Design of Non-orthogonal Multi-channel Sensor Networks

Conference PaperinProceedings - International Conference on Distributed Computing Systems · January 2010with39 Reads
DOI: 10.1109/ICDCS.2010.37 · Source: DBLP
Conference: 2010 International Conference on Distributed Computing Systems, ICDCS 2010, Genova, Italy, June 21-25, 2010

A critical issue in wireless sensor networks (WSNs) is represented by the network throughput. To meet the throughput requirement, researchers propose multi-channel design in 802.15.4 networks to better utilize the wireless medium and avoid the co-channel interference. However, traditional orthogonal channel design restricts the number of channels and limits the throughput performance. We argue that the orthogonality is not necessary for multi-channel design in WSNs. In this paper, we investigate the feasibility of non-orthogonal channel design. In our experiment, we observe that with nonorthogonal transmission, the effect of interference comes from co-channel and inter-channel is different. More specifically, the inter-channel interference is tolerable with certain channel center frequency distance (CFD). According to that, we propose a novel scheme DCN (Dynamic CCA-threshold for Non-orthogonal transmission) which adjusts the CCA-threshold to enable the concurrent transmissions on adjacent non-orthogonal channels and thus improve the overall network throughput performance. Through comprehensive experiments on our testbed, we verify that our DCN achieves about 38.4% ∼ 55.7% throughput improvement in general network configurations comparing to the default ZigBee design.


Available from: Qian Zhang
Design of Non-orthogonal Multi-channel Sensor
Xing Xu
Hong Kong University of
Science and Technology
Ji Luo
Hong Kong University of
Science and Technology
Qian Zhang
Hong Kong University of
Science and Technology
Abstract—A critical issue in wireless sensor networks (WSNs)
is represented by the network throughput. To meet the through-
put requirement, researchers propose multi-channel design in
802.15.4 networks to better utilize the wireless medium and avoid
the co-channel interference. However, traditional orthogonal
channel design restricts the number of channels and limits the
throughput performance. We argue that the orthogonality is not
necessary for multi-channel design in WSNs.
In this paper, we investigate the feasibility of non-orthogonal
channel design. In our experiment, we observe that with non-
orthogonal transmission, the effect of interference comes from
co-channel and inter-channel is different. More specifically, the
inter-channel interference is tolerable with certain channel center
frequency distance (CFD). According to that, we propose a
novel scheme DCN (Dynamic CCA-threshold for Non-orthogonal
transmission) which adjusts the CCA-threshold to enable the
concurrent transmissions on adjacent non-orthogonal channels
and thus improve the overall network throughput performance.
Through comprehensive experiments on our testbed, we verify
that our DCN achieves about 38.4% 55.7% throughput
improvement in general network configurations comparing to
the default ZigBee design.
Index Terms—Multi-channel, orthogonal channel, interference,
CCA-threshold, wireless sensor networks.
As an emerging technology, wireless sensor networks
(WSNs) have been designed to support a wide range of po-
tential applications, including environment monitoring, event
detection as well as information collection [1][2][14]. In these
applications, large number of sensors are deployed in the
interested region, communicate with each other using wireless
medium. Due to the shared nature of wireless medium, the
interference among multiple transmissions that use the same
channel becomes a fundamental issue [3]. Existing works [20]
show that concurrent transmissions in the same channel is
a great menace to the communication, since it will lead to
collision where the collided packets would not be decoded
successfully. It is well known that the current WSN hard-
ware, such as Micaz and Telos that use the CC2420 radio,
already provide multiple frequencies. Therefore, designing
multi-channel based communication protocols in WSNs to
improve network throughput and provide high performance
communication services is then a nature way [18].
For multi-channel based communication protocol design in
WSNs, one of the most important parameters is the number
of channels which can actually be used. The CC2420 radio
chip that follows ZigBee standard [10] provides 16 channels,
with 5MHz as the center frequency distance (CFD) between
neighboring channels. However, as Wu et al. pointed out
[18], not all channels can be used in a single sensor network
to provide parallel transmissions because of close channel
interferences and interferences caused by other wireless net-
works. To address the channel scarcity issue, several studies
[7][15] have been proposed to utilize overlapping channels,
which were targeted for 802.11 networks only. Similar studies
in 802.15.4 networks are limited. Through our experiment,
we find that 802.15.4 networks show unique interference
characteristics which cannot be captured by existing 802.11
models. More importantly, such uniqueness will benefit multi-
channel design of 802.15.4 networks particularly and plays a
key role in our study.
For a given network with a certain spectrum band, non-
orthogonal channel assignment provides more available chan-
nels. However, comparing to the traditional orthogonal channel
assignment, it introduces a critical challenge, i.e., the inter-
channel interference may affect the transmission significantly.
Thus, to decide a reasonable frequency distance between
neighboring-channels, the trade-off between larger number
of channels and weaker inter-channel interference has to be
carefully considered. Through our following experiments, we
verify that assigning each channel with the channel distance
that guarantees orthogonality (i.e., 9MHz for 802.15.4) cannot
fully utilize the bandwidth medium. Moreover, the default
CFD setting of ZigBee, i.e. 5MHz, is inefficient as well.
Our interesting observation is that the assignment based on
a smaller CFD, e.g., 3MHz, provides better bandwidth uti-
lization. Specifically, if we assign each channel with the CFD
as 3MHz and generate saturated traffic on each channel, the
overall throughput on a given spectrum bandwidth would be
improved significantly comparing to the orthogonal channel
assignment scheme. Comparing with the result obtained with
the ZigBee default setting, more than 40% throughput gain
could be achieved by leveraging smaller CFD, showing that
smaller CFD better exploits the bandwidth.
