Congestion-free Routes for Wireless Mesh Networks

Abstract

Recently proposed wireless mesh routing metrics based on awareness of congestion, load or interference typically employ queue occupancy of a node's wireless interface to estimate traffic load. Queue occupancy, however, does not directly reflect the impact of channel contention from neighbor nodes. We propose an alternative called the channel load-aware (CLAW) routing metric that takes into consideration not only the traffic load within the node itself, but also the degree of interference and contention within the channel. CLAW uses local information from a node's MAC layer to estimate channel busyness and contention levels. It does not require complex computations, nor the exchange of link-level statistics with neighbors. Our preliminary results show that CLAW can identify congested regions within the network and thus enable the determination of routes around these congested areas. We present the results of simulations we conducted to evaluate the
use of CLAW in mesh-wide routing.

Full text

Congestion-free Routes for Wireless Mesh Networks
Nemesio A. Macabale Jr.*†, Roel M. Ocampo†, and Cedric Angelo M. Festin†
*Central Luzon State University, Philippines
†University of the Philippines, Philippines
E-mail:{namacabale, roel, cmfestin}@up.edu.ph
Abstract— Recently proposed wireless mesh routing metrics
based on awareness of congestion, load or interference typically
employ queue occupancy of a node's wireless interface to
estimate traffic load. Queue occupancy, however, does not
directly reflect the impact of channel contention from neighbor
nodes. We propose an alternative called the channel load-aware
(CLAW) routing metric that takes into consideration not only
the traffic load within the node itself, but also the degree of
interference and contention within the channel. CLAW uses
local information from a node's MAC layer to estimate channel
busyness and contention levels. It does not require complex
computations, nor the exchange of link-level statistics with
neighbors. Our preliminary results show that CLAW can
identify congested regions within the network and thus enable
the determination of routes around these congested areas. We
present the results of simulations we conducted to evaluate the
use of CLAW in mesh-wide routing.
Keywords - wireless mesh networks, routing, routing metric,
congestion awareness.
I. INTRODUCTION
Wireless mesh networks (WMN) have attracted significant
attention in recent years for flexible and rapid deployment of
wireless services in a wide variety of applications. These
applications include broadband home networking and automation
[1], [2], community mesh networking [3-5], in transportation
systems [6], public safety and disaster scenarios [7], [8], and in
medical applications [9].
Mesh networks are composed of wireless nodes that participate
either as routers or clients of the network. The mesh routers are
generally static or minimally mobile and serve either as dedicated
forwarding nodes, access points for clients like desktop PCs, laptops
and mobile devices, or both. Collectively, mesh routers form the
backbone of the wireless network, enabling traffic to be transported
and ensuring reachability between participating nodes.
However, despite advances in the field, there are still many
interesting research challenges in optimally routing traffic within a
wireless mesh network. Due to the shared nature of the wireless
channel, routing based on metrics traditionally used in wired
networks such as hop counts do not take into account interference
and contention within the channel shared among neighboring mesh
nodes. As a result, routing algorithms that use such "congestion-
agnostic" metrics may tend to direct multiple traffic flows naively
along known best paths, eventually congesting wireless channels
along the path and causing significant drops in network throughput.
In contrast, a routing algorithm that is able to veer the traffic flow
towards calmer regions of the network would be less likely to suffer
