A Throughput Optimizing Routing
Protocol for Wireless Mesh
Networks
Seminar ID: 1582
A Technical Seminar Report
submitted for fulfillment of
the requirements for the
Degree of Bachelor of Technology
Under Biju Pattnaik University of Technology
Submitted By
NISHANT KUMAR
Roll No. # CSE 201010340
March- 2013
Under the guidance of
Prof. Jaydip Sen
NATIONAL INSTITUTE OF SCIENCE & TECHNOLOGY
PALUR HILLS, BERHAMPUR, ODISHA –761008, INDIA
ABSTRACT
Wireless mesh networks (WMNs) are evolving as a key technology for nextgeneration wireless networks showing raid progress and numerous applications. These
networks have the potential to provide robust and high throughput data delivery to
wireless users. In a WMN, high speed routers equipped with advanced antennas,
communicate with each other in a multi-hop fashion over wireless channels and form
a broadband backhaul. However the throughput of a WMN may be severely degraded
due to presence of some selfish routers that avoid forwarding packets for other nodes
even as they send their own traffic through the network. This report presents an
algorithm for detection of selfish nodes in a WMN. It uses statistical theory of
inference for reliable clustering of the nodes and is based on local observations by the
nodes. Simulation results show that the algorithm has a high detection rate while
having a low rate of false positives.
i
ACKNOWLEDGEMENT
I would like to express my sincere gratitude to Prof. Jaydip Sen, Faculty Advisor, for
his valuable advice and constant support throught the period of my seminar and mini
project preparation.
I owe special thanks to Mr. Ranjit Kumar Behera technical Seminar co-ordinator
for giving his valuable suggestion for this event.
We thank Prof. Sangram Mudali, for his continued drive for better quality in
everything that happens at NIST and we thank our Placement Director, Prof. Geetika
Mudali for her constant encouragement. This report is a small contribution towards
the greater goal.
Last but not the least, I express my heartfelt gratitude to my parents for their support
and effort in making what I am and to my friends whose help was always available
whenever I needed it.
NISHANT KUMAR
ROLL NO-201010340
BRANCH-CSE
ii
TABLE OF CONTENT
ABSTRACT .................................................................................................................... i
ACKNOWLEDGEMENT .............................................................................................ii
TABLE OF CONTENT ................................................................................................iii
LIST OF FIGURES ...................................................................................................... iv
1. INTRODUCTION ..................................................................................................... 1
2. RELATED WORK .................................................................................................... 3
3. WMN ROUTING CHALLENGES ........................................................................... 5
4. THE PROPOSED ROUTING PROTOCOL ............................................................. 8
4.1. Estimating reliability of routing paths................................................................. 8
4.2. Use of multi-point relay nodes ............................................................................ 8
4.3. Estimating available network bandwidth ............................................................ 9
4.4. Routing through the fixed network ..................................................................... 9
5. SIMULATION RESULTS ...................................................................................... 11
5.1. Control Overhead .............................................................................................. 12
5.2. Network throughput .......................................................................................... 13
6. CONCLUSION ........................................................................................................ 15
REFERENCES ............................................................................................................ 16
iii
LIST OF FIGURES
Figure 3.1. The Architecture of a WMN........................................................................ 5
Figure 5.1 Control overhead (bytes) vs. number of data source nodes (50 nodes in the
network) ..................................................................................................... 12
Figure 5.2 Control overhead (bytes) vs. number of data source nodes (75 nodes in the
network) ..................................................................................................... 13
Figure 5.3. Network throughput (Bps) vs. number of data source nodes (50 nodes in
the network) ............................................................................................... 14
Figure 5.4. Network throughput (Bps) vs. number of data source nodes (75 nodes in
the network) ............................................................................................... 14
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1. INTRODUCTION
Wireless mesh networking has emerged as a promising concept to meet the challenges
in next generation networks such as providing flexible, adaptive, and reconfigurable
architecture while offering cost-effective solutions to the service providers. Unlike
traditional Wi-Fi networks, where each access point (AP) is connected to a wired
network, in WMNs only a subset of the APs are required to be connected to a wired
network. The APs that are connected to the wired network are called the Internet
gateways (IGWs), while the APs that do not have wired connections are called the
mesh routers (MRs). The MRs are connected to the IGWs using multi-hop
communication. Due to the recent research advances in WMNs, these networks have
been used in numerous applications such as in home networking, community and
neighborhood monitoring, security surveillance systems, disaster management and
rescue operations etc.