To further study how to better leverage the non-orthogonal
channels with smaller CFD, e.g., 3MHz, we verify the feasi-
bility of concurrency between assigned non-orthogonal chan-
nels. Different to the collision of co-channel interference that
collided packets cannot be both decoded successfully, packets
transmitted simultaneously from two non-orthogonal channels
2010 International Conference on Distributed Computing Systems
1063-6927/10 $26.00 © 2010 IEEE
DOI 10.1109/ICDCS.2010.37
2010 International Conference on Distributed Computing Systems
1063-6927/10 $26.00 © 2010 IEEE
DOI 10.1109/ICDCS.2010.37
Page 1
(e.g., CFD=3MHz) could be decoded successfully in most
power settings or just be slightly corrupted. According to this
unique observation in 802.15.4 networks, we propose a new
scheme DCN (Dynamic CCA-threshold for Non-orthogonal
transmission) that can dynamically modify CCA-threshold
By doing so, non-orthogonal channels possess the capability of
concurrency, which is usually supported by orthogonal channel
assignment. We also discuss that the packet recovery scheme
could be integrated with DCN to rescue the slightly corrupted
packets in some special cases. The empirical experiments show
that our scheme DCN can achieve about 38.4% 55.7%
bandwidth throughput improvement with CFD=3MHz, com-
paring to the default multi-channel settings of ZigBee.
In this paper, we for the first time investigate the fea-
sibility of non-orthogonal multi-channel design for ZigBee-
based WSNs and propose a CCA-threshold adaptation scheme
to maximize the bandwidth throughput. As a summary, we
have the following contributions: 1) we empirically verify
that the default setting of CFD=5MHz in ZigBee is quite
conservative. The assignment with smaller CFD could achieve
better throughput on a given bandwidth; 2) we observe that
different to co-channel interference, the inter-channel interfer-
ence introduced by non-orthogonal assignment is tolerable in
most of the communication settings (e.g., transmission power,
CFD). The collided packets caused by such inter-channel
interference could be decoded successfully or only have a
small portion of bit errors. Thus, we propose a new scheme
DCN to exploit the concurrencies on non-orthogonal channels;
3) with the comprehensive experiments on a real testbed,
we verify that our DCN could obtain significant throughput
improvement comparing to the traditional ZigBee setting.
The rest of the paper is organized as follows. We discuss
the related work in Section II. In Section III, we introduce a
concrete example which motivates our investigation. Section
IV is devoted to the detail analysis of non-orthogonal multi-
channel design, which consists of the study on the co-channel
interference and inter-channel interference; while Section V
presents our scheme DCN. The extensive experiments of our
proposed design is shown in Section VI and in Section VII we
will have a further discussion on the packet recoverability and
the upper bound of bandwidth throughput with DCN. Finally,
a conclusion for the paper will be made in Section VIII.
It has been well studied that using multiple channels can
significantly enhance the network capacity in wireless net-
works [17]. In recent years, there are also some studies on
efficient delivery in wireless sensor networks by making use
of multi-channel, such as MCMAC [6], TMMAC [21], MMSN
[22], etc. However, all these works assume the channels are
orthogonal and they all evaluated by simulation only.
CSMA exploits clear channel assessment (CCA) method, which deter-
mines the state of channel by checking whether the in-channel energy is
above a given threshold (CCA-threshold) and transmission can only happen
if the channel appears idle.
Wu et al. proposed TMCP [18], a realistic multichannel
protocol based on empirical experiments. In their conclusions,
they complained that the small number of available channels
limits the multi-channel design of WSNs. Therefore, they find
fully orthogonal and high-quality channels first and partition
the whole network into sub-trees according to the number
of available channels. However, in our experiment, we come
out an interesting observation that though the non-orthogonal
channel interference affects the transmission, such interference
is tolerable. Thus, we use non-orthogonal channels in our
scheme with CFD smaller than the default setting of ZigBee
to provide more available channels.
Although non-orthogonal channel interferences have been
addressed in wireless networks [15] [16], related empirical
works in WSNs are rare. Incel et al. have conducted a measure-
ment study for adjacent channel interference in WSNs using
Ambient uNode [11]. In our work, more popular MicaZ motes
[8] are used. Besides, they disabled the MAC layer behaviors
of sensor node to introduce collisions; but we have studied
a new dimension: setting different CCA-threshold instead of
disabling entire CSMA policy. By varying CCA-threshold, we
can control the power level of introduced interference and
demonstrate the different throughput performance. Recently,
Xing et al. studied the interference of adjacent channel in
WSNs [19], but they use the channel assignment according to
ZigBee [10], while we use even smaller frequency distance to
partition channels for maximizing the utilization on bandwidth.
There are also some works discussed the optimal setting
of CCA-threshold [3] [23]. However, since their works are
based on single channel design where there is no neighboring-
channel interference, they only consider co-channel inter-
ference. Our study is a multi-channel design that exploits
non-orthogonal channels, thus the uniqueness of our CCA-
threshold setting scheme (DCN) is the differentiation of the
interference from co-channel and neighboring-channels, with
an emphasize on dealing with the neighboring-channel inter-
ference. More, Bertocco et al. used White Gaussian noise gen-
erated by signal generator as interference source [3]. However,
in densely deployed WSNs applications, the main interference
is transmitting packets, i.e., valid IEEE 802.15.4 packets.
