A survey on various time synchronization techniques in underwater sensor networks

1. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
116
A SURVEY ON VARIOUS TIME SYNCHRONIZATION
TECHNIQUES IN UNDERWATER SENSOR NETWORKS
Anil C.B.1
, Vani Sreekumar2
, Manjari Joshi3
1
Asst. Professor, Computer Science and Engg. Dept, SNGCE, Kadayiruppu, Kerala, India
2, 3
M.Tech Student, Computer Science and Engg. Dept, SNGCE, Kadayiruppu, Kerala, India
ABSTRACT
Time synchronization is an important task in Wireless sensor Networks (WSNs), because in most of the
applications in WSNs need accurate time in nodes. A WSN consists of sensor nodes, which have the capability sense the
data from its neighbouring area and process the data and forward it to other sensor nodes. Underwater wireless sensor
network (UWSN) is a type of WSN, which is deployed in underwater. Time synchronization protocols have been
introduced for WSNs, but none of them can be applied directly to UWSNs as most of these protocols do not consider the
delay during propagation, limited band width, high error rate and mobility of nodes. Therefore to overcome these
problems, new time synchronization methods have been introduced for UWSN. This paper examines the benefits,
applications, challenges of time synchronization UWSN and also gives comparative study recent time synchronization
methods in UWSN.
Keywords: CLUSS, DA-Sync, MC-Sync, Mobi-Sync, MU-Sync, Sensor Node, Time Synchronization, TSHL, UWSN.
I. INTRODUCTION
UWSNs have a number of applications [1] such as Seismic monitoring which is a promising application for
underwater sensor networks. UWSN are used for oil extraction from underwater fields and also for monitoring the waves
in oceans in order to detect the variations undergone in the underwater in order to detect earthquakes and tsunami.
Another application is Equipment monitoring and controlling. This can be long term monitoring or temporary
monitoring. In case of temporary monitoring, the initial operations have been monitored after the deployment of the
equipment. long-term seismic monitoring, including the need for wireless communication, automatic configuration into a
multi hop network, localization, time synchronization and energy efficient operation. Another applications includes
coordinating and sensing of chemical leaks or biological phenomena such as oil leakage, phytoplankton concentrations,
oceanographic data collection, disaster prevention, pollution monitoring, offshore exploration, and military surveillance.
Time synchronization is an important concern that we need to consider especially for sensor networks. There are
many time synchronization techniques that have been proposed for terrestrial Wireless sensor Network (WSN) [2] which
provides high accuracy and high degree of precision. But the time synchronization techniques [5] which are used in these
terrestrial WSN cannot be directly applied to Under Water Sensor Networks (UWSN).This is because UWSN exhibit
different features, when we compare UWSN with the terrestrial WSN [4].These features include node mobility, long
propagation delay, limited bandwidth, limited transmission rate, high bit error rate etc. In case of terrestrial WSN the
propagation delay of sensor nodes are considered to be negligible. But in case of UWSN it suffers from the low
propagation speed, of acoustic signals. UWSNs require long propagation delays due to the low transmission speed of
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 5, Issue 12, December (2014), pp. 116-122
© IAEME: www.iaeme.com/IJCET.asp
Journal Impact Factor (2014): 8.5328 (Calculated by GISI)
www.jifactor.com
IJCET
© I A E M E

2. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
117
sound in water. For mobile UWSNs, the delays in propagation between these sensor nodes are time-varying because of
sensor node mobility. Wireless transmissions need to be energy efficient as it demands more power. All these reasons in
UWSNs bring new challenges for time synchronization algorithms.
