Wireless Sensor Network, 2011, 3, 39-53
doi:10.4236/wsn.2011.32005 Published Online February 2011 (http://www.SciRP.org/journal/wsn)
Copyright © 2011 SciRes. WSN
Network-Wide Time Synchronization in Multi-Channel
Wireless Sensor Networks
Jari Nieminen1, Lijun Qian2, Riku Jäntti1
1 Department of Communications and Networking, Aalto University, Espoo, Finland
2 Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, USA
E-mail: {jari.nieminen, riku.jantti}@aalto.fi, liqian@pvamu.edu
Received December 21, 2010; revised December 29, 2010; accepted February 9, 2011
Recent advances in wireless sensor technology have enabled simultaneous exploitation of multiple channels
in wireless sensor systems. In this paper, a novel time synchronization algorithm is proposed for multi-
channel Wireless Sensor Networks (WSNs) called Multi-Channel Time Synchronization (MCTS) protocol.
Time synchronization is critical for many WSN applications and enables efficient communications between
sensor nodes along with intelligent spectrum access. Contrary to many existing protocols that do not exploit
multi-channel communications, the protocol takes advantage of potential multiple channels and distributes
the synchronization of different nodes to distinct channels and thus, reduces the convergence time of syn-
chronization processes significantly.
Keywords: Time Synchronization, Multi-Channel Communications, Wireless Sensor Networks
1. Introduction
Evolution of high-tech and tiny Micro-Electro-Mecha-
nical Systems (MEMS) has provided the platform for
successful implementation of Wireless Sensor Networks
(WSNs). Wireless communications are free from the
costs and physical constraints of communication cables,
and the development of small, low-cost wireless sensor
nodes has enabled designing of various wireless sensor
network applications such as military, health care, habitat
monitoring and industrial applications [1]. These versa-
tile sensors can be used to measure movement, humidity,
pressure and temperature [2], just to name a few. Thus, it
is natural that WSNs have gained a lot of attention from
both, academic and industrial partners. However, the
design challenges of WSNs include reliability, robust-
ness, interference and scalability issues in addition to
resource constraints. Multi-channel communications can
be used to address these challenges and to provide high
performance and trustworthy delivery of packets [3].
Naturally, exploitation of multiple channels at the same
time requires more intelligence from sensor nodes and
novel WSN protocols than those of single-channel sys-
Currently, wireless sensor systems usually utilize un-
licensed frequency bands and hence, coexistence with
various wireless systems such as WLAN introduces sig-
nificant interference problems for low-power sensor
nodes. Because of the crowded spectrum, the perfor-
mance of WSNs is deteriorated and reliable communica-
tions cannot be guaranteed [4]. Hence, a new sensor
networking paradigm called Cognitive Radio Sensor
Networks (CRSNs) has been proposed [5]. Sensors
equipped with cognitive radios are aware of their envi-
ronment and internal state, and can make decisions about
their radio operating behavior based on that information
and predefined objectives. This enables more reliable
packet delivery and more efficient utilization of scarce
spectrum resources. In the near future, we will see the
deployment of this kind of smart wireless sensor systems
which enable dynamic spectrum access and opportunistic
channel usage. In the field of CRSNs, our previous work
has considered energy efficient adaptive modulation [6]
and energy efficient spectrum access [7]. This paper in-
vestigates time synchronization in such networks where
cognitive radio can be the enabling technology for the
exploration of multiple channels and the focus is on the
design of a time synchronization protocol that can be
used after available channels have been identified.
Time synchronization plays a crucial role in various
WSN applications. Precise time synchronization has
been identified as one of the most important design ob-
Copyright © 2011 SciRes. WSN
jectives of WSN communication protocols, such as for
industrial automation applications [8]. Industrial control
applications require accurate time synchronization in
order to achieve predictable data collection and enable
reliable event logging [9]. Another promising application
for WSNs is structural health monitoring which requires
simultaneous vibration measurements [10]. Moreover,
the impact of synchronization errors on damage detection
in structures was studied in [11], where the authors show
that even small timing misalignments will cause time
shifts in sensor data which lead to problems in shape
reconstruction. Time synchronization is mandatory for
other applications as well, such as detection and tracking
of various objects. Hence, the importance of time syn-
chronization in WSNs motivated us to propose a novel
protocol for such systems.
Furthermore, utilization of multiple frequency bands
simultaneously enhances system throughput since many
data transmissions can take place in parallel channels. On
the other hand, wireless sensors are often energy con-
strained and power consumption is an important design
issue. In order to save energy, devices can turn their
transceivers off if they do not need to transmit or receive.
Sleeping times of nodes can be maximized by minimiz-
ing transmission times when applying multi-channel
communications. The realization of this so called sleep
mode will require accurate time synchronization for co-
ordination since otherwise nodes would not be able to
sleep or wake up at the correct time. Moreover, many
existing multi-channel MAC schemes use time frame/
slot structures and therefore require precise time syn-
chronization for effective operations, e.g. [12] and [13].
Time synchronization also enables the use of Time Divi-
sion Multiple Access (TDMA) techniques that are gener-
ally considered to be more efficient than contention
based Media Access Control (MAC) layer techniques.
Radio communication networks can be synchronized
using in-band or out-of-band solutions. Currently Global
Positioning System (GPS) is the most common out-of-
band synchronization method and can provide precise
timing. However, GPS may suffer from availability
problems due to failure, blockage or jamming. In addi-
tion, GPS does not work in all situations such as indoors.
Furthermore, cost and power consumption of GPS re-
ceivers makes it an infeasible solution for energy con-
strained WSNs [14]. Nonetheless, the proposed Multi-
Channel Time Synchronization (MCTS) protocol can be
exploited as an augmentation method for GPS as well.