Obviously, signal generator cannot emulate a real interfering
network: first, White Gaussian noise is different from a valid
packet; and second, constant noise cannot simulate the traffics
of a transmitting network. In this work, we use actual testbed
to generate interference from multiple channels.
In this section, we focus on a simple example to demon-
strate the feasibility of non-orthogonal multi-channel design.
Through the experiment, we find that comparing to the
CFD which guarantees the orthogonality, smaller and non-
orthogonal CFD may have better utilization on the given
bandwidth but we also point out that there is a trade-off.
To further explain the advantages of non-orthogonal channel
design, we study the concurrency between non-orthogonal
channels. We observe that the concurrent transmission between
Page 2
9 5 4 3 2
Channel Frequency Distance (MHz)
Throughput (packets/s)
Fig. 1. Bandwidth Throughput with
Different Frequency Distance
1 2 3 4 5 6 7 8 9 10 11
1 2 3 4 5 6 7 8 9 10 11
Channel Number
Normalized Throughput (1)
Fig. 2. Uniqueness of 802.15.4 Net-
Fig. 3. Collision of Attacker and
5 4 3 2 1
Channel Frequency Distance (MHz)
Collided Packet Receive Rate (100%)
Normal Sender
Fig. 4. CPRR with Different Fre-
quency Distance
non-orthogonal channels is feasible which implies the potential
bandwidth improvement.
A. Channel Distance vs. Overall Throughput
We first evaluate different CFD=9, 5, 4, 3, 2MHz with
ZigBee default protocol on a given bandwidth of 12MHz: for
CFD=9MHz which almost guarantees the fully orthogonality,
we only have 1 channel while for CFD=5MHz which is the
default setting of ZigBee, we have 2 channels. We also try
smaller CFD (e.g., 4, 3, 2MHz) which could assign more
number of channels but introduce severer inter-channel inter-
ference. For each assigned channel, we have 4 MicaZ nodes
with maximum transmission power (i.e., 0dBm). All the nodes
are sending packets at the maximum data rate, generating a
saturated traffic of the channel.
As shown in fig. 1, it demonstrates that the orthogonal
channel assignment will not provide the maximum throughput
(e.g., in Fig. 1 the maximum throughput is achieved at
CFD=3MHz ). The reason is that, though the throughput for a
single channel is maximized by orthogonal CFD setting (i.e.,
CFD=9MHz), but the overall throughput for orthogonal CFD
setting is poor due to the limited number of channels. However,
smaller CFD (e.g., CFD=2MHz) may not always benefit the
overall throughput since severer inter-channel interference may
corrupt more and more packets.
This example indicates that: 1) default setting of
CFD=5MHz in ZigBee is quite conservative, smaller CFD for
multi-channel design could obtain better bandwidth through-
put; 2) there is a tradeoff between throughput benefit from
more number of channels and harm from severer inter-channel
interference. To further get the understanding of how to
leverage the non-orthogonal channels to achieve the maximum
bandwidth throughput, we study the concurrent transmission
between non-orthogonal channels in the following subsection.
B. Concurrency between Non-Orthogonal Channels
In this subsection, we verify the feasibility of concurrent
transmissions in non-orthogonal channels. In 802.11 networks,
concurrent transmissions on non-orthogonal channels is in-
feasible. It is because inter-channel interference acts as valid
packets and force receiver to decode it (even the interference
is from three channels away, i.e., 15MHz away); during
the decoding, the receiver lose desired packet that is sent
simultaneously in the same channel (see experiments result
conducted by Mishra et al. [15] as shown in Fig. 2). As
the comparison, in our experiment on 802.15.4 (ZigBee) [10]
compliant devices (Crossbow MicaZ motes [8]), we found that
sensor device cannot decode packets from inter-channels, even
the packets are from only 1MHz frequency distance away.
Therefore, inter-channel concurrency is feasible for 802.15.4
To generate inter-channel concurrencies, we set two links on
non-orthogonal channels sending out packets simultaneously
by disabling their carrier sense module. Since it is hard to
synchronize the collision of two packets, we design the sender
of one link as an attacker which sends out packets at an
extremely fast rate, i.e., 1 packet for each 3ms. With such high
channel occupancy, all the packets from the normal sender on
the other link would be collided as shown in Fig. 3.
We focus on the node’s capability of decoding with interfer-
ence by computing the Collided Packet Receive Rate (CPRR)
for both the attacker and normal sender. By evaluate different
CFD with the same transmission power, the experiment results
shown in Fig. 4 address the feasibility of concurrency on adja-
cent channels: for CFD greater than 4MHz, both CPRR of the
attacker and the normal sender is 100%; CFD=3MHz provides
97% CPRR which indicates that most collided packets could
be decoded successfully; for the case of CFD=2MHz, CPRR
decreases to around 70% due to the severer inter-channel
interference; and for the extreme case of CFD=1MHz, it shows
poor concurrent feature that less than 20% collided packets
could be decoded successfully.
In this section, we observe that simply using smaller CFD
design instead of traditional ZigBee setting or orthogonal
assignment could achieve better bandwidth throughput. Above
experiment results also show the feasibility of concurrent
transmission in non-orthogonal channel which implies the
potential bandwidth improvement that we can obtain.
