A taxonomy of cross layer routing metrics for wireless mesh networks
© Bin Ngadi et al; licensee Springer. 2012
Received: 21 December 2011
Accepted: 30 March 2012
Published: 21 May 2012
Multi-hop, multi-channel, and multi-radio wireless mesh networks (WMNs) are emerging as promising field of wireless technology with self-organizing and self-healing features for internet and real time applications, i.e., VoIP and Video over IP. Interoperability feature of WMNs have made them to integrate easily with other network technologies like wired networks, WiFi, WiMax, MANETs, and cellular networks. WMNs are gaining popularity due to their high network throughput which highly depends on the routing procedures. Routing algorithms like optimized link state routing protocol and dynamic source routing make efficient routing decisions on the basis of routing metrics which actually predict the cost of link quality. Most of the routing protocols and routing metrics implemented in WMNs are actually designed for mobile ad hoc networks (MANETs). Since WMNs have different characteristics and limitations as compared to MANETs, so the routing metrics design for MANETs do not perform well in WMNs. Furthermore, quality of service (QoS) and throughput of the network in WMNs can be enhanced by using cross layer routing approach and by deploying multi-channel multi-radio (MCMR) scenarios in each relay node. This article discusses a design taxonomy, limitations and qualitative comparison of existing routing metrics for QoS in MCMR WMNs with respect to routing parameters, i.e., transmission rate, inter-flow interference, intra-flow interference, congestion, and channel diversity. Moreover, our taxonomy also opens the door up for new research areas in the design of cross layer routing metrics for MCMR radio WMNs for high throughput IP connectivity.
Routing metrics predict the cost of the route calculated by the routing protocols. They provide quantifiable values that can be used to judge the cost or efficiency of a route. Since WMNs inherit many features of MANETs, routing protocols and routing metrics developed for MANETs can be set up for WMNs. Present framework of IEEE 802.11s deploy an ad hoc on-demand distance vector (AODV) algorithm as a major building block for routing in WMNs . Power requirements and mobility features of WMNs are very different from ad hoc networks. Mesh routers possess minimal mobility with no power constraints where as mesh clients are mobile with limited power. Therefore, routing metrics designed for ad hoc networks does not perform proficiently in WMNs . Moreover, WMNs support MCMR technologies which provide each node with multiple radios for improving the capacity of the networks, stipulate efficient routing protocols and metrics for WMNs . Also cross layer communication between network layer and MAC layer or physical layer has increased the performance of routing protocols in MCMR WMNs especially in real time and multi-cast applications . Thus design of routing protocols and routing metrics play a critical role in order to find out the efficient route to the destination in a network . Consequently this study is focused on the taxonomy, limitations, recent challenges and future perspective in the design and development of cross layer routing metrics for MCMR WMNs. Design taxonomy of the routing metrics will be based on the parameters classified as basic (transmission rate, packet loss ratio, delay), interference aware (received signal strength (RSSI), bit error rate, frame error rate, signal to noise ratio (SNR)), and load aware (number of flows, queue size). Limitations of each metric is discussed in detail with respect to real time applications like VoIP, Video over IP and multi-casting in MCMR WMNs. Qualitative comparison, recent challenges, and future perspective in the design and development of cross layer routing metrics are also summarized.
The rest of the article is organized as follows. The components and architecture of WMNs are discussed in Section 2. Characteristics and application scenarios of WMNs are elaborated in Section 3. Cross layer routing in MCMR WMN is explained in Section 4. Design taxonomy, characteristics and limitations of existing routing metrics are discussed in Section 5. Qualitative comparison and summary of the routing metrics is elaborated in Section 6. Finally, the conclusions and future perspective of this research is presented in Section 7.
2 Components and network architecture of WMNs
WMNs work as backhaul networks which provide broad band services to homes, offices, security systems, transportation systems. WMNs also provide interoperability with several wireless and wired networks. Thus they provide a bench mark for community multi-hop ad hoc networks . Components of WMNs are explained as follows:
2.1 Components of WMNs
There are three main components of WMNs such as mesh gateway routers, mesh routers, and mesh clients. Mesh gateway routers provide internet access to the network, mesh routers develop the main backbone of the wireless network where as mesh clients serve as a end user devices in the network. These components are explained in detail as follows:
Mesh gateway routers act as a network backhaul for communality networks with bridging and routing functionality which allow them to incorporate with different wired and wireless networks like Ethernet, mobile ad hoc wireless networks (MANETS), wireless sensor networks (WSNs), Wi-Fi, VANETS, and WiMax .