In this paper, a network-wide time synchronization
protocol is proposed especially for multi-channel WSNs
called MCTS. The fundamental idea behind the proposed
time synchronization algorithm is that multiple channels
can be used simultaneously in order to minimize con-
vergence time of the synchronization process. The main
contributions are: 1) Using the proposed MCTS, net-
work-wide synchronization is achieved in a fully distri-
buted manner; 2) The proposed protocol takes advantage
of the potential multiple frequency bands and distributes
the synchronization of different pairs of nodes to distinct
channels which reduces the synchronization time signif-
icantly; 3) MCTS also exploits multiple transceivers if
available and thus, the capacity of multi-transceiver de-
vices can be fully exploited using MCTS; 4) Detailed
theoretical analysis of synchronization error and conver-
gence time are provided and by simulations we show that
the proposed MCTS is robust and outperforms other
protocols such as TPSN in multi-channel WSNs in prac-
The paper is organized as follows. In Section 2 a brief
overview on synchronization in communication systems
is presented. Section 3 introduces our proposed MCTS.
Performance analysis is provided in Section 4 which
includes theoretical convergence time analysis and si-
mulation results. Root node selection in case of MCTS
will be discussed in Section 5. Section 6 contains the
concluding remarks.
2. Related Work
Synchronization in communication systems includes
physical layer synchronization and network time syn-
chronization. Physical layer synchronization is required
for successful transmissions between two radios. How-
ever, physical layer synchronization offers only phase
synchronization between two radios and therefore, does
not provide global time synchronization across entire
Network Time Protocol (NTP) [15] has been widely
used in the Internet to provide time synchronization in
accuracy of milliseconds. However, WSNs need more
accurate time synchronization for efficient operations
and to comply with time synchronization requirements of
various applications. Moreover, NTP is not designed for
rapidly deployable distributed wireless networks and
requires predefined hierarchy where low quality clocks
synchronize to higher quality clocks. This is usually not
the case in WSNs since nodes typically have similar
clocks and no predefined assumptions on hierarchy can
be made. Time synchronization in Mobile Ad Hoc Net-
works (MANET) was studied in [16]. The proposed al-
gorithm calculates the time difference between the
transmitter and the receiver so that time stamps from the
transmitter can be mapped to correspond to the receiver's
clock. Consequently, network-wide time synchronization
is not provided and only time stamp transformation is
carried out.
Copyright © 2011 SciRes. WSN
Time synchronization in wireless sensor networks has
been widely studied and several protocols have been
proposed such as Reference Broadcast Synchronization
(RBS) [17] and Timing-sync Protocol for Sensor Net-
works (TPSN) [18]. In RBS, all the receivers time stamp
a synchronization packet from the same transmitter indi-
vidually and then exchange receive time stamps with
neighbors. This scheme offers only relative time syn-
chronization among neighboring receivers, not time
synchronization with the transmitter. TPSN utilizes clas-
sical two-way synchronization between a master and a
slave node. In the beginning, synchronization hierarchy
is formed and after that masters and slaves exchange
messages periodically to achieve time synchronization.
Moreover, one-way time synchronization schemes for
WSNs include for example Flooding Time Synchroniza-
tion Protocol (FTSP) [19] and Time Diffusion Protocol
(TDP) [20] which both exploit one-way messaging be-
tween masters and slaves.
An interesting approach for large scale wireless sensor
networks was presented in [21]. The purpose is to adopt
a synchronization scheme used by biological agents, for
example fireflies, and the protocol synchronizes nodes
by periodically sending pulses. However, the approach
only offers phase synchronization, similarly to syn-
chronously flashing fireflies, but no time synchronization
since no time stamps are taken nor sent.
On the other hand, an algorithm for minimizing energy
consumption of nodes during the synchronization
process was presented in [22] called Pairwise Broadcast
Synchronization (PBS). The innovative idea behind this
approach is that multiple nodes can exploit timing in-
formation they overhear during the synchronization
process and hence, the amount of messages required for
synchronizing the network is minimized. Time stamps
are exchanged between two “super nodes” (one master
and one slave) and all other nodes that can hear both of
these messages will synchronize according to this mes-
sage exchange. It is shown that PBS performs much bet-
ter than RBS and TPSN in terms of energy consumption.
However, PBS introduces additional timing errors be-
cause of the additional synchronization path between
receivers. Hence, even though this scheme is important
in order to minimize the number of synchronization
messages, two-way synchronization between all master
and slaves nodes should be performed for highest accu-
racy. This work was extended in [23] to cover multiclus-
ter networks as well, however, the same problem with
accuracy remains.
Comparing to PBS whose goal is mainly on energy
saving, the proposed MCTS targets the cases where the
availability of multiple channels may be exploited to
reduce convergence time. Since the goals of the two are
completely different, they can be considered as ortho-
gonal approaches. Furthermore, they can be integrated to
achieve greater performance for large sensor networks by
taking advantages from both PBS and MCTS. For in-
stance, in large sensor networks that need multiple super
nodes and have multiple channels available for commu-
nications, MCTS may be used among the super nodes
while PBS can be used for the rest of the sensor nodes.
To the best of our knowledge, utilization of multiple
channels in the context of network-wide time synchroni-
zation in WSNs has not been studied previously, even
though multi-channel wireless systems in general are
continuously attracting more attention in the research
community. For instance, local time synchronization
using multiple channels was considered by So et al. in
[24]. In their work, the authors considered parallel
rendezvous-based multi-channel MAC approaches and
introduced a synchronization protocol to synchronize one
hop neighbor pairs in time. Network-wide time synchro-
nization is not provided and the method in [24] is infeas-
ible for many WSN applications.
We conclude that even though many time synchroni-
zation protocols have been designed for various WSN
applications to provide network-wide time synchroniza-
tion, none of those exploits multiple frequency channels
and thus, the full capacity and advantages of multi-
channel WSNs are not exploited. By using multiple
bands for synchronization, the convergence time of syn-
chronization processes can be minimized and hence, the
demand for a new time synchronization protocol de-
signed particularly for multi-channel WSNs clearly ex-
Our previous work concentrated on time synchroniza-
tion of cognitive radio networks [25]. In this paper we
extend that work significantly by considering important
theoretical issues, such as the convergence time bounds
for an individual node and for the networks. In addition,
we analyze the performance of the proposed protocol by
showing new simulation results with respect to different
critical operation parameters such as the number of
available channels, network density and transmission
range of nodes. Furthermore, we also consider the root
node selection problem and propose a suitable solution
for the problem.