In previous section, we have shown that the concurrency is
feasible for non-orthogonal channel design (e.g., CFD=3MHz)
by disabling the carrier sensing module of senders to introduce
the inter-channel collisions. However, there is another respon-
sibility for carrier sensing module, to filter the co-channel
interference. Thus, simply disabling the carrier sensing module
is not a proper way to achieve better throughput. On the other
hand, the CCA-threshold is fixed at -77dBm in the default
setting of ZigBee. It assumes that the interference/noise over
-77dBm would corrupt the packets, and thus makes the sender
backoff when it hears the inter-channel interference but actu-
Page 3
Fig. 5. Network Configuration without Co-
channel Interference
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Throughput (packets/s)
Sent Packets
Received Packets
Fig. 6. Link Throughput with Different CCA-
Threshold (Case without Co-Channel interference)
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Throughput (packets/s)
Overall Throughput
Fig. 7. Overall Throughput with Different CCA-
Threshold (Case without Co-Channel interference)
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Throughput (packets/s)
Sent Packets
Received Packets
Fig. 8. Link Throughput with Different CCA-
Threshold (Case with Co-Channel interference)
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Throughput (packets/s)
Fig. 9. Link Throughput with Different Transmis-
sion Power (Case with Co-Channel interference)
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Packet Receive Rate (100%)
Fig. 10. Link PRR with Different Transmission
Power (Case with Co-Channel interference)
ally miss the chance that the sender can still do transmission
since such inter-channel interference is tolerable as observed in
previous section. Therefore, the conservative setting of CCA-
threshold prohibits the concurrency opportunities and draws
the throughput gap between orthogonal and non-orthogonal
channel design as shown in Fig. 1. In this section, we modify
the carrier sensing module, analyze and find a principle to
select proper CCA-threshold for the throughput improvement.
We will focus on the case of CFD=3MHz in the following
experiments since it shows that CFD=3MHz achieves best
performance with default carrier sensing module in Fig. 1.
A. Throughput Improvement without Co-Channel Interference
We start with a simple case without co-channel interference.
For a single link transmitting with 4 neighboring-channel
interference (i.e., CFD=±3, ±6MHz, see Fig. 5) using the
same power (0dBm), we enumerate different CCA-threshold
for this particular link. In Fig. 6, with more relaxed CCA-
threshold, such link sends out more packets since it turns
to consider more interfered channel condition as a clear
channel status. As such neighboring-channel interference is
tolerable, more packets that the link sent out lead to better
throughput. Meanwhile, we also notice that the packet receive
rate (PRR) keeps almost 100%, which indicates the reliability
of neighboring-channel concurrency and matching the result
of our previous concurrency test (Fig. 4) as well.
Furthermore, we check the overall throughput to see
whether such link throughput gain degrades other networks’
throughput. As demonstrated in Fig. 7, the overall throughput
also grows which indicates that the inter-channel concurrency
has been leveraged successfully. Therefore, without co-channel
interference, we could obtain better throughput gain by relax-
ing CCA-threshold. The next step is to answer what CCA-
threshold to select by taking the co-channel interference into
the consideration.
B. The Case with Co-channel Interference
From the viewpoint of every sensor node, it not only has
neighboring-channel interference but also encounters the co-
channel competitors. Thus, the CCA-threshold should also be
responsible for dealing with the co-channel interference.
Based on previous experiment, we introduce 3 additional
links working together with that particular link at the same
channel. The result in Fig. 8 shows that relaxing CCA-
threshold will not always benefit the throughput.
To further explain it, in the experiment case, the co-channel
interference is stronger than the neighboring-channel interfer-
ence (the vertical line shows the minimum power level of co-
channel interference) and once we relax CCA-threshold more,
the co-channel interference would be introduced which leads
to a disaster. For two collided packets in the same channel,
current common used modulation component of sensor node
could only decode at most one of them; or in the worst case,
both packets are corrupted. The throughput will not benefit
from the co-channel interference but be harmed.
As a short summary, through the experiments above we
observe that relaxing CCA-threshold could exploit more inter-
channel concurrent opportunities to improve the throughput
where the default CCA-threshold setting is quite conservative
for leveraging inter-channel concurrency. On the other side, the
co-channel concurrency should be avoided since decoding co-
channel interfered packets would robber the time for normal
Page 4
Fig. 11. Architecture of DCN
transmission. This observation would be a basis to propose
our dynamic CCA-threshold scheme in next section.
C. Effect of Transmission Power
In all the experiments above, we discuss feasibility of
concurrency and relaxing CCA-threshold with the setting of
the same transmission power. To further verify whether our
observation holds for different power setting, we will evaluate
the effect of transmission power in this subsection.
Fig. 9 illustrates that with different transmission power,
there would always be some throughput improvement by
relaxing CCA-threshold to introduce the inter-channel inter-
ference. Without surprise, the gain is different with different
transmission power due to the node’s capability of decoding
with interference. However, we found that for most of the
cases, i.e., transmission power is greater than -22dBm, the
PRRs are all 100% (in Fig. 10). Even for the case that link’s
transmission power is -22dBm vs. 0dBm of the interferers, the
PRR is higher than 80%. Such experiment results provide a
support for our observation so that we could design our scheme
for most general cases.
V. D
As a conclusion in previous section, in terms of bandwidth
throughput the CCA-threshold should be relaxed to intro-
duce concurrencies with neighboring-channel transmissions.
However, the CCA-threshold could not be relaxed in an
arbitrary way since there is also a responsibility for it to
prohibit the co-channel collisions. Thus, the CCA-threshold
must be able to introduce the inter-channel interference and
filter the co-channel interference at the same time. Towards this
end, we design and implement DCN, a scheme of Dynamic
CCA-Threshold for Non-orthogonal transmission. In DCN, we
modify sensor MAC protocol by adding an new component,
CCA-Adjustor. In the following subsections, we first show the
architecture of our DCN, followed by the design of CCA-
Adjustor and finally introduce the implementation in detail.