In a community-based WMN, a group of MRs managed by different operators form
an access network to provide last-mile connectivity to the Internet. As with any enduser supported infrastructure, ubiquitous cooperative behavior in these networks
cannot be assumed a priori.
Preserving scarce access bandwidth and power, as well as security concerns may
induce some selfish users to avoid forwarding data for other nodes, even as they send
their own traffic through the network. The selfish behavior of an MR increases the
latency in packet delivery and packet drops and decreases the network throughput in a
WMN.
To address the issue of detection of selfish nodes in a WMN, this report presents a
scheme that is based on local observations in the nodes. The scheme is applicable for
on-demand protocols like AODV, and uses statistical theory of inference and
clustering techniques to make a robust and reliable classification of the nodes. To
increase the detection accuracy, the scheme also introduces some additional fields in
the headers of the AODV packets. The rest of the report is organized as follows.
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Section 2 presents some existing related work in the literature. Section 3 gives a brief
background of the AODV protocol and a finite state machine model of a node.
Section 4 discusses the proposed scheme. Section 5 presents simulations results.
Section 6 identifies some future work and concludes the report
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2. RELATED WORK
Although significant amount of work has been done on routing in wireless networks,
very little work has been done for WMNs. Most of the routing protocols for MANETs
such as AODV and DSR use hop-count as the routing metric. However, this is not
well-suited for WMNs. The basic idea in minimizing the hop count is that it reduces
delay and maximizes the throughput. But the assumption here is that the links in the
path are either perfect or do not work at all, and all links are of equal bandwidth. A
routing scheme that uses the hop-count metric does not take link quality into
consideration. A minimum hop-count path has, on the average, longer links between
the nodes present in the path compared to a higher hop-count path. This reduces the
signal strength received by the nodes in that path and thereby increases the loss ratio
at each link [2]. Hence, it is always possible that a two-hop path with a good link
quality provides higher throughput than a one-hop path with a poor link quality.
Moreover, wireless links usually have asymmetric loss rate [3]. Hence, new routing
metrics based on link quality are proposed such expected transmission count
(ETX),per-hop round-trip time (RTT), and per-hop packet pair. Different approaches
have been suggested by researchers for designing routing protocols for WMNs. In
[4],a QoS routing over OLSR protocol has been proposed that takes into account
metrics such as bandwidth and delay where the source node proactively changes a
flow’s next hop in response to the change in available bandwidth on its path. In
[5],the authors have proposed a link quality source routing (LQSR) protocol. It is
based on DSR and uses ETX as the routing metric. A new routing protocol called
multi-radio link quality source routing (MR-LQSR) is proposed in [6]. The process of
neighbour node discovery and propagation of link metric are same as those in DSR.
However, assignment of link weight and computation of the path weight is different.
A QoS enabling routing algorithm for mesh based wireless LAN architecture has been
proposed in
[7] where the wireless users form an ad hoc peer-to peer network. The
authors also have proposed a protocol for MANET called ad hoc QoS on-demand
routing (AQOR) [8]. In [9], the authors have shown that if a weighted cumulative
expected transmission time [6] is used in a link state routing protocol, it does not
satisfy the isotonicity property of the routing protocol and leads to formation of
routing loops. To avoid routing loops, an algorithm is proposed that uses metric of
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interference and channel switching (MIC) as the routing metric. The Mesh Cluster
architecture [10 ] addresses important issues in WMNs such as auto configuration of
mesh and client nodes, routing and load balancing in the infrastructure. The routing is
performed via AODV-ST, a protocol that proactively maintains spanning trees rooted
at the gateways. The mobility of the clients is managed by DHCP protocol. In contrast
to the above approaches, the proposed protocol performs an on-demand route
discovery using multiple metrics like bandwidth, delay, and reliability of the links and
provides a routing framework that can support high network throughput with a
minimum control overhead.