UWSNs are often deployed in an environment, with many unattended sensor nodes. These sensor nodes are
likely to undergo many critical security attacks [5], such as replay attack, message manipulation attack, and delay attack
[6] [7].Therefore, time synchronization must prevent the modification attempts made by these attackers especially in
applications such as military surveillance and oceanographic data collection. These attackers manipulate time stamp
information of the neighbouring nodes and provide the nodes with incorrect information. In replay attack, a valid data
transmission is repeated maliciously and the attacker can take part in distributing timing messages among its neighbor
nodes because of which the old timing messages will be sent to its neighbouring nodes as the new time stamp
information for disrupting the timing synchronization process. In message manipulation attack, an attacker can drop,
modify the exchanged timing messages to interrupt the process of time synchronization. Similarly in a delay attack, an
attacker delays the time messages in order to disrupt the time synchronization process and cause it to fail.
There are many time synchronization algorithms that have been proposed in UWSNs .These algorithms include
Mobi-Sync [8] [9], DA-Sync [10], MU-Sync [11], D-Sync [12], MC-sync [13], TSHL [14], CLUSS [5] etc. Each
algorithms have their own strength and short comings. Such as the TSHL is mainly used for static networks. Therefore it
does not consider the mobility of the sensor nodes .But in case of MU-Sync it considers the node mobility, but it is not
energy efficient. Whereas in Mobi-Sync, high energy efficient time synchronization algorithms has been designed for
mobile UWSNs [15] but Mobi-Sync is considered only for dense networks. So other methods such as DA-Sync [10]
methods have been introduced .The recently introduced technique is the cluster based secure synchronization approach
where the nodes have been grouped into clusters and the cluster head manages the time synchronization of these nodes.
A Similar process has been done in MU-Sync but it applies half of the round trip time in order to calculate the
propagation delay, which can contain many significant errors when sensor nodes move rapidly, whereas in cluster based
approach (CLUSS) [5] reduces synchronization errors as well as energy consumption compared to that of other methods
In this paper, we discuss a number of techniques along with their benefits and issues in underwater sensor networks. A
comparative study of these techniques used in UWSNs has been elaborated. A thorough understanding and
differentiation of each of these techniques have been tabulated in Table I and an easy overview is been provided
regarding the time synchronization in UWSN.
II. SYSTEM LAYOUT
The network architecture [1][19] mainly consist of 3types of nodes such as surface buoys, super nodes and
ordinary nodes .The surface buoys consist of the GPS from which the time reference and the localization information has
been obtained. They can also be called as satellite nodes in underwater as they provide information regarding the nodes.
The sensor nodes are also called as super nodes which maintain the synchronization with the surface buoys. Super nodes
process the information from the neighbouring nodes and forward the information to other nodes in the network. Super
nodes can communicate directly with the surface buoys and so they can estimate the speed of these nodes by obtaining its
real time location and time information. Whereas ordinary nodes are inexpensive, low complexity nodes which cannot
directly communicate with the surface buoys and can communicate only with its neighbouring ordinary nodes or super
nodes.
Fig. 1: Under water wireless sensor network architecture

3. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
118
A. Major Sources of Errors in Time Synchronization
Message exchange technique is the most common methods used by the mobile sensor nodes to exchange the
information about the local clock drift. The common sources of error [7] for clock synchronization while using the
message exchange technique (introduced by Koeptz and Schwab [16] [17]) are:
• Send time: The time used by the sensor nodes to construct the message and send that message to the MAC layer.
It is highly non deterministic. It depends upon the current load and the operating system.
• Access time: The delay faced by the nodes while waiting to access the channel before transmission begins. It is
least deterministic. It depends upon the current network traffic and the nature of the current MAC protocol[20]. In
order to eliminate this error, we can use physical layer time stamping.
• Propagation time: It is the time taken by the nodes to transmit the message from the sender to the receiver. It is
highly deterministic. It depends upon the distance between the sender and the receiver. In terrestrial sensor
networks, it is negligible because the major contribution of synchronization error is due to the high speed of radio
wave but when dealing with underwater wireless sensor networks, this becomes a major cause of synchronization
error.
• Receive time: The time taken by the synchronizing nodes to process the notification of an incoming message.
• Byte Alignment Time: It is due to the change in the byte alignment at receiver. It is deterministic.
Most of the existing time synchronization schemes focus on eliminating these sources of error. Since each
technique uses different assumptions, as a result each technique uses different approaches to eliminate the sources of
error.