A. Architecture
In the traditional CSMA/CA policy (see Fig. 11), the upper
layer wishing to send packets has to first sense the power
of using channel to check whether there is any activity on the
channel by comparing the sensing power with a predetermined
−100 −80 −60 −40 −20
(1) Overlapped Interference
Updating Phase
Initializing Phase
−100 −80 −60 −40 −20
(2) Separated Interference
Probability Fraction (100%)
Updating Phase
Initializing Phase
Fig. 12. Setting of CCA-Threshold
CCA-threshold. If the sensing power is stronger than the CCA-
threshold, it regards the channel as currently occupied and
postpones the transmission request; otherwise, the channel is
available and the packet will be transmitted immediately.
Based on the CSMA/CA policy above, we add a new
component, CCA-Adjustor, in our design to adjust the CCA-
threshold dynamically, instead of using the fixed value. The
purpose of such CCA-Adjustor is to relax the CCA-threshold
according to different transmitting power of interference, for
leveraging neighboring-channel concurrencies while avoiding
the co-channel collisions.
B. Design of CCA-Adjustor
There are two types of interference information we could
obtain from the sensor node: 1) RSSI of co-channel interfer-
ence packets; 2) in-channel sensing power which means the
signal power of current using channel, including not only co-
channel packets but also inter-channel interference. To avoid
the co-channel collisions, a safe principle for CCA-threshold
setting is to be smaller than the power level of any co-
channel interference packets and with such constraint as high
as possible to introduce the concurrencies on non-orthogonal
Concretely, CCA-Adjustor modify the CCA-threshold in
two phases: Initializing Phase which focuses on getting a con-
servative initial CCA-threshold setting without any relaxing;
and Updating Phase which is devoted to update the CCA-
threshold according to most recent interference traffics.
1) Initializing Phase: In the initial stage when sensor
nodes have just started, aggressive CCA-threshold setting
may introduce unexpected co-channel interference. Thus, the
CCA-threshold should be determined cautiously to avoid any
potential co-channel interference in the initializing phase.
More specifically, assume we obtain the RSSI S
of co-
channel interference packet MSG
in the initial stage and
the in-channel sensing power sequence of {P
}. The CCA-
threshold CCA
should satisfy:
i, CCA
and be even lower as shown in Fig. 12 (2) to avoid the
potential co-channel interference occurring in the gap between
current co-channel and inter-channel interference records:
, ..., max {P
, ...}} (2)
Page 5
where the CCA-threshold is initialized as the smaller one
between minimum power level of co-channel interference and
maximum power level of the inter-channel interference.
Generally, sensor nodes would not only consider S
RSSI of co-channel packets, but also P
the in-channel sensing
power in the initializing phase. According to these informa-
tion, the nodes make a conservative CCA-threshold setting.
2) Updating Phase: Note that, the in-channel sensing
power P
would result extra CPU overhead on sensor nodes.
Therefore, it is not cost effective to do in-channel power
sensing after initialization. In the following updating phase,
CCA-Adjustor would only record the RSSI S
of recent co-
channel interference packet MSG
and dynamically update
to avoid co-channel collisions.
Concretely, the CCA-threshold would be updated in the
following two cases:
CASE I: if the RSSI of a received packet is smaller than
current CCA
, the CCA-threshold would be updated as such
RSSI immediately:
= S
if S
CASE II: if no update has been done by Case I in last
seconds (e.g., T
=3seconds in our experiments), the
CCA-threshold would be updated as the minimum RSSI of
co-channel interference packets recorded in last T
, ...} (4)
In the updating phase, different from in-channel power
sensing, obtaining RSSI information would not introduce extra
overhead since the co-channel interference packet would be
buffered automatically in current design of sensor nodes (i.e.,
MicaZ node in our experiments).
C. Implementation
We implement our DCN on the testbed consists of 35
MicaZ motes [8], which are equipped with Chipcon CC2420
Transceiver module [4]. The main information that our CCA-
Adjustor would leverage, RSSI value of packets, can be
obtained from RSSI field of each received packet [5]. Besides
recording the RSSI of packets, in-channel power sensing could
be performed by accessing the RSSI register RSSI.RSSI
of CC2420 transceiver [5], which is an average of previous 8
symbol periods (128µs).
In our implementation, after the start-up of sensor node, it
first enters the Initializing Phase which lasts for T
and checks the RSSI S
of co-channel packets during that
period of time and keeps the minimum record. Meanwhile, it
senses the in-channel power P
every millisecond and holds
the maximum one. The initial CCA-threshold would be set
as Equ. 2. After moving into the Updating Phase, the node
dynamically maintain the minimum RSSI value of received co-
channel packet in last T
=3seconds. The CCA-threshold
would be updated immediately once the record is smaller than
current CCA-threshold setting. If no update happens in last T
seconds, the CCA-threshold would be modified as the current
record of minimum RSSI value in last T
In this section, we evaluate the performance of our DCN
when compared to the original ZigBee design. We divide our
evaluation in two main subsections. First, we apply our DCN
on five networks (each network consists of 4 MicaZ nodes)
with different channel frequencies and evaluate the impact of
CFD on the performance of DCN. This evaluation answers
what CFD should be selected in our implementation of DCN.