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3. WMN ROUTING CHALLENGES
This section first presents the generic architecture of a WMN and then discusses some
specific challenges in designing routing algorithms for such networks.
Figure 3.1. The Architecture of a WMN
The architecture of a hierarchical WMN consists of three layers as shown in Figure
3.1. At the top layers are the Internet gateways (IGWs) that are connected to the wired
Internet. They form the backbone infrastructure for providing Internet connectivity to
the elements in the second level. The entities at the second level are called wireless
mesh routers (MRs) that eliminate the need for wired infrastructure at every MR and
forward their traffic in a multi-hop fashion towards the IGW. At the lowest level are
the mesh clients (MCs) which are the wireless devices of the users. Internet
connectivity and peer-to-peer communications inside the mesh are two important
applications for a WMN. Therefore design of an efficient and low-overhead routing
protocol that avoids unreliable routes, and accurately estimate the end-to-end delay of
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a flow along the path from the source to the destination is a major challenge. In
particular, measuring link reliability, estimating the end-to-end delay of a path and
reducing control overhead are three important challenges in design of routing
protocols for WMNs. These are briefly discussed below. Measuring link reliability: It
has been observed that in wireless ad hoc networks nodes receiving broadcast
messages introduce communication gray zones [11].
In such zones, data messages cannot be exchanged although the hello messages reach
the neighbours. This leads to a disruption in communication among the nodes. Since
the routing protocols such as AODV and WMR [7] rely on control packets like
RREQ, these protocols are highly unreliable for estimating the quality of wireless
links. Due to communication gray zone problem, nodes that are able to send and
receive bidirectional RREQ packets sometimes cannot Send/receive data packets at
high rate. These fragile links trigger link repairs resulting in high control overhead for
routing protocols. Therefore, designing a robust and reliable link quality estimator is
crucial for reducing the control overhead. End-to-end delay estimation: An important
issue in a routing protocol is end-to-end delay estimation. Current protocols estimate
end-to-end delay by measuring the time taken to route RREQ and RREP packets
along the given path. However, RREQ and RREP are different from normal data
packets and hence they are unlikely to experience the same levels of delay and loss as
data packets. It has been observed through simulation that a RREP-based estimator
overestimates while a hop-count-based estimator underestimates the actual delay
experienced by the data packets [12].
The reason for the significant deviation of a RREP-based estimator from the actual
end-to-end delay is interference of signals. The RREQ packets are flooded in the
network resulting in a heavy burst of traffic. This heavy traffic causes inter-flow
interference in the paths. The unicast data packets do not cause such events.
Moreover, as a stream of packets traverse along a route, due to the broadcast nature of
wireless links, different packets in the same flow interfere with each other resulting in
per-packet delays. Since the control packets do not experience per-packet delay, the
estimate based on control packet delay deviate widely from the actual delay
experienced by the data packets. Computing a reliable estimate of the end-to-end
delay is, therefore, another challenge in design of a routing protocol for WMNs.
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Reduction of control overhead: Since the effective bandwidth of wireless channels
vary continuously, reduction of control overhead is important in order to maximize
throughput in the network. Protocols like reactive protocols like AODV and DSR use
flooding of RREQ packets for route discovery. This consumes a high proportion of
the network bandwidth and reduces the effective throughput. An efficient routing
protocol should avoid using flooding as far as possible.
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4. THE PROPOSED ROUTING PROTOCOL
The goal of the proposed routing protocol is to establish a route from the source to the
destination that allows traffic flow within a guaranteed end-to-end latency using the
minimum control overhead. The salient features of the proposed algorithm are now
discussed in the following subsections.