from such a scenario.
To address this issue, we propose a routing metric called the
channel-load aware routing metric (CLAW) designed to take into
account congestion, interference and load-imbalance issues found in
wireless mesh networks. Our design goal is to come up with a simple
yet accurate congestion / interference / load-aware routing metric
that can be incorporated into a more general concept of capacity
awareness [10]. To accomplish this task, in CLAW's design, we
avoided the need to advertise and collect link-level statistics between
neighbors. Consequently, we found out that the information provided
by the MAC layer of a wireless node would be sufficient to achieve
our goals.
The rest of the paper is organized as follows: Section II elaborates
further on the motivation for this work, and discusses similar work
found in the literature. Section III presents an analysis that leads to
CLAW's design and implementation, while Section IV discusses the
results of the preliminary evaluation. Finally, Section V concludes by
enumerating the contribution of this work.
II. RELATED WORK
In a multi-hop WMN, routing is more critical than in wired
networks, because the wireless medium is shared and is highly
dynamic [11]. Different packet flows may interfere with each other
even when they do not necessarily traverse the same path. Along a
path, neighboring nodes that share a channel compete for its use
forming a collision domain (see Figure 1). As more flows traverse
nearby paths and nodes, they compete for access to the shared
channel, eventually congesting the path and lowering throughput
significantly.
There have been several efforts to address this issue through the
use of load-aware, interference-aware, and/or congestion-aware
routing metric either singly [12-23] or in combination with multiple
metrics [24-32]. Load-aware routing algorithms such as DLAR [16]
and ALARM [30] measure load based on the number of packets
buffered in the interface queue. However, a single node's internal
load as gauged from the state of its buffers cannot reliably estimate
the level of congestion within a collision domain, because the queues
of other nodes within that domain could be empty or lightly loaded.
In this case, the heavily- and lightly-loaded nodes do not jointly paint
a consistent picture of the channel. In other words, while interface
queue occupancy accurately measures load on nodes, it does not
necessarily estimate the load on a region in a network.
To measure loaded regions, many proposals either obtain the sum
[13], [18], [20], [21], [28], [29], [31] or the average of queue length
[14], [15], [19], [25] of nodes within a collision domain. This
approach requires the data to be collected or exchanged among
neighbors, and thus generates additional overhead in terms of
bandwidth and route convergence time.
Other proposals measure channel load based on radio-frequency
(RF) channel interference [18], [23] and delay [33], [34]. However,
in most wireless environments there are other potential sources of
interference and delay aside the load in the channel, such as physical
layer impairments and bad channel conditions [11]. Hence, there
should be a way to both measure and differentiate channel and node
load. The interference awareness and load-balancing metric in [26],
[27] requires probe packets and neighbor-wide gathering of link-state
statistics, which likewise generate overhead in the bandwidth and
time needed to calculate the metric.
Some proposal that truly measure congestion, interference, and
load include LWR [12] and C2WB [17]. LWR however combines
multiple metrics to achieve its goal, requiring more calculations than
CLAW, which relies on a single metric. In addition, LWR collects
information from neighbors. Similarly, C2WB requires probing
packets, neighbor information, and a complex computation. In
addition, it requires a change in the MAC layer protocol,
In proc eedings of the 2011 International Symposium on Multimedia and Communication Technology