III. STATIC NETWORK APPROACHES
1. TSHL
TSHL is the first time synchronization technique introduced for high latency networks. TSHL is a two phase
technique for time synchronization. The main idea in TSHL [14] is that it splits time synchronization process in the
mobile UWSN into two phases. In the first phase, the nodes estimate clock skew. In the second phase in order to
determine the offset, they swap skew compensated synchronization messages within the network. After the completion of
the 2 phases we get a model that maps the local, inaccurate clock to the reference time base. We can then compute a
global time for synchronization.
In phase one, without receiving any knowledge about the propagation delay we are estimating the clock skew,
which means the accuracy of the estimation is dependent on consistency, not the duration, of propagation delay. In this
method we assume that the propagation is constant over the message exchange. The second assumption we are made in
the TSHL is that clocks are short term stable, which means clock frequency and skew remains constant for a short period
of time (typically 5-10 min). The factors enabling the short term instability includes environmental factors such as
sudden variation in temperature, supply voltage or shock. This assumption allows us to use linear regression for
modeling the clock skew.
2. WATERSync
WATERSync [21] is a correlation-based time synchronization protocol specifically for shallow underwater
sensor networks. WATERSync integrates the time synchronization procedure with the tree-like network routing topology
in vertical direction (the surface station is the tree root), which consists of Gradual Depth Timing (GDT) phase and Level
1 (i.e., between the surface station and first depth nodes) Skew Compensation (LSC) phase. To make the time
synchronization dependable, WATERSync adopts a correlation based security model to detect outlier timestamp data and
identify malicious nodes. However, horizontal direction is neglected during the process of time synchronization, which
results in high offset errors.
IV. DYNAMIC NETWORK APPROACHES
1. MU-Sync
MU-Sync is a cluster-based synchronization algorithm for mobile underwater sensor networks. By estimating
both the clock skew and offset, MU-sync avoids the frequent re-synchronization. MU-sync uses the message exchange
for gathering the local time information, by using these information we can estimate the clock skew by performing the

4. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
119
linear regression twice over a set of local time information. The first linear regression helps the cluster head to calculate
the effect of long and varying propagation delay; the second regression obtains the estimated skew and offset.
The MU SYNC mainly consists of 2 phases, namely, the skew and offset acquisition phase, and the
synchronization phase. In the first phase, the clock skew and offset is estimated. The estimation of the clock skew and
offset are done by applying linear regression twice over a set of n reference beacons. Comparing MU-sync with other
existing technologies, it performs the linear regression twice. The first regression enables the cluster head to gather the
amount of propagation delay using message exchange technique from each reference packet (REF). After receiving the
REF, it adjusts the REF beacons’ timings with their respective propagation delays [18]. To estimate the skew and offset a
second linear regression is applied to this new set of points. In case of MU-Sync since it is cluster-based, it can be
directly applied to mobile multi-hop UWSN.
2. MC-Sync
MC-Sync is a time synchronization technique for mobile underwater sensor networks which uses two mobile
reference nodes for node mobility. It consists of a network with a number of sensor nodes in which a node has to be
synchronized(N).MC-sync approach has two reference nodes along with time and will be located at opposite sides of the
node N. Node N sends its synchronization information to both these reference nodes and due to the mobility of the node
N, changes will occur in the sending position information. By using this information, the clock skew and offset have
been calculated.
To implement Mc-Sync algorithm [13], we need to consider two requirements that is to assume that the two
reference nodes that are formed along the direction of ocean current and the node is placed in the connection link of the
two reference nodes. The first requirement has been fulfilled designing equipment which consists of a two reference
nodes, in which one node remains mobile and the other is a static reference node. The static reference node is connected
to the mobile reference node through a light cable. After forming it we assume that the mobile reference node is in a still
stage and the static reference node moves with the ocean current. The main factors affecting the movement of the static
reference node is ocean current. Which in turn causes the light cable connecting the two reference nodes to move in a
direction parallel to the direction of ocean current? And the second requirement is fulfilled by considering the two
reference nodes should be deployed at the opposite sides of the area. Reference nodes should move a little distance and
should be in the still state to perform the time synchronization. Until all the nodes have been synchronized the reference
nodes keep repeating this process.