3. Multi-Channel Time Synchronization
Multi-Channel Time Synchronization protocol (MCTS)
is a master-slave protocol where all slave nodes syn-
chronize to a pre-selected root node. In small wireless
sensor networks, the gateway (GW) node can act as a
root node and provide time reference for the entire net-
Copyright © 2011 SciRes. WSN
work. However, in moderate sized WSNs the root node
should be in the middle of the network to minimize con-
vergence time and synchronization errors. How to select
a root node in moderate sized networks will be discussed
in detail in Section 5. In general, all time synchronization
schemes need a functioning MAC protocol to operate
and so does MCTS. To be more specific, MCTS requires
a functioning multi-channel MAC that uses periodic
beaconing, such as [13]. The synchronization protocol
has three phases, Hierarchy Discovery (HD), Synchroni-
zation Negotiation (SN) and Synchronization Execution
(SE). First, HD phases are used to create a synchroniza-
tion hierarchy and keep the hierarchy up to date in order
to cope with topology changes and node mobility. SN
and SE phases always follow a HD phase.
An example of operations of MCTS in a multi-channel
network is illustrated in Figure 1. We assume that the
gateway node will set one channel as a Common Control
Channel (CCC). τ is the synchronization slot length. TBI
means the end of the Beacon Interval (BI) and TT stands
for the total time that it takes to finish the synchroniza-
tion process. HD phase is carried out during BI and SN
and SE phases are carried out during the Negotiation
Interval (NI). In the figure operations of MCTS are illu-
strated in discrete time in order to enable theoretical
analysis later on. However, in practice the operations do
not have to be bounded by this kind of discrete time
presentation, instead the nodes can work without divid-
ing time into synchronization slots.
In the beginning of HD phase, a selected root node
will broadcast a Hierarchy Beacon (HB) message during
the Beacon Interval (BI) that includes a root node's ID,
synchronization level 1 and a list of available channels in
addition to a send time stamp. All the nodes that receive
this beacon message will set their synchronization level
to 2 and broadcast a similar HB message with a new send
time stamp. At this point the nodes at level 2 will set the
root node as their master and synchronize to it in a coarse
manner by using the time stamps they received. This
process goes on until every node in the network has
found out its level in the hierarchy and broadcasted a HB
message. Since HD phase can be included into multi-
channel MAC protocols that utilize periodic beaconing,
overhead can be minimized and only the addition of
node’s synchronization level to a beacon is required.
After creating the synchronization hierarchy, MCTS
proceeds to NI. In NI phase we have four different mes-
sages, Synchronization Negotiation Message (SNM), two
Synchronization Execution Messages (SEMs) and Data
Negotiation Messages (DNMs). SNM and DNM mes-
sages are similar and should be defined by the MAC
layer protocol. NI starts with the root node announcing
on the CCC that it is ready to start the synchronization
process. After this, all its slaves will contact it and nego-
tiate a channel for synchronization. After agreeing on the
used synchronization channel, both the master and the
slave will tune to the synchronization channel and carry
out the actual time synchronization as illustrated in Fig-
ure 2. Each node has to wait until they have synchro-
nized themselves to an upper level node before synchro-
nizing others in order to prevent distribution of false
synchronization information in the network.
After a slave has been synchronized it will announce
on the CCC that it is ready to be a master for other nodes,
if necessary, and all its slaves will contact it and nego-
tiate synchronization channels. Later on in the case study
we will show more precisely how this works. The exact
operations depend on the number of transceivers per
node and available channels as well as network topology.
SE phase is initiated by a slave and it consists of two
messages, Synchronization Request (Sreq) and Synchro-
nization Response (Sres). The slave first transmits a Sreq
message to the master, including the slave and master
IDs, and a send time stamp (T1). Send time stamps are
taken at the MAC layer in order to mitigate send and
Figure 1. Demonstration of MCTS operations.
Figure 2. Synchronization Execution (SE).
Copyright © 2011 SciRes. WSN
access delays. At the receiver side, the master should
time stamp (T2) the incoming Sreq message as close to
the physical layer (PHY) as possible, even before open-
ing the packet, to diminish the impact of receive delay
variation to synchronization accuracy. After receiving
the packet, the master will create and transmit a Sres
message which contains master ID, slave ID, T1, T2 and
T3. Send time stamp (T3) is again taken at the MAC
The slave will time stamp (T4) the incoming Sres
message. It is important to take both send and receive
time stamps at the same place in both transceivers, re-
spectively, to mitigate delay variations. After collecting
all the time stamps, {T1, T2, T3, T4}, the slave node can
be synchronized to the master and the propagation delay
(d) and the clock offset (θ) can be calculated as follows
21 43
21 43
Since the response from the master comes instantly, it
is safe to assume that clock drift will be constant during
the synchronization procedure. By using linear regres-
sion for several time stamps it is possible to determine
deterministic upper and lower bounds for clock offset
and drift and so, synchronization accuracy can be im-
proved. Processing of time stamps increases robustness
of the synchronization process as well since false time
stamps can be neglected. For time stamp processing, the
algorithms presented in [26] or in [27] can be exploited.
Current wireless sensor nodes have only one tran-
sceiver which means that in order to avoid multi-channel
hidden node problem, the nodes have to sense the partic-
ular channel before transmitting and exchange Re-
quest-to-Send (RTS) and Clear-to-Send (CTS) messages
before synchronization execution. However, in the near
future wireless sensor nodes may have at least two tran-
sceivers and in that case one transceiver can be tuned on
to the CCC all the time and thus, RTS/CTS messages
will not be needed on the synchronization execution
channels. Added to this, MCTS is suitable for any system
that utilizes multiple channels and so the nodes may even
have 2 transceivers and a dedicated receiver tuned on to
the CCC or more. MCTS enables simultaneous synchro-
nization of numerous slaves if multiple transceivers are
The entire synchronization procedure is executed pe-
riodically so if a node moves or a new node wants to join,
they will find their place in the synchronization hierarchy
quickly. However, synchronization does not have to be
performed every beacon interval and therefore, we in-
troduce a design parameter M (a positive integer) that
determines how often synchronization is carried out.