We proposed CLAW to address the issues mentioned, through the
use of node-local information, and by requiring only simple
computations. In our investigation, we found that the MAC layer has
all the information needed to accurately estimate channel load,
interference, and node load. CLAW can be used by routing protocols
as an alternative to existing congestion awareness mechanisms either
in single channel or multi-channel environments.
III. DESIGN AND IMPLEMENTATION
Our analysis begins by looking at a node j's collision domain. It is
comprised of all nodes within j's carrier sensing range that operate on
the same channel. Transmissions of these nodes may interfere with
transmissions from j. This is illustrated in Fig. 1, with the
simplifying assumption that the carrier sensing range is circular. The
nodes in this diagram are furthermore assumed to operate using the
IEEE 802.11b wireless standard.
Because of the shared nature of the channel, the load on a node
affects all the neighbor nodes that can sense its transmission. That is,
an idle node will respond to a new traffic flow request like a busy or
loaded node if a neighbor within its carrier sensing range is in fact
busy or loaded. Hence, identifying busy regions, rather than busy
nodes, is a more effective approach in avoiding congestion,
preventing interference, and distributing traffic loads. The routing
protocol may then assign a lower cost to the next-hop node that has
the least busy collision domain. This is the basic intuition behind,
and our motivation for, the development and use of the CLAW
metric.
A. Channel Load
From the point of view of a node, the channel is in use, i.e. busy,
when the node is either transmitting or receiving a packet from the
channel, or if it senses any transmission energy that hinders
successful transmission such as those resulting from collisions,
interference, or other forms of noise. In addition, the channel may
likewise be considered busy when the node is blocked from
accessing the channel, such as due to the back-off and defer periods
in the distributed coordination function (DCF) in the IEEE 802.11
standard [35]. If all these events can be classified into one of two
fractional components of time, called TsensedEnergy and
TblockedForAccess, then channel load is the total fraction of time that a
node is busy due to any of these contributing events. Equation (1)
expresses this definition of channel load.
We derived this definition from the result of a simple experiment
with three IEEE 802.11b nodes
placed within a single collision domain. In the experiment, a node
Node0 sent packets to another node Node1 until channel saturation,
while a third node Node2 silently observed. Although the physical
layer of all three nodes sensed the channel with the same degree of
actual utilization (i.e. amount of time packets occupied the channel),
the sender Node0 was loaded/busier (see Fig. 2) than the the receiver
Node1 and the observer Node2, all the way through saturation,
because of the blocking time (back-off and defer periods) in the DCF
functionality of IEEE 802.11b[35]. At saturation, although the
sender viewed channel load to be 100% the receiver and observer
only viewed the channel as around 78% loaded. It is interesting to
note that the 78% load approximated the ratio of time the packets
propagating in the air occupied the channel. This is comparable to
the throughput saturation encountered at around 80% channel
busyness by others [36]. Generally, saturation throughputs have not
been achieved at 100% busyness [36], [37] as may be intuitively
expected from such a metric, because the back-off and defer periods
in the IEEE 802.11 MAC protocol were not taken into account. In
contrast, by taking these into account, the CLAW metric is able to
account for the missing ~20% busyness. Thus, not only can CLAW
effectively identify busy regions, in addition, it can discriminate
between loaded and non-loaded nodes within such busy regions.
Ch _ load=TsensedEnergyTblockedForAccess
(1)
where :
TsensedEnergy is that fraction of time that a node is transmittinga packet to
thechannel , is receiving a packet from the channel ,is sensing
transmissionenergy be it collision , interference ,or noise 
in thechannel
TblockedForAccess isthat fraction of time that anode
is backing -off ordeferring
Ch _ load=busy _ count
scan _ count
(2)
CLAW jt=1−×CLAW jt−1×Ch _ load j
(3)
where :
CLAW jt The value of CLAW at time t
α isa tunable parameter : 0≤ α ≤ 1, here 0.5is used
Ch _load j isthe current observed channel load at node j
CLAW jt−1 isthe previous CLAW
t refers tothe current measuring period
CLAW Pt= ∑
j∈P
CLAW jt
(4)
where :
CLAW Ptisthe equivalent path metric based on CLAW
01258
0
0.2
0.4
0.6
0.8
1
1.2
Estimated
Channel Load
Estimated
Packet I n the Air
Node 0 Ch_load
Node 1 Ch_load
Node 2 Ch_load
input traffic (Mbps)
percent of time
Nod e i's collisio n
do main
01 02 03 04 05
06 i08 09 10
11 12 j14 15
16 17 18 19 20
21 22 23 24 25
Nod e j's collissio n
do main
k
Figure 1: A Wireless Mesh Network with 25 nodes
Figure 2: Channel Load Measurement