The MC-sync is not energy efficient when compared to mc-sync as it does not allow the reference nodes to
broadcast the synchronization messages. Thus the packet number of Mc-Sync is smaller when compared to MU-Sync.
3. Mobi-Sync
Mobi-sync is used for mobile under water sensor networks. Mobi-Sync differs from other approach as it
considers spatial correlation among the moving patterns of the neighboring nodes which allow to accurately estimating
the long dynamic propagation delay. There is no specific movement for the underwater objects that is it moves in random
motion. So spatial correlation is required in such movement. This is used for an ordinary node to calculate its own
moving velocity. The time synchronization in Mobi-Sync [8] has mainly three phases that is delay estimation, linear
regression and calibration. The delay estimation is done in order to accurately estimate the propagation delay the
information regarding spatial correlation is acquired. This phase mainly consist of two phases that is message exchange
and delay calculation. During the message exchange an ordinary node starts the time synchronization by sending a
request message to its neighboring super nodes. These super nodes on receiving the request schedules a response
message back to these nodes. These response messages consist of the velocity of the super nodes along with its time
stamp. Fig. 2 illustrates message exchange among sensor nodes for the case where there are three super nodes available
to assist the ordinary node perform time synchronization. The second step during the first phase is delay calculation
where the ordinary node computes its own velocity by using the spatial correlation information contained in the velocity
vectors of the super nodes. The ordinary nodes continue to send the response message until they get enough points to
perform linear regression. During the second phase the first round of linear regression is performed with the propagation
delay and the time stamp obtained in the first phase and estimates the clock skew and the offset. Here weighted lest
square estimation procedure is used in order to reduce the assumptions made during the 1st
phase. In the last phase that is
the calibration phase to improve the accuracy during the synchronization the ordinary node updates the initial skew and
distance and then recalculates the propagation delay and performs the linear regression again. The second round of linear
regression provides the final clock skew and offset.
4. DA-Sync
DA-Sync that is Doppler assisted time synchronization scheme uses a pair wise cross layer time synchronization
approach for under water sensor networks. Compared to the other synchronization approaches DA-Sync works in
physical MAC cross layer. In DA-Sync the Doppler shift [6] [18] that is the relative changes in velocity has been
estimated and then the accuracy in synchronization is maintained by employing a kalman filter. In order to measure the

5. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
120
relative velocity of the sensor nodes they use Doppler scaling factor estimation algorithm which are further refined by a
kalman filter in order to improve accuracy. Advantages of this approach is that accuracy of the propagation delay
estimation is tremendously improved when compared to the other approaches as it incorporates the relative moving
velocities between these nodes. Whereas the previous approaches used half of the round trip time to estimate the
propagation delay. Also it uses the MAC layer time stamp information’s to reduce the non-deterministic errors in the
time synchronization.
5. CLUSS
The CLUster-based Secure Synchronization approach (CLUSS) [5] considers both security and accuracy in
synchronization. CLUSS ensures the security of synchronization against various attacks like Sybil attack, replay attack,
message manipulation attack, and delay attack. This system mainly consists of three types of nodes such as beacons,
cluster head and ordinary nodes as shown in Fig.3. Beacons are nodes that have unlimited energy resources and two
beacons communicate with each other through radio frequencies. Whereas beacons communicate to the ordinary nodes
and cluster heads using links. These nodes are classified or divided into different clusters and each of these clusters has
one cluster head. All the ordinary nodes are connected to these cluster heads through a hop. The beacons are present in
the water surface along with the GPS in order to obtain time information. CLUSS protocols mainly consist of three
phases which include the authentication phase, inter-cluster synchronization phase as well as the intra-cluster phase. In
the authentication phase all the sensor nodes are authenticated to each other and they identify and remove the malicious
nodes from the networks. Then in the inter cluster phase the cluster nodes synchronize with the beacons in the sender to
receiver mode and the intra-cluster phase the ordinary nodes synchronize with the cluster head in the receiver to receiver
mode. In order to reduce the message overhead a part of the inter-cluster synchronization phase and the intra-cluster
synchronization phase can be executed concurrently. The advantage of the cluster based secure synchronization protocol
is that it improves the time synchronization accuracy by detecting abnormal end to end delay and also identifies the
malicious nodes in the network. It also finds the difference in the propagation delay of the downlink from that of uplink
caused during the node movement. CLUSS can reduce the errors during time synchronization as well as the energy
Consumption when compared to other protocols.