4.1. Estimating reliability of routing paths
Every node estimates the reliability of each of its wireless links to its one-hop
neighbour nodes. For computing the reliability of a link, the number of control
packets that a node receives in a given time window is used as a base parameter. An
exponentially weighted moving average (EWMA) method is used to update the link
reliability estimate. Every node maintains estimates the reliability of each of its links
with its neighbours in a link reliability table. The reliability for an end-to-end routing
path is computed by taking the average of the reliability values of all the links on the
path. The use of path with the highest reliability reduces the overhead of route repair.
The paths with reliability values less than 0.5are never selected for routing.
4.2. Use of multi-point relay nodes
The proposed routing protocol uses the multi-point relay (MPR) nodes like the
optimized link state routing (OLSR) protocol [13] in order to reduce the control
overhead in routing. In order to under the concept of MPR let us consider Figure 4.1.
The control messages sent by an IGW, called GW_INFO messages are never flooded
throughout the entire WMN; they are transmitted inside the corresponding subnet
only if the neighbour which forwarded it has been validated as bi-directional (i.e., the
sender is reachable by the receiver via the reverse link). The bi-directionality of a link
is determined by appending the list of neighbours in the periodic hello messages. In
this way, if a node finds itself in the list of neighbours advertised by its own
neighbour, the link is considered bi-directional. This additional list of neighbours in
the hello messages is used to compute the MPR of a node. The objective of
identifying the MPRs is to minimize control packet overhead. When MPRs are used,
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it is not necessary to send a message to all the nodes in a network when that message
is required to reach all the nodes. If we visualize the WMN as a connected graph, the
objective is to find the minimum subset of nodes which covers the whole graph. With
a denser network, the benefits of using MPRs are more [1]. The proposed protocol
exploits the advantages of MRPs in order to reduce the control overheads of RREQ
messages.
4.3. Estimating available network bandwidth
In addition to computation of path reliability and use of MPRs, it is also necessary
that the effective bandwidth in a routing path is reliably estimated. This is extremely
important to support real-time applications since these applications require a
guarantee for a minimum available bandwidth. In the proposed protocol the available
bandwidth for a wireless link is estimated using its end-to-end delay and loss of
packets due to congestion.
The packet-loss due to congestion in the link is estimated as follows. In a wireless link
packet loss may happen due to two reasons: (i) loss due to faulty wireless links and
(ii) loss due to network congestion. The radio link control (RLC) layer segments an IP
packet into several RLC frames before transmission and reassembles them into an IP
packet at the receiver side. An IP packet loss occurs when any RLC frame belonging
to an IP packet fails to be delivered. When this happens, the receiver knows the RLC
frames reassembly has failed and the IP packet has been lost due to wireless error.
Meanwhile, the sender detects retransmission time out (RTO) of the frame and
discards all the RLC frames belonging to the IP packet. This enables the sender to
compute packet drop rate in the wireless links. Moreover, using the sequence numbers
of the IP packets received at the receiver, it possible to differentiate the packet loss
due to link error and packet loss due to congestion [14].
4.4. Routing through the fixed network
In the proposed algorithm, the routing efficiency is further enhanced by occasional
routing of packets through the fixed wired network backbone. Since the wired
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network backbone provides higher available bandwidth with more reliable links, it is
advantageously exploited for intra-mesh message communication. Since the IGWs
(Figure 4.1) periodically announce their presence in the network through beacon
messages, every mesh client knows the hop count from itself to its selected gateway.
In the proposed protocol, the RREQ messages include this hop information. When the
destination receives the RREQ, since it also knows its distance from its gateway, it
checks whether it is better (in terms of number of hops) to route the packet through
the wireless nodes (mesh) or through the fixed network. In case the destination node
finds that the better route is through the fixed network, the proposed routing algorithm
routes the RREP message through the wired network using the default route.
Therefore, in such situations, the forward route is established between the source and
the destination through the wired network, while the reverse route is set up through
the WMN. This approach is known as CIRCULAR routing [1].This approach,
improves the performance of bi-directional flows between a source and destination
pair (as in a TCP connection) since the nodes in the forward and in the reverse routes
are on node-disjoint paths and do not contend for access of the wireless medium.