It is also worth noting that we do not make any assumption about
the operating channel of a collision domain. Our analysis only
require that i and j's collision domain operate on the same channel. If
some collision domains operate over different channels the analysis
will follow the same process. In addition, the analysis (TsensedEnergy
and TblockedForAccess) will still be valid had a different mac layer
technology been used. Thus, CLAW is suitable to single- and multi-
radio or multi-channel mesh networks.
B. Implementation
To estimate the channel load, we simply monitor how the MAC
layer views the channel. The MAC layer senses the busyness of the
channel through carrier sensing (provided by the physical layer) and
virtual carrier sensing through its NAV (network allocation vector)
[35]. Within a defined observation period the MAC layer is queried
whether it senses the channel to be busy, backing-off, or deferring.
The number of times where the MAC layer reports any of these three
conditions (busy_count), divided by the number of times the MAC
layer is queried (scan_count) becomes the estimated channel load as
defined in Eq. (2). It is interesting to note that the channel load
computed using Eq. (2) consistently matched the estimated channel
load (for Node0) and actual fractional packet-in-the-air time (for
Node1 and Node2) as observed and presented in Fig. (2).
To account for sudden changes in traffic and the dynamic
behavior of the wireless channel, we employ a moving average for
the channel load using a tunable parameter α. We initially used
α=0.5, although further experimentation and study may suggest other
values. The CLAW metric is thus defined in Eq (3) as the moving
average of the estimated channel load. Equation (4) is the equivalent
path metric based on CLAW.
IV. SIMULATION AND DISCUSSION
We performed preliminary qualitative and quantitative
experiments to evaluate the performance of our proposed routing
metric using ns-2 [38] with the OLSR extension as used in [39]. We
wanted to quickly test whether our metric would in fact avoid busy
regions of the network, and whether it would achieve better
throughput compared to hop count-based routing.
The first set of simulations were designed to show whether the
CLAW metric could steer flows away from loaded regions of the
network. The set-up shown in Fig. 3, similar to that in [36] involved
25 nodes uniformly distributed in a grid of 800 x 800 square meters.
Data-rates between nodes is set to 11 Mbps. For the main traffic
flow, FTP bulk traffic over TCP was used, with the packet size set of
1040 bytes (NS2 default size [38]). Constant bit rate (CBR) is used
for the interference flow. To simplify the simulation both
transmission range and sensing range were set to 250m, while the
distance between nodes was set to 176 m. At the start of the
simulation, the interference flow between nodes 11 and 12 was
initiated, creating the busy region indicated by the two circular areas
in Fig. 3. With a traditional hop count metric, packets traversed the
path 00-06-12-18-24. With CLAW, packets followed the path 00-
01-02-03-09-14-19-24, effectively avoiding the busy region in the
network.
In the second set of simulations, the interfering traffic was varied
from 0, 0.5 Mbps, 1 Mbps, 1.5 Mbps, …, 5 Mbps in order to observe
network behavior and performance with varying degrees of
busyness. Fig. 4 compares the throughput attained by the main flow
with hop count and CLAW routing metrics. Each data point in the
graph represents the average from 10 simulation runs. The dramatic
decrease in the throughput of the network that used hop count
routing, especially around 2-2.5 Mbps interference traffic, was due to
packet drops within the busy region. In contrast, CLAW was able to
avoid the busy region, resulting in significantly better end-to-end
throughput even with high levels of busyness within the network.
V. CONCLUSIONS
We propose the channel-load aware (CLAW) routing metric to
address issues in congestion, interference and load-imbalance
problem in wireless mesh networks. CLAW does not require
complex computations, nor any exchange or collection of neighbor-
wide link-level statistics. Its simplicity allows it to be easily
integrated, if necessary, with other capacity-aware routing metrics
with minimal overhead. Analysis also shows it is suitable to single-
and multi-channel or multi-radio mesh networks.
Initial simulation results demonstrated its ability to effectively
estimate channel busyness and enable flows to avoid congested
regions.
Although it shows promise, our initial comparison with hop-count
routing merely demonstrates CLAW's basic ability to support
congestion-free routing. A more comprehensive performance
comparison with similar congestion-aware metrics is therefore in
order. Ultimately, the usefulness of this metric can only be fully
realized through actual, working implementations, rather than
through theoretical simulations. We will hopefully address all of
these in our future work.
ACKNOWLEDGMENT
This work has been supported by the Engineering Research and
Development for Technology (ERDT) Consortium, Department of
Science and Technology – Science Education Institute (DOST-SEI),
Republic of the Philippines.
01 02 03 04
05 06 08 09
10 11 12
15 16 17 18 19
20 21 22 23 24
13 14
07
00
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0
100
200
300
400
500
600 CLAW
HopC
Interfering Traff ic (Mbps)
Throughput (k bps)
Figure 4: Throughput comparison between CLAW and Hop-
count metrics
Figure 3: Node 00 originates an FTP flow towards Node 24.
CBR traffic between Node 11 and Node 12 form an
interference flow. While hop-count based routing would
result in the straight-line path 00-06-12-18-24, CLAW
routes the flow through 00-01-02-03-09-14-19-24,
avoiding busy regions

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