Fig.2: Message exchange

6. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
121
Fig.3: Cluster based Approach
V. COMPARATIVE STUDY OF VARIOUS APPROACHES
Table I. Comparison of various approaches
TECHNIQUE ADVANTAGE DISADVANTAGE
TSHL • Accurate
• Less energy cost
• Works for static WSN only
• Accuracy decreases if the nodes are at a long
distance
MU-Sync • Avoids the frequent re-synchronization by estimating both
the clock skew and offset
• Produces a more accurate time synchronization
• It reduce the nondeterministic errors by incorporating
MAC-level time stamping
• Every node knows skew and offset of all other nodes in the
same cluster.
• Higher communication overhead
• Accuracy depends on the propagation delay
changes within the round trip message exchange.
MC-Sync • Eliminate the problems of node mobility and improve the
synchronization.
• Not energy efficient
• It uses a large number of two-way messages
exchange.
Mobi-Sync • The calibration procedure produce more accurate clock
• Calibration procedure of Mobi-Sync is more efficient than
MU-Sync.
• Mobi-Sync achieves higher accuracy, when compared to
other methods such as TSHL.
• More energy efficient than MU-Sync
• Higher accuracy than MU-sync.
• Message overhead affects the accuracy of Mobi-
Sync that is more messages exchanges provides
more sample data in which we need to conduct
linear regression to each of these samples
• The influences of MAC layer activities have not
been considered
DA-Sync • Reduces the non-deterministic errors
• It incorporates the relative moving velocities between these
nodes
CLUSS • It detects the malicious nodes
• It detects the abnormal end to end delay
• It improves the time synchronization accuracy
• It also finds the propagation delay in downlink when
compared to that of uplink caused during the node
movement
• The communication path between the nodes are
authenticated using cryptographic methods and therefore
provide more security
• More complex
• More computational cost

7. Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
122
VI. CONCLUSION
This paper presents the study of various Time Synchronization Techniques used in under water sensor networks.
The approaches discussed in this paper are TSHL, MC-Sync, MU-Sync, Mobi-Sync, DA-Sync and CLUSS. A
comparative study of all these methods along with their benefits and short comings have also been discussed in order to
provide a clear overview of these techniques.
VII. REFERENCES
[1] John Heidemann, Wei Ye, Jack Wills, Affan Syed, Yuan Li, “Research Challenges and Applications for
Underwater Sensor Networking,” IEEE Communications Society (2006), 228-235.
[2] Li Wang, Zhi Bin Wang, et al., “A survey of time synchronization of wireless sensor networks”, Conference on
Wireless, Mobile and Sensor Networks (CCWMSN07)2007.
[3] Zhengbao Li, Zhongwen Guo, Feng Hong, Lu Hon, “E2DTS: An energy efficiency distributed time
synchronization algorithm for underwater acoustic mobile sensor networks,” Ad Hoc Networks 11 (2013) 1372–
1380
[4] Lasassmeh, S.M., Conrad, J.M., “Time Synchronization in wireless sensor networks: a survey”,IEEE
SoutheastCon, 2010.
