have ensured that the number of traffic sources is the same for all three CAAs in the following way. If A = {end nodes for SPT} and B = {end nodes of MST}, then for comparing all three CAAs, we have made a super set 'C' which is defined as C = A U B. Hence, C = {end nodes of SPT and MST}. Thus, the traffic sources in each realization of the random topology for each CAA are the end nodes of the SPT and the end nodes of the MST.
9.4.3.1. Throughput ratio
Results for throughput ratio (36node network)
CAA  Average throughput ratio  95% CI for average throughput ratio 

eTICA2  0.94  0.890.99 
eTICA  0.90  0.830.97 
TICA  0.84  0.770.91 
Results for throughput ratio (100node network)
CAA  Average throughput ratio  95% CI for average throughput ratio 

eTICA2  0.95  0.920.98 
eTICA  0.83  0.760.91 
TICA  0.77  0.700.85 
9.4.3.2. Fairness ratio
Results for fairness ratio (36node network)
CAA  Average fairness ratio  95% CI for average fairness ratio 

eTICA2 over eTICA  0.97  0.891.06 
eTICA2 over TICA  1.04  0.951.13 
Results for fairness ratio (100node network)
CAA  Average fairness ratio  95% CI for average fairness ratio 

eTICA2 over eTICA  0.97  0.821.11 
eTICA2 over TICA  1.07  0.891.26 
These results clearly show that eTICA2 leads to improved network throughput, as compared to eTICA and TICA. Also, eTICA2 is fairer than TICA but less fair as compared to eTICA. In eTICA2, MST leads to shorter links/hops having shorter interference range, which leads to reduced LICs, improved fairness in medium access and hence, improved network throughput. Although MST leads to shorter hops but it also leads to more hops from the source to the gateway and the average number of hops from the sources to gateway increases. Due to these more hops from the sources to the gateway, more flows pass through the same link and have to share that link, which negatively impacts the fairness among the flows in the network.
10. Conclusion
In this article, we have introduced Select x for less than x TCA, which minimizes the cochannel interference by selecting the nearest neighbors for each mesh node in the network. We have introduced TICA, which is a fixed and centralized CAA for MRMC WMNs. It employs topology control based on power control by using Select x for less than x TCA for building the connectivity graph. It assigns channels to the multiradio mesh nodes with the objective of improving the network throughput by minimizing the cochannel interference as well as ensures network connectivity. As verified by simulation results, TICA significantly outperforms the CCA scheme and its variant, CCATC scheme, in terms of network throughput. We proposed a new FRM for our proposed CAAs, which supports automatic and fast failure recovery. The GW runs the FRM in case of node failure.
We have shown that enhancements made to TICA lead to an improved CAA, eTICA, which is verified by the simulation results presented herein. The key objective during the CA phase in an MRMC WMN is to eliminate the presence of conflicting channels within the interference range of nodes. However, due to the availability of a limited number of orthogonal channels, this is not always possible. Hence, a CAA that reduces interference among nodes and provides maximum spatial reuse is needed. The twoway interferencerange edge coloring model, introduced in eTICA, implies that links formed by nodes that are within the interference range of each other will not be allocated the same channel, provided that there is a channel available for allocation. This leads to a better CA strategy yielding an improved CAA, eTICA, which improves the fairness among traffic flows without compromising the network throughput.
We have shown that enhancements made to eTICA lead to a more efficient CAA, eTICA2, which has been verified by the simulation results. To overcome the cochannel interference problem caused by long links in a random topology, eTICA2 utilizes an MST rooted at the GW. The shorter links resulting from MST lead to a small interference range. Replacing SPT with MST in eTICA2 leads to the reduction of LICs, which reduces the interference and improves medium access fairness, thereby, increasing the network throughput. The simulation results indicate that the average throughput ratio over 25 random topologies for the 100node network, using eTICA2, is 14% more than that achieved by eTICA and 23% more than that achieved by TICA. The fairness among traffic flows with eTICA2 is better than that with TICA but less than that with eTICA. The two enhancements of utilizing an MST and maximum possible out of the four radios of the GW, when coupled together, yield an improved CAA, eTICA2, which is successful in improving the medium access fairness by reducing the conflicting channels, thereby increasing network throughput, while also improving the fairness among traffic flows.
The propagation model used is freespace model or tworay model depending upon the crossover distance. As part of future work, the performance of the proposed CAAs may be tested under more realistic propagation models, such as Shadowing and Rayleighfading.
Declarations
Authors’ Affiliations
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