Mesh routers provide multi-hop routing between mesh gateways and mesh clients with self organizing and self healing features. Minimum mobility with no power constraints features help them to form the backbone of the wireless network. Mesh routers are also outfitted with multi channel single radio or MCMR devices to further enhance the capacity of the network.
Mesh clients are the end user devices in WMNs with high mobility and power constraint features, i.e., laptops, IP phones, personal digital assistant (PDA), and pocket PC. Mesh clients are normally provided with single radio devices and may or may not have routing functionality depending on the architecture of the network.
2.2 Architecture of WMNs
WMN is comprised of three main type of architecture such as client WMNs, hierarchical WMNs, and hybrid WMNs . Characteristics of each architecture is explained as follows:
Client WMN provides peer-to-peer networking facility between the client nodes. Client nodes are normally single radio devices with optional routing capability depending on the end user requirements. Client WMNs are very simple to design but scalability and limited resource allocation are the critical issues which may cause throughput degradation in the network.
Hierarchical WMNs consist of a hierarchy in which mesh gateway routers are at the top with bridging functionality for backhaul internet connectivity, mesh routers are in the middle with self configuring and self healing functionality to act as a network backbone and mesh clients are at the bottom to serve as an end user devices as shown in the Figure 2.
Hybrid WMNs consist of both hierarchical and client architectures. Mesh routers are equipped with the bridging functionality in order to integrate with other networks like wired networks, WiFi, MANETs and VANETs as shown in the Figure 2. multi-hop cellular networks is an example of hybrid WMNs .
3 Characteristics and application scenarios of WMNs
Multi hop, self-organizing and self-healing WMNs are popular in real time applications for there increased throughput and reliability as compared to other networks like WiFi and MANETs. Characteristic of WMNs are summarized under the following headings:
3.1 Low up-front deployment cost
Installation cost of 802.11 WiFi frameworks is quite high as compared to WMNs since cable connectivity to the network backbone, is required by each access point (AP) for internet access. Moreover, transmission range of 802.11 WiFi framework is very limited and often cause the problem of dead zones where as WMNs do not need any cabling structure to internet backhaul and dead zones are easily eliminated by changing the position of the mesh router. As a result coverage area in WMNs can be extended easily and more quickly because of ad hoc nature as compared to 802.11 based APs.
3.2 Reliability and robustness
WMNs are multi hop wireless networks with redundant paths between source and the destination. Therefore chance of single point of failure due to hardware failure, path failure, obstacle or power outage is eliminated. Furthermore, multiple links between the nodes also facilitate in avoiding the congested and the bottleneck links present in the network. Thus the communication in the network becomes more reliable and robust in nature .
3.3 Multi-channels multi-radios
3.4 COTS products
Common off-the-shelf (COTS) is a technology which is ready-made and easily available to the general public. Motivation for using COTS is to facilitate the development and minimize the cost of the product. COTS products include computer software, hardware systems or free software. Deployment of WMN is quite easy as compared to other wireless networks because most of the time network backbone consists of COTS products which are cheap and easily available. For example mesh routers and mesh gateways can be deployed by using normal personal computers.
3.5 Application scenarios of WMNs
WMNs broadband services are playing an effective role in the home, office, enterprise and community networks. Especially, multi-casting feature of WMN support Video over IP in community networks with high level of quality of service (QoS) by making delay variation and packet loss ratio to minimum . Public transportation system, public safety surveillance system, medical health system in hospitals, and Voice over IP (VoIP) or Internet telephony in community networks are making effective use of WMNs . During rescue operation due to natural disasters (floods, earthquakes or landslides), WMNs are helping by developing a peer to peer communications at anytime and anywhere between the group of people. US department of defense is also taking benefits from WMNs in the battlefields due to its self-organizing, self-healing, and ad hoc nature .