This is an important enhancement particularly for WSNs
since wireless sensors usually have small batteries and
hence, energy consumption should be optimized. In Fig-
ure 3 the idea of Synchronization Interval (SI) is de-
picted. In the figure M is set to 4 so synchronization is
done every fourth beacon round. This way the synchro-
nization overhead can be reduced and the operation of
the protocol can be optimized with respect to the desired
synchronization accuracy.
Nodes will use the information recorded from the BI
to determine whether they may have slaves or not. If a
node did not hear any HBs after sending one, it can start
negotiation for a data transmission immediately after it
has been synchronized since it does not have any slaves.
However, if a node hears a HB from a lower level node
after sending one, it has to announce on the CCC that it
is ready to act as a master for other nodes after it has
been synchronized. Furthermore, masters have to wait
for one slot after synchronizing all their slaves before
starting the data negotiation to ensure that synchroniza-
tion negotiation is prioritized over data negotiation.
Because the wireless channel may have deep fade and
suffer severe packet losses, a node may not be synchro-
nized during the synchronization process while it is still
able to negotiate for data transmissions after the channel
recovers from deep fade. In this case, we suggest a back-
up synchronization execution, where two-way synchro-
nization messaging is carried out before data transmis-
It is worth noting that MCTS is locally executed at
each individual node, i.e., all the definitions including BI
and NI, are local to each node because of the fact that a
node only needs to consider the nodes within its trans-
mission range. For instance, times TBI and TT are not
fixed and may be different for different nodes. Hence,
each node acts individually and the proposed protocol is
fully distributed. Therefore, NI phase will start propa-
gating after the root node has sent out the HB and no-
ticed that all of its neighbors have sent out their HBs as
well. In other words, synchronization process moves on
as a “wave” from the root node, which means that mo-
mentarily synchronization only impacts the nodes that
are two hop away.
Figure 3. Synchronization Interval (SI).
Copyright © 2011 SciRes. WSN
3.1. Case Study
In order to clarify the detailed operation of MCTS, we
present a case study according to the example scenario
shown in Figure 4. To simplify the operations we as-
sume that there exists a global CCC. We discuss two
cases where each node has one transceiver in the first
case and two transceivers in the second case. The focus
will be on NI since it is the most important part of the
protocol. The scenario consists of 9 nodes and the root
node is denoted by 1. In the figure available channels for
each node are presented next to each node. It is assumed
that the required time for SN is larger than the time for
First in the beginning of the HD phase the root node
will send a HB in order to start the synchronization
process. Nodes {2, 3, 4} will set their level as 2 and the
root node will be their master. Now, all the nodes on
level 2 send a HB and nodes {5, 6, 7, 9} will find out
that they are on level 3 and send a HB as well. Finally,
node 8 will receive a HB from node 7 and the hierarchy
is completed. However, node 8 still has to broadcast a
HB since there may be additional nodes. After this, the
protocol proceeds to the NI phase.
At this point all the nodes have found out their levels
in the synchronization hierarchy as described before. The
root node starts the NI phase by announcing that it is
ready to proceed with synchronization. After receiving
this announcement, all the nodes on level 2 will negotiate
with the root node and schedule a channel to carry out
synchronization. Up to this point, the protocol operation
has been the same regardless of the number of transceiv-
ers per node. However, in the next step, MCTS will take
advantage of multiple transceivers if available. It is no-
ticed that all nodes should listen to the CCC during NI in
order to prevent overlapping allocations or alternatively
use RTS/CTS message exchange on the chosen synchro-
nization channel.
Let us first consider the one transceiver plus one re-
ceiver case presented in Figure 5. All nodes tune their
receivers on the CCC and listen to that all the time.
When a node negotiates transmissions it has to take into
account the number of transceivers it has. Naturally, in
case of one transceiver, a master can synchronize only
one slave at a time. In our example, node 2 will first
synchronize with the root node on channel 1. After this,
node 2 will announce in the third slot that it is ready to
synchronize its slave nodes and negotiate with node 5 on
which channel to use, and the root node will synchronize
another slave (node 3) on channel 2 simultaneously.
Similarly, in the fourth slot, nodes 1 and 4 will syn-
chronize on channel 3 and nodes 2 and 5 will synchron-
ize on channel 1. At the same time node 3 will negotiate
Figure 4. Network topology of the case study scenario.
Figure 5. Negotiation Interval (NI) (1 transceiver).
with nodes 6 and 7. So in the fifth slot, node 4 will an-
nounce that it is ready to serve as a master. Node 5 does
not need to announce on the CCC since it did not hear
any HBs after sending one. Same applies for nodes 6, 8
and 9 as well. In the sixth slot, node 3 will also syn-
chronize node 7 since it was unable to synchronize both,
nodes 6 and 7, in the fifth slot. Finally, the last node to
be synchronized is node 8. After the synchronization
process, negotiations for data transmissions begin. Again,
the ending of the synchronization for each node, TT,
could be different. In this case the length of the NI, TNI =
TTTBI, for node 5 is four slots and for nodes 1, 2 and 6
five slots.
Negotiation interval for two transceiver case is pre-
sented in Figure 6. Now a master can synchronize two
slaves simultaneously so the convergence time of the
synchronization process becomes smaller and it is possi-
ble to utilize multiple frequency bands even better. For
instance, the root node can synchronize nodes 2 and 3 at
the same time in the second slot, and in the fourth slot,
three pairs of nodes can synchronize simultaneously. In
this case, nodes 2 and 3 are spatially separated so they
can share the SN slot. Compared to the one transceiver
case, the entire NI has reduced from eight slots to six
Copyright © 2011 SciRes. WSN
Figure 6. Negotiation Interval (NI) (2 transceivers).