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5. SIMULATION RESULTS
The proposed protocol has been implemented in the network simulator ns2 version
2.29 [15]. The simulated network consists of 50 and 75 static nodes randomly
distributed in the simulation area forming a dense WMN. Four IGWs are placed at
locations (100,100), (100, 900), (900, 100) and (900, 900). The simulation parameters
are presented in Table 1. The MAC protocol used is IEEE 802.11b CSMA/CA
protocol. The performance of the proposed protocol is compared with the protocol
presented in [1]. Two important metrics- control overhead and network throughput are
studied in the simulation.
Table 1. The simulation parameters
Pararmeter
Values
Simulation area
1000 m * 1000 m
Propagation channel frequency
2.4 GHz
Raw channel bandwidth
2 Mbps
Simulation duration
600 s
Traffic type
CBR UDP
Packet size
512 bytes
Data rate in the network
32 Kbps
IGW hello packet broadcast interval
200 ms
No. of source nodes
15, 25, 35
IP queue scheduler
Strict priority
Propagation model
Two ray ground
Wired network bandwidth
100 mbps
Delay in wired links
1.8 ms
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5.1. Control Overhead
For studying the control overhead, four algorithms are considered. The DYNMOUM
algorithm in [16], the DYNMOUM with MPR, in [1] DYNMOUM with MPR and
circular routing (CR) [1] and the proposed algorithm compared with respect to their
control overhead in routing. The number of data source nodes is varied from 15 to 35
and the control overhead in bytes is studied for 50 nodes and 70 nodes networks
respectively. The results are presented in Figure 2 and Figure 4. It may be easily
observed that the proposed protocol has the least control overhead among all the four
protocols. The reason for the less control overhead in the proposed protocol is due to
the fact that there are much less number of route errors and route repairs due the
reliable link quality estimation and bandwidth estimation technique used in the
protocol, which were absent in the other three protocols. In addition, it exploits the
advantages of using MPRs and the circular routing. The MPRs reduce the overhead
by controlled flooding and the circular routing reduces the overhead by routing some
of the RREPs through the fixed network. The results clearly show that the proposed
protocol will be very much suitable in dense mesh networks with high number of data
sources.
Figure 5.1 Control overhead (bytes) vs. number of data source nodes (50 nodes in the
network)
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Figure 5.2 Control overhead (bytes) vs. number of data source nodes (75 nodes in the
network)
5.2. Network throughput
The performance of the protocol is also studied with respect to its ability to enhance
network throughput. It may be intuitively clear that the reduction in control overhead
should lead to a corresponding increase in the network throughput. Figure 4 and
Figure 5 represent the data throughput in the network under varying number of source
nodes with total number of nodes in the network being 50 and 70 respectively. It may
be observed that the proposed protocol produces maximum network throughput
among all the four protocol studied. There are various factors that contribute to the
enhanced throughput with the proposed protocol. First, the network throughput
significantly increases when MPRs are used due to reduced number of collision in the
wireless medium resulting in fewer numbers of retransmissions required. Therefore
wireless bandwidth is used much more efficiently. Moreover, circular routing
improves the performance further due to use of fixed network that has higher effective
bandwidth and lower latency. Accurate estimate of link quality also contributes to
higher throughput, since packets are always forwarded through the link that has the
highest effective bandwidth. Finally, efficient bandwidth estimation ensures that there
will be minimum packet retransmission required.
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Figure 5.3. Network throughput (Bps) vs. number of data source nodes (50 nodes in the
network)
Figure 5.4. Network throughput (Bps) vs. number of data source nodes (75 nodes in the
network)
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6. CONCLUSION
WMNs present a promising technology for providing low cost last mile broadband
Internet connectivity through multi-hop communications. Designing an efficient and
reliable routing protocol for WMNs is a challenging issue. This report has presented
an efficient routing protocol that has very low control overhead and high network
throughput when the number of source nodes in a WMN is large. By robust
estimation wireless link quality and the available bandwidth in the wireless route and
exploiting the benefits of using MPRs and circular routing technique, the protocol is
able to sustain a high level of throughput while reducing the control overhead.
Simulation results have shown that the proposed protocol is more efficient than some
of the current routing protocols for WMNs.
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