4 Cross layer routing approach in MCMR WMNs
Routing characteristics summary
Interfaces per node
One or more
One or more
Static in nature
4.1 Design parameters of cross layer routing metrics in MCMR WMNs
Routing protocols use routing metrics which actually predict the weight of the link or the path in order to make efficient routing decisions. Parameters related to the design of cross layer routing metrics in MCMR WMNs can be categorized as Basic, Load aware, Interference aware, and QoS. In spite of these, selected path or route must be Isotonic in nature in order to carry out loop free routing. Furthermore, asymmetry of the wireless links (transmission behavior of wireless link is different in different direction), and route stability parameters are very critical in the design of efficient cross layer routing metric in MCMR WMNs. Theses parameters are explained in detail as follows:
Parameters for cross layer routing metric
Packet loss ratio
No. of flows
Isotonic aware property of the routing metric is an important design parameter for selecting optimum weight paths and to avoid routing loops. Isotonic property of the routing metric is defined as, the order of weights of two paths is preserved if they are connected to a common third path. Isotonic property of the routing metric must be followed to calculate the optimum paths using Dijkstra’s algorithms in multi hop routing scenarios .
Asymmetry of wireless Links actually define the propagation behavior of links which is quite different in different directions as compared to wired links. Disseminate packets normally send from a source node may successfully be received at the destination node but the connection may failed when the destination node want to send replay packets back to source node. This criteria is known as asymmetry of wireless link . Asymmetry of the wireless links may reach up to 5 to 15% as mentioned by Ganesan et al. . Hence asymmetry of link must also be taken in to account while developing the routing metrics for WMNs.
Route stability parameters effect the overall throughput of the network. Since the overall performance of the network is highly dependent on the route stability parameters which actually minimize the fluctuation of the route after being declared as an efficient one. Frequent path oscillations results in the poor network performance because these frequent changes in path weight cause an increase in the number of route update packets. Route stability mechanism in wireless networks can be achieved by setting a limit of 10% throughput increase over the route which is currently being used by the routing protocol .
Design parameters shown in Table 2 reside at different level of the network. Design of routing metric may consist of one or more parameters. However, it is a very challenging research problem to design a MCMR routing metric so that it will capture all above mentioned parameters . On the basis of above discussion, taxonomy of available routing metrics for WMNs is explained in the following section.
5 Cross layer routing metrics for MCMR WMNs
High link losses, asymmetric link, and MCMR functionality in WMNs have made the design of routing metric quite challenging. However quite a good number of routing metrics are designed in the recent years for WMNs. This section will scrutinize the existing routing metrics with its definition and limitations in MCMR WMNs on the basis of parameters explained in Section 4.
5.1 Expected transmission count
ETX metric has significantly improved performance over minimum hop count routing metric as shown by the test bed results . ETX develop its design foundation on delivery ratios which truly effects the throughput as compared to minimum hop count metric. Furthermore, ETX take account of asymmetry of links in a duplex manner by considering the loss ratios. The utilization of the spectrum is also minimized by ETX which is helpful in increasing the capacity of the network.
ETX was actually designed for single-channel single-radio multi-hop wireless networks so it does not capture the channel diversity in MCMR multi-hop wireless networks. The design of ETX only predict about the inter flow interference by considering the loss ratios in a static manner but it does not have any information about the intra-flow interference faced by the links. In addition, active probing technique fail to predict the queuing delay in the network and without load balancing mechanism ETX matrix may lead the traffic to bottleneck routes in the networks. Active probing technique incorporated by this metric to capture the loss ratio may result in underestimation or overestimation of losses because data packets of IEEE 802.11 real transmission are of different sizes as compared to probe packets of same size, i.e., 134 bytes . ETX is based on average or mean loss ratio where as in WMNs burst losses exists which does not make off well by this routing metric . In addition, ETX does not take account the option that different communication links may possesses different transmission rates which has a critical effect on the network throughput.
where μ is average or mean packet loss ratio and σ2 is variance of packet loss ratio.
where δ is the strictness of the loss rate requirement.