3.2. Practical Considerations
In practice, MCTS can be implemented on top of various
multi-channel MAC protocols. MCTS can be imple-
mented together with any multi-channel MAC protocol
that utilizes periodic beaconing and a common control
channel. For example, multi-channel MAC protocols
which are based on the dedicated control channel ap-
proach can be used. Dedicated control channel designs
reserve one channel for distributing control information,
see for example Dynamic Channel Assignment (DCA)
[28], while data transmissions take place on different
data channels simultaneously. MCTS can be easily im-
plemented together with different dedicated control
channel MAC protocols since the frame structure is es-
sentially the same in both. Moreover, since these MAC
protocols take care of the most complex operations, such
as channel selections and resource reservations, the im-
plementation of MCTS on top of dedicated control
channel based MAC protocols should be straightforward.
On the other hand, Multi-channel MAC (MMAC) [13] is
an example of a split phase based approach in which
time has been divided into two intervals. The scheme
uses separate fixed time intervals for contention and data
transmissions. Resource reservations are carried out on a
common control channel during the contention period.
MMAC provides periodic beaconing and enables colli-
sion-free transmission by using contention periods. Thus,
MMAC and other split phase approaches are suitable for
MCTS as well. We conclude that MCTS can be imple-
mented on top of different MAC protocols. More impor-
tantly, we want to emphasize that the proposed multi-
channel time synchronization algorithm can be used to-
gether with various MAC designs and it is not tied to any
specific multi-channel MAC protocol.
Exploitation of multi-channel communications natu-
rally introduces some additional complexity. However,
since the used multi-channel MAC handles resource res-
ervations and channel allocations, MCTS does not gen-
erate much complexity itself. Creation of the synchroni-
zation hierarchy is straightforward and only the addition
of hierarchy levels to beacons is required. Moreover,
during the actual synchronization phase two-way syn-
chronization is carried out. This does not require heavy
calculations and hence, requirements for processing
power are light and the protocol can be implemented on
existing wireless sensor nodes.
4. Performance Analysis
In this section, we analyze the performance of MCTS
with respect to the convergence time of the synchroniza-
tion process. We derive analytical results for the conver-
gence time of an entire network and convergence time
bounds for individual nodes. We also study the perfor-
mance of MCTS under interference using simulations.
We show that the convergence time of the synchroniza-
tion process using MCTS is much less than that with
serial two-way synchronization, such as TPSN [18], and
MCTS performs well under interference as well. In this
section we concentrate on the convergence time analysis
since it is the main advantage of MCTS.
In case of MCTS time synchronization errors are sim-
ilar to TPSN and thus, error analysis will be bypassed for
now. However, for completeness we derive the results
for synchronization errors and show in general that the
accuracy of the two-way synchronization in the proposed
MCTS is much better than that of a simple one-way
synchronization in Appendix A.
4.1. Convergence Time Bounds for an Individual
Since MCTS is locally executed in each node, conver-
gence time of MCTS may be different for different nodes.
In this subsection we derive theoretical upper and lower
bounds for the convergence time of MCTS in case of
individual nodes. We assume that the interference range
is twice the transmission range and therefore, for an in-
dividual node the convergence time of MCTS is deter-
mined by its two hop neighborhood. In this model we
also assume that the data rate of each of the channels is
the same regardless of the amount of channels, which
means that each additional channel uses additional (or-
thogonal) frequency band, respectively. The number of
additional masters in the two hop neighborhood is de-
noted by M. With additional masters we mean all other
masters in two hop neighborhood of a node except its
master and the node itself. For simplicity, all nodes have
the same channels available for synchronization execu-
In general, the higher the data rate the smaller the
Copyright © 2011 SciRes. WSN
length of synchronization slots and hence, the length of
the synchronization slot τ is inversely proportional to the
data rate. As a consequence, the convergence time will
be inversely proportional to the data rate as well. From
Figure 1 the convergence time of MCTS for an individ-
ual node is
TTT (3)
Clearly, if a node does not have any slaves, the mini-
mum convergence time will be achieved if its master can
immediately carry out synchronization. In this case, the
node only has to successfully negotiate and execute syn-
chronization which takes two synchronization slots in the
optimum case. Hence, the convergence time is lower
bounded as follows
Now, we denote the number of available channels by
N. If there exists some other master nodes in the two hop
neighborhood, the convergence time will depend on the
ratio of channels to additional masters (N/M). If (N/M) 1,
the lower bound can be achieved. However, if (N/M) < 1,
additional delay of NM
may be induced, where
is the ceiling function. Moreover, if this particular node
has one or more slaves, the convergence time will be
extended. In case of idle channel conditions and only one
slave, S = 1, the convergence time will increase by two
slots and is given by
 . (5)
However, if a node has more than one slave, the number
of transceivers X will have an impact as well. Hence, in
idle channel conditions the convergence time in general
form is
 
, (6)
given that the number of available channels is large
enough (N S). By taking into account the fact that
additional masters will have multiple transceivers as well,
we find out that the maximum number of occupied
channels is now occ
NMX. If Nocc < N, a node can
find at least one free channel for synchronization execu-
tion. Naturally, in the worst case a node cannot syn-
chronize any of its slaves and has to wait until there is a
channel available. Hence, we can summarize the upper
bounds of convergence times for a master node as fol-
ul occ
 
If the amount of free channels is smaller than the
number of transceivers, the convergence time of MCTS
will be upper bounded with respect to the amount of free
channels. Furthermore, if the amount of free channels is
larger than the number of transceivers the convergence
time of MCTS will be upper bounded with respect to the
number of transceivers. Finally, if all the channels are
occupied, no upper bound can be found.
4.2. Network Convergence Time
In this subsection we present a theoretical framework
that can be used to analyze the overall time duration of a
MCTS process in multi-channel networks. The overall
convergence time in multi-channel systems depends on
various design parameters. First of all, the number of
transceivers per node determines how many slave nodes
one master can synchronize simultaneously provided that
the number of available channels is large enough. This
leads to the second critical parameter, which is the num-
ber of available channels. Furthermore, network topolo-
gy has an impact as well since it determines the number
of levels in the synchronization hierarchy L. Another
critical parameter is the maximum number of slave nodes
that a master node may have S, which is closely related
to network density (nodes/area). We denote the number
of nodes by η and the transmission range by r. In the
following analysis it is assumed that the transmission
range is fixed for all nodes.