Next we evaluate our DCN for the entire non-orthogonal
multi-channel design. Given a spectrum bandwidth, e.g., from
2458MHz to 2473MHz, our DCN separates nodes into 6
networks with CFD=3MHz. We give a detailed account of
throughput compared with ZigBee standard (i.e., 4 networks
with CFD=5MHz). Finally, we conclude our evaluation by
verifying DCN with different network configuration.
Our data analysis mainly focuses on network throughput and
discusses the impact of transmission power and the fairness
issue. We also conduct extensive experiments with various
network configuration such as topology, power variation, etc.
A. Effect of CFD on DCN
In order to find the CFD influences of our DCN on the
network throughput, as illustrated in Fig. 13, we evaluate two
typical CFD=2MHz and 3MHz on 5 networks. The primary
reason to compare CFD=2MHz and 3MHz is that, the exper-
iment results in Fig. 1 and Fig. 4 realize the observation that
CFD=3MHz achieves best throughput performance without
introducing DCN and CFD=2MHz with a little bit lower
throughput still has potential since its CPRR has about 30%
room to improve.
We grasp this observation and first apply our DCN only on
network N
(i.e., the network with median frequency) to check
how it beats traditional CCA-threshold fixed scheme. It can be
seen in Fig. 14 that, our DCN achieves about 27% throughput
improvement on network N
for both CFD=2MHz and 3MHz.
However, from the viewpoint of other networks (i.e., Network
), the throughput is degraded by around 5%
in Fig. 15. The results explain the influence of inter-channel
interference of non-orthogonal design. More over, as shown in
Fig. 14, for CFD=3MHZ its throughput reaches around 250
packets/s which is almost the same as the orthogonal channel
assignment (e.g., CFD=9MHz in Fig. 1). It implies that for
CFD=3MHz, after applying DCN, network N
achieves near-
upper bound of throughput by beating other networks which
are not applied DCN scheme.
To further evaluate the interaction of DCN scheme on
multiple networks, we apply our DCN to all 5 networks.We
see in Fig. 16 (CFD=2MHz) and Fig. 17 (CFD=3MHz) that
the throughput of every network improves. It indicates a good
collaboration of our DCN: all the networks can tolerate the
inter-channel interference and leverage concurrency as well.
Note that the throughput improvement varies between different
networks. For example, in Fig. 17, network N
has 4.6%
throughput improvement which is lower compared with 16.5%
improvement of network N
. The reason is that, network
Page 6
Fig. 13. Configuration of 5 Networks
2MHz 3MHz
Channel Frequency Distance (MHz)
Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 14. Throughput of Network N
with Differ-
ent CFD (Apply DCN Only on Network N
2MHz 3MHz
Channel Frequency Distance (MHz)
Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 15. Throughput of Networks Except N
Different CFD (Apply DCN Only on Network N
N0 N1 N2 N3 N4
Different Networks
Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 16. Throughput of Each Networks
(CFD=2MHz, Apply DCN on All Networks)
N0 N1 N2 N3 N4
Different Networks
Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 17. Throughput of Each Networks
(CFD=3MHz, Apply DCN on All Networks)
2MHz 3MHz
Channel Frequency Distance (MHz)
Overall Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 18. Overall Throughput with Different CFD
(Apply DCN on All Networks)
works on the boundary frequency which faces less inter-
channel interference as shown in Fig. 13 and thus has less
concurrency opportunities to leverage.
Back to the target of this subsection, to see the CFD
influence of our DCN on the overall throughput, we calculate
the overall throughput in Fig. 18. Clearly, for CFD=3MHz,
DCN improves the overall throughput by 10% and reaches
about 1300 packets/s which is 1.37 times compared with
the setting of CFD=2MHz. The experiment results show that
CFD=3MHz provides better overall throughput. Therefore,
we select CFD=3MHz for our non-orthogonal multi-channel
design in the following experiments.
B. DCN on Non-orthogonal Multi-channel Design
In this subsection, we thoroughly evaluate the performance
of DCN on non-orthogonal multi-channel design in terms of
throughput, transmission power effect, fairness concern, and
network topology. Our major performance benchmark is to
improve the overall throughput for a given spectrum bandwidth
by better utilizing the wireless medium. Given a spectrum
bandwidth of 15MHz (i.e., from 2458MHz to 2473MHz), we
first compare the performance of DCN with the default design
of ZigBee. Next we verify the general applicability of our
DCN with different transmission power setting. The fairness
issue is also evaluated and we finally testify our DCN with
more general cases of various network configuration.
1) Comparison with Default ZigBee Design: In our non-
orthogonal multi-channel design, we select CFD=3MHz and
apply DCN on 5 networks for the given spectrum bandwidth
of 15MHZ. The corresponding ZigBee design only assign 4
channels with CFD=5MHz and fixed CCA-threshold scheme.
In Fig. 19, we see that each individual network’s throughput
of DCN is better than the ZigBee channel (about 5.4%
improvement for an individual network), because frequency
distance of 5MHz cannot guarantee the orthogonality, and thus
the network with fixed CCA-threshold would be affected by
the non-orthogonal transmission from neighboring channels.
On the contrary, our DCN design better exploits the inter-
channel concurrencies even with severer interference intro-
duced. Besides, we have more channels assigned in our DCN
design due to small CFD setting. Therefore, the result shows
that our DCN design has 58% overall throughput improvement
compared with default ZigBee design.
2) Impact of Transmission Power: In previous Section IV,
we’ve already shown the effect of transmission power on
concurrency. The results in Fig. 9 and Fig. 10 present the
feasibility of concurrency. Based on that, we evaluate the
effect of transmission power on our DCN design. We focus on
network N
, the one with central frequency of the spectrum
band and suffers almost the severest inter-channel interference,
and vary the transmission power of network N
from -33dBm
to 0dBm. Meanwhile, for the sensor nodes in other networks,
we keep the same transmission power of 0.6dBm.