Although mETX and ENT are improved form of ETX but they still failed to capture the link quality in terms of inter-flow and intra-flow interferences of the route . Furthermore, they compute the losses on the basis of bit error rate which is quite infeasible due to its complex verification mechanism and MAC layer error packet drop mechanism.
5.2 Expected transmission time
where S is the packet size, p j is the rate of packet loss and B j is the transmission rate of link j. The main idea behind the design of ETT metric is the use of multi radios in multi hop wireless networks to enhance the network performance. ETT is the amalgamation of packet loss rate and transmission rate of each individual link. ETT is an enhanced version of ETX with improved performance but still inherit the drawbacks of ETX being unaware of traffic load, intra-flow interference, inter-flow interference and channel diversity in MCMR WMNs. The design of ETT does not capture the losses due to contention caused by the traffic generated by the neighboring nodes. The traffic generated from the neighboring nodes contribute in the losses in two ways. First, it causes increase in collision which definitely increases the packet loss ratio. Secondly, it consumes the channel bandwidth. Active probing mechanism implemented in the design of ETT to capture the transmission rate may lead to over estimation during the time when the communication channels are quite busy. Although minimum delay (MD)  and improved expected transmission time (iETT)  are delay based routing metrics, but both of them inherit the basic drawbacks of ETT.
5.3 Weighted cumulative ETT
Q L i indicates the mean or average queue length and b i gives the transmission rate which captures the level of congestion at each node where as N i indicates the total number of child nodes using node i as their next hop on path p which actually predict the intensity of traffic congestion or concentration at each node.
5.4 Metric of interference and channel switching
where CSC i indicates the channel reserved for node i ’ s transmission and prev(i) denotes the previous hop of the node i through the route p. Thus CSC can capture the inference only between two successive links. MIC extends ETT by considering the intra and inter-flow interference required in MCMR WMNs but still lacks in load balancing and isotonic characteristics. To make MIC isotonic in nature, decomposition is carried out by transforming the real network into virtual networks which further increases its complexity [56, 57]. Moreover, it only measures the interference in a static way which is actually the total number of interfering node that may or may not be causing interference at that time. Thus MIC prefers nodes having less number of neighbors, as a result of which traffic will be routed towards the edges or the boundary of the network . MIC required dynamic information about ETT of each link in the network which introduces the overhead and degrade the performance efficiency of the network.
5.5 Load aware expected transmission time
where as γ ij is a link quality factor, RC i and RC j are the RC of the node i and j, respectively. Practically RC is calculated at layer 2 by measuring the free slots and completed slots provided by the modulation scheme in use. Transmission rate measurements in LAETT are carried out with the help of total number of flows passing across the node and are assumed to be of same data rate. This is actually not true in relation with the wireless networks as the data rates vary because of congestion and interference over the links from time to time. Moreover, different radios and applications utilizing the network have different transmission rate. Probing mechanism used in the design of LAETT to measure ETX may result in underestimation of the link quality. Furthermore, Equation (15) does not predict any information about the intra-flow and inter-flow interference, which is very critical in MCMR environments.
5.6 Airtime cost routing metric
where O ca represents channel access constant, O p indicates protocol overhead, B t is the number of bits in the test frame, r is the node transmission rate in Mbit per second and e pt frame error rate for the test frame having size equal to B t . The taxonomy of Airtime Cost routing shows that, it is very close to ETT. Actually (O ca + O p + B t /r) indicates the transmission time and (1/1−e pt ) indicates the number of retransmissions same as ETT. No load balancing mechanism is defined in this metric which may lead the route to congested areas. Airtime metric is unaware of intra-flow interference which has a significant effect on the network performance in MCMR WMNs. Moreover, active probing mechanism to capture the data rate and losses cause overhead in the network depending on the traffic congestion. Therefore, airtime cost metric does not predict the actual quality of the link .
5.7 Interference aware routing metric
where 1 ≤ j ≤ k.
Basically iAWARE is non isotonic in nature like WCETT thus cannot be used in link state routing protocols, i.e., OLSR. It only predicts the interference on the receiver side where as sender side interference component is also important for quality routes. Moreover, iAWARE has no MAC layer interference measurement mechanism, as it only capture the interference at a node level in terms of ratio between SINR and signal strength P which is being received from other interfering nodes . Lack of load balancing parameters may lead the traffic to congested route. When the value IR i of the link is greater then ETT i in Equation (18) then the value of iAWARE i becomes small causing the traffic to route towards the links having small value of ETT but may have higher level of interference causing performance degradation in the network.