We approximate the number of hierarchy levels in a
square network, x2 m2, as follows. Naturally, the distance
from the root node, which is in the center of the network,
to the edge is 2yx. Hence, the expected number of
hierarchy levels is
EL rr
 . (8)
Furthermore, we set network density as 2
thus, each node has 2
neighbors on average. Clear-
ly, all neighbors of the root node are its slaves and the
nodes on the edges of the network do not have any slaves.
Since the nodes are randomly positioned in the network,
we estimate that in the vicinity of each node one half of
the neighbor nodes can be on a lower level at maximum.
Thus, the maximum amount of slave nodes is
1In theory all the channels can be occupied for infinity by other nodes.
However, in practice the channels will be freed eventually since other
masters cannot have an infinite number of slaves and thus, the process
converges in any case at some point. Determination of the upper bound
in this case would require more assumptions.
Copyright © 2011 SciRes. WSN
 
, (9)
where Ar is the area of transmission. Now, if the number
of available channels is large enough, i.e. N >> S, the
convergence time of a typical synchronization process of
MCTS can be approximated as follows
 . (10)
Figure 7 compares theoretical and simulated results. As
the figure demonstrates, theoretical results match well
with simulation results if we have moderate network
density, i.e. 1/(20 m × 20 m) δ 3/(20 m × 20 m),
since the area was set as 200m×200m in this simulation.
Even though in this theoretical analysis it is assumed that
the network topology is wide spread, i.e. slave nodes of
each master are not in the transmission range of each
others, the theoretical results match simulation results
perfectly in one transceivers case.In case of two tran-
sceivers the results do not match exactly but since the
difference is relatively small, theoretical results can be
used to give guidelines of the performance2.
4.3. Simulation Results for WSNs under
We performed simulations to determine convergence
times of wireless sensor networks in case of MCTS and
TPSN in practice. Since wireless sensors are often colo-
cated with Wireless Local Area Networks (WLANs),
coexistence of IEEE 802.15.4 based sensor networks and
IEEE 802.11 b/g/n systems has gained a lot of attention
recently [29,30]. This motivated us to test the perfor-
mance of MCTS under interference in a real world sce-
nario. In the simulations one WLAN transmitter was
randomly placed in the area of 200 m × 200 m with va-
rying number of wireless sensors. The maximum inter-
ference range of WLAN transmitters and the transmis-
sion range of WSN nodes was set as 200 and 50 meters,
respectively, and the total amount of channels was set as
16. The WLAN transmitter occupied one randomly se-
lected channel and thus, four channels were unavailable
for some WSN nodes at a time. One channel was as-
signed as CCC to ensure the operation of both time syn-
chronization protocols. Again, convergence times are
calculated in slots.
In the first simulation all 16 channels were available
for all WSN nodes, except the channels that are occupied
by the WLAN transmitter, and we studied the impact of
network size on the performance of MCTS and TPSN.
Convergence times as a function of network size are
shown in Figure 8. The convergence time of TPSN
grows linearly when the amount of nodes in the network
is incremented. MCTS performs similarly as a function
of network size, however, since neighboring master
nodes can use different channels for synchronization
execution the angular coefficient of MCTS curve is sig-
nificantly smaller. This leads to much smaller conver-
gence times when using MCTS, even in one transceiver
case. In general, the benefit gained using MCTS grows
as the size of the network increases. In this scenario,
MCTS with two transceivers uses less than 20% and
even with one transceiver less than 40% of the resources
compared to serial two-way synchronization when the
amount of wireless sensors is 200. Moreover, the per-
formance of MCTS is quite stable as the network size
grows whereas the convergence time of TPSN is heavily
affected by the number of nodes. However, other net-
work parameters have an impact on the performance of
MCTS as we will see next.
Figure 7. Convergence time as a function of network size.
Figure 8. Convergence time as a function of network size.
2However, since this is only a simple approximation, it may not give as
accurate results in all possible cases.
Copyright © 2011 SciRes. WSN
Secondly, we simulated the impact of transmission
range on the performance of MCTS. Convergence times
as a function of wireless sensors’ transmission range are
presented in Figure 9. Interference range of a WLAN
transmitter was fixed as 200 meters. The amount of
WSN nodes was set as 100. As we can see, the transmis-
sion range of wireless sensors has a bigger impact on the
performance of serial two-way synchronization since all
masters have to synchronize on the same channel and can
only synchronize one slave at a time. The results imply
that MCTS performs significantly better than TPSN in
case of small transmission ranges but the difference
shrinks while transmission range is increased. The reason
for this is that in case of large transmission ranges each
master node will have many slaves and thus, multi-
channel communications cannot be fully exploited be-
cause of the small number of transceivers. Hence, as the
transmission range grows the achieved gain from using
two transceivers increases.
Finally, we simulated the effect of available channels
on the performance of MCTS in general without consi-
dering any specific interference sources. Convergence
times as a function of available channels are presented in
Figure 10. Again, the amount of wireless sensors was set
as 100. Naturally, the number of available channels does
not have any impact on the convergence time of serial
two-way synchronization. When the amount of available
channels is low, the amount of transceivers has negligi-
ble impact on the performance of MCTS since the prob-
ability that a master and its slaves would share many
available channels is extremely low. However, when the
amount of available channels is increased, the perfor-
mance of MCTS quickly improves. In this scenario, the
performance of MCTS saturates when 40% of the chan-
nels are available. Consequently, only a small amount of
available channels is enough to ensure close to the op-
timal performance in case of MCTS.
As the simulation results show, MCTS performs much
better than serial two-way synchronization in most of the
cases. Only when the amount of available channels is
very low, MCTS and serial two-way synchronization
perform similarly. Furthermore, the benefit gained from
the use of MCTS depends on various network parameters.