[5] Ming Xu, Guangzhong Liu, Daqi Zhu, Huafeng Wu, “A Cluster-Based Secure Synchronization Protocol for
Underwater Wireless Sensor Networks,” Hindawi Publishing Corporation International Journal of Distributed
Sensor Networks Volume 2014, Article ID 398610, April 2014
[6] Y. Liu, J. Li, and M. Guizani, “Lightweight secure global time synchronization for wireless sensor networks,” in
Proceedings ofthe IEEE Wireless Communications and Networking Conference:Mobile and Wireless Networks,
pp. 2312–2317, 2012.
[7] A. S. Uluagac, R. A. Beyah, and J. A. Copeland, “Secure sourcebased loose synchronization (SOBAS) for
wireless sensor networks,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 4, pp. 803–813,
2013.
[8] Jun Liu, Zhong Zhou, Zheng Peng, Jun-Hong Cui, Michael Zuba, “Mobi-Sync: Efficient Time Synchronization
for Mobile Underwater Sensor Networks”,IEEE Trans on parallel and Distributed Systems, Vol. 24, No. 2, Feb
2013.
[9] Jun Liu, Zhong Zhou, Zheng Peng, Jun-Hong Cui, “Mobi-Sync: Efficient Time Synchronization for Mobile
Underwater Sensor Networks,” IEEE Globecom 2010.
[10] Jun Liu, Zhaohui Wang, Michael Zuba, Zheng Peng, Jun-Hong Cui, Shengli Zhou, “DA-Sync: A Doppler-
Assisted Time-Synchronization Scheme for Mobile Underwater Sensor Networks,” IEEE Transactions on
Mobile Computing, Vol. 13, No. 3, March 2014.
[11] Nitthita Chirdchoo, Wee-Seng Soh, Kee Chaing Chua, “MU-Sync: A Time Synchronization Protocol for
Underwater Mobile Networks,” WuWNet’08, September 15, 2008, San Francisco, California, USA.
[12] Feng Lu, Diba Mirza, Curt Schurgers, “D-Sync: Doppler-Based Time Synchronization for Mobile Underwater
Sensor Networks,” WUWNet’10, Sept. 30 - Oct. 1, 2010, Woods Hole, Massachusetts, USA.
[13] Ying Guo, Yutao Liu, “Time Synchronization for Mobile Underwater Sensor Networks,” JOURNAL OF
NETWORKS, VOL. 8, NO. 1, JANUARY 2013.
[14] Affan A. Syed, John Heidemann, “Time Synchronization for High Latency Acoustic Networks,” IEEE
INFOCOM, April 2006, Barcelona, Spain.
[15] Weihong Zhai, Jun Zhang, Sindhu Hak Gupta, “Asynchronous Reservation MAC Protocol for Underwater
Acoustic Sensor Network,” International Conference on Signal Processing and Integrated Networks (SPIN),
2014.
[16] H. Kopetz and W. Schwabl, “Global time in distributed real-time system,” Technical Report 15/89, Technische
Universit at Wien, 1989.
[17] H. Kopetz and W. Schwabl, “Clock Synchronization in Distributed Real-Time Systems,” in IEEE Transactions
on Computers, C-36(8), Aug. 1987, p. 933–939.
[18] Jun Liu, Zhaohui Wang, Zheng Peng, Michael Zuba, Jun-Hong Cui, Shengli Zhou, “TSMU: A Time
Synchronization Scheme for Mobile Underwater Sensor Networks,” IEEE GLOBECOM 2011 - Ad-hoc and
Sensor Networking Symposium.
[19] Z. Zhou, J. H. Cui, and A. Bagtzoglou, “Scalable localization with mobility prediction for underwater sensor
networks”, In Proceedings of IEEE INFOCOM’08, Mini-Conference, Phoenix, Arizona, USA, 2008.
[20] N. Chirdchoo, W. S. Soh, K. C. Chua, “Aloha-based MAC Protocols with Collision Avoidance for Underwater
Acoustic Networks,” in Proc. IEEE INFOCOM 2007, May 2007.
[21] F. Hu, S. Wilson, and Y. Xiao, “Correlation-based security in time synchronization of sensor networks,” in
Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC ’08), pp. 2525–2530,
April 2008.