5.8 Interferer neighbors count routing metric
where N j indicates the number of interfering links resulting from the transmission taking place on link j, r k represents the available transmission rate of the link k. Although INX is isotonic in nature, it performs well only under low load scenarios because no load balancing mechanism is defined in the routing metric. As a result, it faces quick performance degradation as the network load increases. Moreover, it uses a probing technique to measure the interference parameters of the link in a static way which causes an overhead and also fail to predict the true quality of the link. Nevertheless INX behave in a better way as compared to MIC because it follows asymmetric links and isotonic behavior .
5.9 Resource aware routing for MESH
where T Xrate is the transmission rate, Tidle is the idle time interval and Tbusy is the busy time interval, respectively, for the calculation of traffic load based on passive monitoring technique. Low overhead RARE can predict the inter-flow interference through contention component N c as defined in Equation (24) in a passive manner but fail to predict the intra-flow interference and channel diversity in MCMR WMNs. Moreover, passive measurements does not predict about the brusty losses which normally occur in wireless links. As a result RARE may under estimate the link quality of the network. Furthermore, WMNs use Common-off-The-Shelf (COTS) products so normally their network cards or drivers do not support passive monitoring while transmitting which may result in the performance degradation.
5.10 Contention aware transmission time
where N i is total number of links interfering the transmission taking place on the link i. Similarly N j is total number of links interfering the transmission taking place on the link j. R k and R j indicate the packet size of the links containing 1 and 2 hop neighbors, respectively. B k & B j measure the bandwidth of links in 1 and 2 hop neighbors, respectively. τ j is defined as packet transmission attempt rate on link j.
Although CATT captures the inter-flow and intra-flow interferences simultaneously . Like MIC, CATT also assumes that all the neighboring nodes are participating in the inference parameters (weather or not they are involved in transmitting the data) which may overestimate the link quality. Another important drawback in CATT is that it uses active probing mechanism to measure the interference and delay which causes large overhead in the network. Hence this metric is not suitable for triple play application networks where the traffic is quite congested . Moreover, delay in transmission is used to measure the traffic load which does not predict the load in an accurate way.
5.11 Interference load aware routing metric
where p is the path in the network, metric of traffic interference (MTI) and CSC which measures the efficiency of flows routed through the path p. These two components of the metric measure the intra flow interference, inter flow interference, transmission rates, congested areas, and packet loss ratios.
where I L ij (C) is the interference load of the neighbors.
where min(ETT) and min(AIL) is the smallest ETT and average load in the network, respectively. In order to capture the difference in transmission rate, packet loss ratio, intra-flow interference, and inter-flow interference. ILA uses a active probing mechanism which induces a large overhead in the network. However, it may not be suitable for congested traffic areas. Since as it is based on ETX and ETT, it inherits their drawbacks. Exposed node terminal problem causes the interference to occur in two hop range instead of one hop range as consider in ILA and MIC, results in the underestimation of the link quality. Furthermore, ILA does not consider the transmission delay in order to route the traffic efficiently .
5.12 Contention window based routing metric
So β is equal to 1 when channel utilization is quite small and β is equal to βmax when channel utilization is maximum. Where as T1 and T2 indicate the minimum and maximum threshold values of the channel utilization represented by u and the value α will decide about the change in the value of β as channel utilization u passes the threshold value T1. CWB can only capture the inter-flow interference and traffic load but fail to capture the intra-flow interference which is a critical parameter for MCMR WMNs. Furthermore, this metric perform poor when the network conditions change quickly because calculations needed to find out the size of CW are quite sophisticated .
5.13 Metric for interference and channel diversity
where 0 ≤ RI ≤ 1 and 0 ≤ CBT ≤ 1.
Total Time is the measure of time between the first attempt to send the packet and the reception of its acknowledge. Idle Time is the measure of back off times and the time in which the radio nodes sense that the medium is free for access. Thus CBT is the measure of time spend during transmission, receiving, and occupying states.