In one transceiver case MCTS outperforms serial two-
way synchronization clearly and with two transceivers,
the difference in performance is even larger. MCTS is
scalable with respect to the number of nodes in the net-
work as well as to the transmission range. This is a very
promising and important property because it implies that
MCTS can be applied to large scale multi-hop multi-
channel wireless sensor networks. Simulation results
show that MCTS is robust and performs well under in-
Figure 9. Convergence time as a function of transmission
Figure 10. Convergence time as a function of available
channels (16 channels in total).
It is evident that if less time is spent to carry out the
synchronization process, more time for data transmis-
sions is available. If multiple synchronization messages
are required to estimate the clock drift, this time saving is
even more important since multiple messages are sent
and thus, the achievable gain by using MCTS increases.
Hence, we conclude that utilization of MCTS is very
important if multiple messages are required for precise
estimation of the drift.
5. Root Node Selection for MCTS
Root node selection is an essential topic since it is im-
portant to choose a node in the topological center of the
network as the root node in order to minimize the con-
vergence time and worst case synchronization error of
MCTS. We show that in order to minimize the conver-
Copyright © 2011 SciRes. WSN
gence time of MCTS, proper root node selection is re-
quired. Moreover, since synchronization error in multi-
hop communications generally grows as a function of
hops, the root node selection algorithm should be based
on location in case of MCTS. It is assumed that all nodes
in the network have similar clocks so there is no need to
compare clock attributes of different nodes. For now, we
consider only moderate sized networks where only one
root node should be selected.
Root node election problem considered in this work is
similar to the well-studied leader election problem in
mobile ad hoc networks. The multicast operation of the
Ad-hoc On-Demand Distance Vector (AODV) routing
protocol [31] performs leader election to elect a new
multicast group leader when a partition occurs. After the
multicast tree becomes disconnected due to a network
partition, there are two group leaders. If the components
reconnect, the multicast operation of the AODV protocol
ensures that only one of the group leaders eventually
becomes the leader of the reconnected tree. A random
leader election algorithm is proposed in [32]. Two dis-
tributed leader election algorithms, based on the routing
algorithm TORA, are designed for operation in ad hoc
networks. Both leader election algorithms guarantee that
every connected component in the network will even-
tually have a unique leader. The first algorithm works
when only a single topological change occurs. The
second algorithm handles multiple concurrent topologi-
cal changes.
In the above two approaches, the leader is randomly
chosen without considering any specific requirements on
the leader. On the contrary, extrema-finding leader elec-
tion algorithms for mobile ad hoc networks have been
proposed in [33], however, these algorithms are unrealis-
tic as they require nodes to meet and exchange informa-
tion in order to elect a leader. A more practical extre-
ma-finding leader election algorithm is proposed in [34]
based on self-stabilizing systems that is highly adaptive
to arbitrary (possibly concurrent) topological changes.
Since there will be frequent topological changes in
WSNs due to the disruptions resulted from interfering
users’ activities, wireless sensors running out of battery
and possibly sensors’ mobility, this type of algorithms
are highly desirable. Hence, we may adapt the algorithm
proposed in [34] to find a root node in MCTS by assign-
ing proper values to each node based on their topological
locations. The difficulty is that it is non-trivial to obtain
such topological information, and the mapping between
the topological locations and the values assigned to each
node needs to be designed.
In order to obtain proper values of each node based on
their topological locations, we review two possible solu-
tions based on the classical k-center problem and the
minimum Connected Dominant Set (CDS), respectively.
Then we highlight how to use these algorithms to fit our
needs. We denote the WSN topology by a graph G = (V,
E), where V is the set of nodes and E is the set of links.
The k-center problem identifies a subset S of V contain-
ing k nodes such that the distance from the rest of the
nodes (in V-S) to S is minimized [35,36]. It formulates
the scenario where a known number (k) of service facili-
ties are to be deployed in the network so that they are
“close to every client”. The problem itself is NP-complete
but can be approximated within a factor of 2 [37,38]. The
root node selection problem in MCTS can be formulated
as a 1-center problem [39]. However, most of the exist-
ing algorithms are centralized and many of them are only
for a tree topology.
Another possible solution is using the well-developed
distributed algorithms for computing minimum Con-
nected Dominant Set (CDS) repeatedly (trim one layer of
nodes in each round) to find the center node. The generic
minimum dominating set problem in graphs is to find a
minimum subset S of V such that any node in V-S has at
least one neighbor in S, i.e., dominated by S. S is a min-
imum CDS if it is also a connected subgraph. The prob-
lem of finding minimum CDS in a graph is NP-complete
and cannot be approximated better than log(n), where
n=|V| [40]. However, there are distributed approximation
algorithms that meet such an approximation ratio with
small overhead, such as [41] and [42]. For our problem
of finding a center node, we apply distributed algorithm
to compute a minimum CDS repeatedly until there is
only one node left in the minimum CDS. In other words,
we obtain a minimum CDS of V, S1, in the first round.
For all the nodes in V-S1, their values are assigned as b1.
Then a minimum CDS of S1, S2, is obtained in the second
round, and for all the nodes in S1-S2, their values are as-
signed as b2 = b1 + 1. We repeat this process until there
is only one center node left in the final minimum CDS.
Both the k-center problem and minimum CDS prob-
lem assume static network topology and do not consider
frequent topology changes that may happen very often in
WSNs. Hence, we propose the following scheme:
1) Firstly, each node needs to obtain the set of their
1-hop neighbors.
2) Then we compute minimum Connected Domi-nant
Set (CDS) repeatedly (trim one layer of nodes in each
iteration) to compute the center node as well as map the
topological locations to their respective values. CDSs
can be calculated in a distributed manner using the algo-
rithm presented in [42] for example.
3) After that, we apply the leader election algo-rithm
in [34] to find the root node under highly dynamic sce-
narios if needed.
If the network topology changes slower than the con-
Copyright © 2011 SciRes. WSN
vergence of the scheme, then only steps 1 and 2 are ne-
cessary on the condition that the topology discovery al-
gorithm in step 1 is able to discover network partitions
due to link failures or networks merging due to new link
formations. When the network is highly dynamic, i.e.,
there are frequent topology changes induced by primary
users’ activities or node mobility, steps 1 and 2 and 3
will be executed periodically to accommodate the dy-
namic nature of a WSN. The scheme will guarantee that
there will be a unique root node for each group of con-
nectednodes when the algorithm converges, even under
frequent network partitions or networks merging.