Fig. 20 testifies the fact that with increasing transmission
power of N
, the corresponding throughput would improve.
To further analyze in detail, we notice that the throughput
improvement could be divided into two phases: 1) for trans-
mission power lower than 15dBm where the packet receive
rate is not 100%. Increasing transmission power could provide
better SINR so as to bring better PRR and improve the
throughput; 2) for transmission power greater than 15dBm
Page 7
ZigBee Design Our Design with DCN
Throughput (packets/s)
Fig. 19. Overall Throughput Comparison between
ZigBee and our Design with DCN
−33 −15 −6 −3 −0.6
Transmission Power (dBm)
Throughput (packets/s)
Throughput of N0
Fig. 20. Throughput of Network N
−33 −22 −15 −11 −6 −5 −3 −2 −0.6 0
Transmission Power (dBm)
Throughput (packets/s)
Throughput of Others
Fig. 21. Throughput of Networks Except N
where PRR almost reaches to 100%, the grown of transmission
power for N
would only bring larger CCA-threshold setting
of N
in our DCN design (see Equ. 4). Thus, such relaxed
CCA-threshold could introduce more concurrency opportuni-
ties and benefit the network throughput.
This observation illustrates that the performance of DCN
is quite related to the co-channel transmission power. For
higher signal strength of co-channel packets, DCN would
have more relaxed CCA-threshold and introduce more inter-
channel concurrencies for better throughput. Furthermore, Fig.
21 shows that high co-channel transmission power would not
result trouble for neighboring channels. The reason is that we
select a proper CFD=3MHz in our design which could tolerate
the inter-channel interference.
(packets/s) 259.3 260.8 261.9 272.5 272.9 273.4
3) Fairness Issue: In DCN scheme, the CCA-Adjustor
dynamically change the CCA-threshold according to recent
records of co-channel and inter-channel interference. In order
to show that DCN would not drive some networks against
others, we compare the throughput of each individual network.
In our DCN design of 6 networks for 15MHz spectrum
bandwidth, network N
which uses middle frequency suffers
greater inter-channel interference. It faces different interfer-
ence condition compared to networks N
and N
located at
the ends of spectrum band. However, as shown in Table I,
the throughput difference among these networks is actually
slight, with about 4% variation. It demonstrates that our DCN
provides good fairness among all the networks, even though
we are given a relatively narrow bandwidth, i.e., different
networks suffer different inter-channel interferences.
4) Network Configuration: In this part, we evaluate DCN
with various network configuration. We set three typical cases
of network topology. Note that to behave like practical WSNs,
we set different transmission power for different node within
[-22dBm, 0dBm] at random in all of the following cases.
Case I: all networks in one interfering region
Case I (see Fig. 22) reflects a common situation of dense
deployed wireless sensor network. In this case, all sensor
nodes interfere with each other at a strong interfering power
level, since they are deployed close to each other. As a result,
the performance of just using small CFD=3MHz without DCN
could not benefit the overall throughput a lot as shown in Fig.
25. On the other side, our DCN achieves high throughput gain
ratio, about 14.7% compare with CFD=3MHz design without
DCN scheme and 55.7% against the default ZigBee design.
Case II: all networks separated with each other
In Case II (see Fig. 23), we treat each network as a cluster
formed by their locations, i.e., sensor nodes of the same
network are located together. One example is that the sensor
nodes are deployed in the building and all nodes in each office
room organized as an individual network with their own chan-
nel. In such situation, the inter-channel interference becomes
the relatively small. Thus, the throughput performance without
DCN is better than Case I due to the weak interference . As
a consequence, the throughput improvement ratio with DCN
is about 10.4% which is smaller than 14.7% in Case I.
Case III: all networks with random topology
In the last case (see Fig. 24), all sensor nodes from 6
networks are randomly deployed in a large region. This is
to simulate sensor nodes with various functionalities working
together. In Fig. 27, we see the relaxing gain of performing
DCN degrades significantly, comparing to above two cases. We
explain the result like this: sensor nodes in the same network
might be deployed far away from each other in this case, so
the RSSI of overheard co-channel packets might be small.
As studied in Fig. 20, lower co-channel interference power
would constrain the CCA-threshold setting in DCN because
the CCA-threshold needs to filter the co-channel interference.
Thus, it also prohibit more concurrency opportunities from
neighboring channels at the same time. As shown in Fig. 27,
it only achieves 6.2% throughput gain by including DCN and
38.4% compared with default ZigBee design. The experiment
for Case III actually illustrates one weakness of our DCN
design: avoidance for weak co-channel interference sometimes
limits the CCA-threshold relaxing and gives up the chance of
concurrent transmission with neighboring channels.
As a summary, in this section we study the CFD selection
for our DCN and use CFD=3MHz in our non-orthogonal
multi-channel design. With extensive experiments, we analyze
the network throughput, impact of transmission power, and the
fairness issue in our DCN design. We also evaluate DCN with
three general network configurations. The experiment results
verify that our non-orthogonal multi-channel design with DCN
could achieve 38.4% 55.7% improvement on the overall
throughput for a given spectrum bandwidth.