Overall, MIND captures the inter-flow interference and intra-flow interference in intelligent manner by considering physical and logical interference models. The major limitation of MIND is its non isotonic nature which induced complexity in its implementation through virtual networks. IR component of MIND is quite different from the IR designed for iARWE because the design principle of MIND focus on the node parameters where as design of iARWE is based on link parameters. Furthermore, MIND does not judge the asymmetry of the links which cause erroneousness in channel quality measurement parameters. MIND considers back off time period as an idle time. Therefore, it may underestimate the channel interference. Unlike MIND, interference-delay aware routing metric for multi-interface mesh networks balances the load by using multi-interface multi-channel capabilities of the node but the interference is measured in a static manner which actually underestimate the quality of link in the network . Similarly channel utilization and contention window based (C2WB) metric is a interference and load aware metric which capture the inter-flow component of interference and congestion but unaware of the intra-flow component of interference and channel diversity in multi-interface WMNs .
Simple routing metrics for WMNs utilize transmission rate, packet loss ratio, and delay parameters to capture the link quality of the link, e.g., ETX, mETX, ETT, ENT, MD, and iETT. These routing metrics are simple in design and easy to implement in the routing protocol but they lack in capturing the load and interference aware parameters of the links. Furthermore, these routing metrics are unaware of channel diversity in MCMR scenarios. As a result, these routing metrics lead the traffic towards the congested areas. Hence not suitable for efficient routing in MCMR WMNs.
Interference aware routing metrics capture the inter-flow and intra-flow interference parameters along with the transmission rate and packet loss ratio to predict the quality of the link. Thus increasing the intelligence behavior of the cross layer routing metrics as interference has a critical effect on the delay and overall throughput of the network in MCMR WMNs. WCETT, MIC, iAWARE, INX and Airtime Cost are the examples of iAWARE routing metrics. Although theses routing metrics perform quiet efficiently as compared to ETX and ENT but still lack in load balancing features. Furthermore, some of them only capture the single component of interference, i.e., inter-flow or intra-flow interference although both are mandatory for quality links in real time applications. Non isotonic behavior of some of these routing metrics make their implementation in the routing protocol quite complex because they demand virtual networks to produce loop free routing.
Load aware routing metrics capture the traffic concentration and congestion parameters at node level to introduce load awareness in the routing which has a significant effect specially in multi casting and real time applications, i.e., VoIP and Video over IP in community networks. Examples of theses routing metrics are LAETT and WCETT-LB. Since they are based on EXT and ETT, so they inherit their drawback and make the traffic to route towards boundaries of the network. Moreover, they are unaware of inter-flow interference in MCMR WMNs.
Load & interference aware routing metrics, e.g., RARE, CATT. ILA, CWB, MIND, and C2WB are most recent development in routing metrics as they incorporate the transmission rate, packet loss ratio, congestion, channel diversity, and interference parameters in to the quality aware cross layer routing metric for MCMR WMNs. They actually interrelate traffic load and interference in the network and lead the network traffic towards efficient routes. The key benefit of MIND is that it uses a passive monitoring technique to overcome the overhead caused by active monitoring. Moreover, it does not inherit the drawbacks of ETX or ETT as it is not based on them.
In spite of these routing metrics, cross layer routing metric design is still an open research issue in MCMR WMNs for QoS specially in real time applications.
In this article, we provide a comprehensive taxonomy and qualitative comparison of most recent cross layer routing metrics in MCMR WMNs with respect to their design factors, characteristics and limitations. Study revealed that load & interference aware cross layer routing metrics are more efficient to pick up link quality parameters as compared to simple, interference, and load aware routing metrics in highly congested traffic areas especially in real time applications like VoIP and Video over IP in multi casting and peer-to-peer service models. Moreover, this research open up several future investigations regarding cross layer design of routing metrics in terms of load balancing and route stability mechanism in MCMR WMNs. Logical and Physical models for inter-flow and intra-flow interference measurements need to be further investigated. Furthermore, integration of WMNs with other network technologies like WiMAX, MANETS, VANETS, Wireless Sensor Networks (WSN) and cellular networks, need to be further investigated.
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