Figure 11 demonstrates the importance of root node
selection on the performance of MCTS. In the figure,
RAND stands for the case when the root node is random-
ly selected instead of using the proposed method for
MCTS. The simulation scenario was the same as in Sec-
tion 4 except the network density was fixed as 1/(20 m ×
20 m). This is typical in WSN deployment and we
wanted to study how MCTS scales with increased net-
work size. The results imply that in order to minimize the
convergence time of MCTS, proper root node selection is
essential especially as the number of nodes grows.
6. Conclusions
In general, time synchronization is essential for many
WSN applications and makes it possible for sensor nodes
to communicate in a smart and efficient manner using
sleep modes and TDMA. With cognitive radio as the
enabling technology, multiple available channels can be
identified by the sensor nodes in a cognitive radio sensor
network. In this context, we presented a novel protocol
for time synchronization of multi-channel wireless sen-
sor networks, named Multi-Channel Time Synchroniza-
tion (MCTS) protocol, and explained the operation of the
protocol with a detailed case study. The unique features
of the proposed MCTS include achieving network-wide
synchronization using a fully distributed protocol and
exploiting multiple channels to reduce convergence time.
MCTS exploits the benefits of using multiple transceiv-
ers as well, if available. We studied the convergence time
analytically and demonstrate the performance of MCTS
through simulations. We show that the simulation results
match our analytical results well. In addition, we observe
that MCTS works well even only a small number of
channels is available. Importance of root node selection
was also discussed and a suitable solution is provided.
We conclude that MCTS outperforms other existing so-
lutions such as TPSN in multi-channel wireless sensor
networks, and it is a promising candidate for time syn-
chronization in future multi-channel cognitive radio sensor
Figure 11. Impact of root node selection on the convergence
time of MCTS.
7. Acknowledgements
This research work is supported in part by TEKES (Fin-
nish Funding Agency for Technology and Innovation) as
part of the Wireless Sensor and Actuator Networks for
Measurement and Control (WiSA-II) program and by the
U.S. Army Research Office under Cooperative Agree-
ment W911NF-04-2-0054.
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Copyright © 2011 SciRes. WSN
Appendix A: Error Analysis
Many existing MAC layer protocols (such as [13]) use
beacons to carry time synchronization information. As a
result, only one-way synchronization is performed. In
this appendix we derive the error formulas for MCTS
and one-way synchronization and show that the accuracy
of the two-way synchronization in the proposed MCTS is
much better than that of a simple one-way synchroniza-
tion in general. In order to derive synchronization error
formulas for one-way and two-way multi-hop synchro-
nization, let us first consider a simple case where Node 1
synchronizes Node 2, i.e. 1-hop case where Node 1 is the
master and Node 2 is the slave. MAC layer time stamp-
ing is used in all of the following calculations. The slave
node initializes the synchronization execution. First, the
receive time T2 for the SReq message at the master
(Node 1) can be formulated as follows
21 1sp rct
  , (A-1)
where T1 is the send time stamp at Node 2, 2
T the
send delay at Node 2, 21
Tis the propagation delay
from Node 2 to Node 1 and 1
T the receive delay at
Node 1. 21
stands for the clock drift between nodes
at time t1. θ is the clock offset between the nodes. Simi-
larly, the receive time T4 for the SRes message at the
slave (Node 2) is
112 212
43 3sp rct
, (A-2)
where T3 is the send time stamp at Node 1, 1
T is the
send delay at Node 1, 12
T is the propagation delay
from Node 1 to Node 2 and 2
T is the receive delay at
Node 2. 12
stands for the clock drift between nodes
1 and 2 at time t3. Then, the relative clock drift between
nodes during the synchronization message exchange,
marked with δ, can be calculated as follows
21 21
, (A-3)
12 1221
34 4tt t
 
 
, (A-4)
where 12
is the clock drift between nodes 1 and 2 at
time t3, 12
is the clock drift between nodes 1 and 2
at time t4 and 21
stands for the clock drift between
nodes 2 and 1 at time t4. Next, by subtracting equation
(A-2) from (A-1) and by using (2), (A-3) and (A-4), the
equation for synchronization error in case of two-way
synchronization can be formulated as follows
 
12 1221 21
MCTSsspprc rc
Tp Ts Trc
  
where ΔTp is the difference in propagation delays and ΔTs
and ΔTrc are differences in send and receive times, cor-
respondingly. Note that by using MAC layer time
stamping, access delays are eliminated and most of the
send delays as well. For N-hop communications with
two-way synchronization the synchronization error is
NTp Ts Trc
 
, (A-6)
where δi corresponds to the error introduced by ith oscil-
lator on the path. Similarly we can derive the equation
for error in case of one-way synchronization. Now the
receive time T2 for the message is the same as in (A-1).
From that we can derive the synchronization error in
N-hop communications with one-way synchronization
the synchronization error is
totali i ii
beaconsp rc
. (A-7)
When using one-way synchronization, the error is
cumulative and includes all the delays. Thus, the error
grows fast as a function of hop count and distance. On
the contrary, when using two-way synchronization, the
synchronization error includes only the differences in
various delays, which are much smaller than the delays
In fact, the error with two-way synchronization will be
only slightly cumulative. According to practical mea-
surements with wireless sensors reported in [18], the
synchronization error is almost the same over multiple
hops on average. However, the worst case synchroniza-
tion error will grow as a function of hops. The measure-
ments performed in [26] show that the one-way delay
with wireless sensors is approximately 1.4 milliseconds,
which means that the synchronization error with one-way
synchronization will be the same. With two-way syn-
chronization, the average error is 17 microseconds and
worst case error is 44 microseconds [18]. Naturally, the
performance of one-way synchronization can be im-
proved by using FTSP [19] or TDP [20]. However, these
methods require multiple messages to achieve microse-
cond precisions and thus, convergence times are signifi-
cantly larger and mobility of nodes will cause problems.