Page 8
Fig. 22. Network Configuration I Fig. 23. Network Configuration II Fig. 24. Network Configuration III
ZigBee W/o DCN With DCN
Throughput (packets/s)
Fig. 25. Throughput Comparison on Case I
ZigBee W/o DCN With DCN
Throughput (packets/s)
Fig. 26. Throughput Comparison on Case II
ZigBee W/o DCN With DCN
Throughput (packets/s)
Fig. 27. Throughput Comparison on Case III
In previous section, we have evaluated our DCN in different
network settings. From the experiments, we have an interesting
observation that: in some cases where the concurrency on non-
orthogonal channels leads to packet loss, most of the packets
are actually received with a small portion of error bits. Thus,
some packet recovery schemes could be integrated with DCN
to correct the CRC-failed packets. In addition, due to the
limitations on experimental hardware, we only conduct the
experiment on the bandwidth of 12MHz. For larger bandwidth,
we study the theoretical bound of throughput improvement
of DCN. We also discuss the intelligent approach of CCA-
threshold rather than simply avoid the co-channel interference.
A. Packet Recovery
As a complement result from Fig. 9, Fig. 28 shows that
severed inter-channel interference with higher transmission
power may bring trouble to decoding the concurrent low
power transmitting packets. For the case of 0dBm interfering
transmission against -22dBm link transmission power, a clear
gap between number of packets sent out and received could
be observed in Fig. 28 which indicates about 20% of packet
loss rate.
By further checking the detail of such packet losses, we
find that most of the lost packets are received (e.g., with
preamble captured) but with some error bits and could not
pass CRC-checksum. The statistic result in Fig. 29 shows that
most of such CRC-failed packets only have a small portion
of error bits (e.g., 87% CRC-failed packets have only 10%
error bits). According to that, if some packet recovery scheme
could be introduced to correct those, we can achieve better
throughput as the line of “Recoverable” shown in Fig. 28 and
the corresponding PRR will approximate to 100% as well.
However, even the best packet recovery scheme in the state
of art, e.g., Partial Packet Recovery (PPR) [12] introduces
extra overhead. Since the packet recovery scheme, in this
work, is only necessary for some special cases (e.g., inter-
channel interference with much higher transmission power
than the concurrent working link), an online dynamic recovery
scheme which could identify the recover-demand for different
links might be one of our future directions.
B. Throughput Performance for More General Setup
In the general cases above, we verified 30% through-
put improvement by assigning channels with smaller CFD
(CFD=3MHz) without DCN; and 15% additional throughput
gain from our proposed DCN. From Fig. 25, one interesting
phenomena is that, DCN provides more throughput gain on
the channel exploiting the middle bandwidth (e.g., N
) than
the ones on the boundary (e.g., N
). To explain the reason, we
conduct extra experiments on wider bandwidth. Note that to
eliminate the effect of randomness in the general cases setting
above, we fix the transmission power at 0dBm.
Recall Fig. 17, when the given bandwidth is 12MHz, we
verified 10% improvement for introducing DCN compared
with CFD=3MHz design without DCN; and the throughput
of the channel located in the middle of the bandwidth shows
the greatest improvement. If we have wider bandwidth, e.g.,
18MHz which supports 7 channels, (see Fig. 30), the corre-
sponding improvement is 13%. The reason for more improve-
ment gain is, the network (channel) with more neighboring-
channel interference will improve throughput more (the mid-
dle one). Therefore, wider bandwidth provides severer inter-
channel interference, then more concurrent transmissions can
be leveraged and higher performance gain can be achieved.
Page 9
−120 −100 −80 −60 −40 −20
CCA−Threshold (−dBm)
Throughput (packets/s)
Fig. 28. Packet Recovery for Severe Inter-Channel
Interference (Interference Power = -22dBm)
0 0.2 0.4 0.6 0.8 1
(0.1, 0.87)
Cumulative Fraction (100%)
Proportion of Error Bits (100%)
Fig. 29. Portion of error bits of CRC-failed
N0 N1 N2 N3 N4 N5 N6
Different Networks
Throughput (packets/s)
W/o Scheme
With Scheme
Fig. 30. Throughput Gain with 6 interfering
C. CCA Adjustment
To preserve the simplicity of our DCN, in this work, we sim-
ply ignore all the inter-channel interference and prohibit all the
co-channel interference. However, there is still much work to
do: 1) non-orthogonal design anyhow introduces inter-channel
interference, which might corrupt transmission in some cases.
Therefore, ignoring all the neighboring-channel interference is
unsafe. Future scheme should filter the neighboring-channel
interference which is intolerable to provide better transmis-
sion reliability; 2) current CCA-threshold is bounded by the
minimum power level of co-channel interference. Such setting
cannot leverage all the possible concurrencies and constrains
the relaxing gain. If some approach could differentiate the
current interference (i.e., identify it as co-channel interference
or not), then our DCN could leverage the inter-channel con-
currency and avoid co-channel collision at the same time. This
would be another direction of our future works.
In this paper, we propose a scheme DCN (Dynamic CCA-
threshold for Non-orthogonal transmission) for non-orthogonal
multi-channel design in WSNs. We observe that most
inter-interference generated from non-orthogonal neighboring-
channels is tolerable which indicates potential concurrent
transmissions between non-orthogonal channels. To capture
such concurrency opportunities, our DCN adjust the CCA-
threshold based on the recent records of interference. The
comprehensive experiments on our testbeds verify a significant
throughput improvement of DCN comparing to the default
ZigBee design.
This research was supported in part by Supported
by Huawei-HKUST joint lab project, Hong Kong ITC
ITP/023/08LP, National Natural Science Foundation of China
Grant No. 60933012.
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