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Review Article

Energy Efficient on Aspect of Clock Synchronization in a Wireless Sensor Network

Zeyad Ghaleb Al-Mekhlafi, Zurina Mohd Hanapi, Mohamed Othman, Zuriati Ahmad Zukarnain and Ahmed M. Shamsan Saleh
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Recent advances in the areas of Micro-electrical Mechanical Systems (MEMS) spurred the interest of researchers in Wireless Sensor Networks (WSNs). These networks are made up of sensor nodes which have the capability to sense, process and transmit gathered data of an environmental phenomenon of interest. Such synchronization is vital for the proper coordination of the power cycles for energy conservation. Here, a large presence of fireflies employ the principle of pulse coupled oscillators for the emission of light flashes for the attraction of mating partners. With respect to WSNs, the nodes are generally unable to afford packet transmission and reception simultaneously, thus preventing complete network synchronization. This study presents a literature overview concerning the energy efficient on aspect of clock synchronization in a wireless sensor network. In addition, the idea of data transmission based on synchronization can be ensured through the optimization of energy usage periodic data capturing in the wireless sensor network. This study serves as a useful source on clock synchronization to assist WSN researchers and novices to gain a better understanding of the energy efficient on aspect of clock synchronization in a wireless sensor network and to promote effective designs and systems that address this problem.

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Zeyad Ghaleb Al-Mekhlafi, Zurina Mohd Hanapi, Mohamed Othman, Zuriati Ahmad Zukarnain and Ahmed M. Shamsan Saleh, 2014. Energy Efficient on Aspect of Clock Synchronization in a Wireless Sensor Network. Journal of Applied Sciences, 14: 1101-1113.

DOI: 10.3923/jas.2014.1101.1113

Received: December 12, 2013; Accepted: March 03, 2014; Published: March 24, 2014


Recent advances in the areas of Micro-electrical Mechanical Systems (MEMS) spurred the interest of researchers in Wireless Sensor Networks (WSNs). These networks are made up of sensor nodes which have the capability to sense, process and transmit gathered data of an environmental phenomenon of interest. Some of the applications of WSNs are military surveillance systems, industrial monitoring, etc. The sensor nodes are often constrained by their limited energy, processing and storage capacity and hence, usually transmit the sensed data to a more resource-rich node called the sink node (base station). Moreover, as mentioned earlier, the WSN technology is characterized by limited processing capability and communication radius (Dutta et al., 2012) and because these limitations are critical to the overall lifetime of the WSN, it is prudent that they are considered in the routing protocol design (Saleh et al., 2012). Since, individual node failure has a direct repercussion on the whole network with time, regular sensing and packet relaying to the sink may be seriously jeopardized as more and more sensors cease to operate as they exhaust their limited energy (Senouci et al., 2012). Each routing protocol algorithm sends data from the sources to the targets and is expected to increase network exposure even as the propagation value decreases (Jacobsen et al., 2011). In general, WSN is a collection of self-directed devices that are associated wirelessly. Sensor networks are an example of wireless networks that need every sensor to execute events in synchronization. This synchronization coordinates power cycles conserve energy and ensures the smooth operation of WSNs that calculate time-sensitive events. So, our study focus in energy efficient is sensor network which classify in Fig. 1.

Clock synchronization is an important issue in the operation of any distributed WSN. Synchronization sets the same time limit for different sensor nodes, which unifies functions for video and voice data, organizes different wakeup or sleep node scheduling schemes and ensures time-based channel distribution (Wu et al., 2011).

Fig. 1: Classification of energy efficient issues in sensor networks

Clock synchronization has several advantages over unsynchronized systems (Chu et al., 2006) and is often inherently assumed to facilitate certain techniques and algorithms on physical and Medium Access Control (MAC) layers. From the physical layer perspective, slot synchronization enables advanced cooperative transmission technologies. From the perspective of the MAC layer, slot synchronization enables the coordinated packet transmission of nodes in order to attain optimal throughput and power efficiency. This study provides an extensive overview of the energy efficient on aspect of clock synchronization in a wireless sensor network. In addition, the idea of data transmission based on synchronization can be ensured through the optimization of energy usage periodic data capturing in the wireless sensor network.

This study presents an overview of the energy efficient on aspect of clock synchronization in a wireless sensor network. The main goal of this study is to assist WSN researchers and novices to gain a better understanding of the energy efficient on aspect of clock synchronization in a wireless sensor network and to promote effective designs and systems that address this problem.


Recent advances in the areas of Micro-electrical Mechanical Systems (MEMS) spurred the interest of researchers in Wireless Snsor Networks (WSNs). These networks are made up of sensor nodes which have the capability to sense, process and transmit gathered data of an environmental phenomenon of interest. A persistent issue in the wireless sensor network is the problem of coverage-holes. In medium and large deployments in hostile regions, random dropping of sensor nodes by unmanned vehicles or low flying helicopters may remain the only feasible deployment option. Even where deterministic deployment is possible, coverage-holes will emerge as the sensors run out of battery energy. This problem becomes even more pronounced for nodes located within close proximity to the base station, which are usually the system bottleneck due to their high data relaying task. Moreover, the sensor nodes are left to operate on their own after initial deployment, thereby making the coverage problem even more difficult to solve. As mention above, various researchers have provided different definitions of WSN. Ramirez et al. (2012) defined WSN as a collection of autonomous devices or nodes that are connected wirelessly. Sharma et al. ( 2010) described WSN as a group of thousands of tiny sensor nodes that can perform wireless communication, limited calculation and sensing.

Hanapi et al. (2009) stated that WSN can be a heterogeneous sensor network that consists of many low-cost and low-power sensor nodes that are more likely deployed at fixed locations. These sensor nodes can communicate with one another through Radio Frequency (RF), sense and relay sensor data to other users and compute physical attributes (e.g., pressure, temperature, motion, sound and vibration). Stojcev et al. (2011) explained that WSNs are large-scale sensor networks that monitor and observe various aspects of the natural world. An excerpt from the Defense Advanced Research Project Agency in Bala (2009) states that sensor node networks use many devices that can compute, sense and communicate through additional devices to compile local data and formulate conclusions about the physical world. Bala (2009) also mentioned that according to the United States National Research Council, sensor node networks are composed of a large number of sensors that are commonly used in mechanical and electrical systems to manage (i.e., effect) and observe (i.e., sense) almost all aspects of the natural world.

Fig. 2: Typical wirless sensor network elements

Table 1: Description of sensor network elements

A WSN comprises a sensor, node, base station, gateway and coordinator. Figure 2 illustrates the WSN elements and Table 1 provides the definition of each element.

As shown in Fig. 1, the research issues of energy efficient in WSN classified into routing, localization, clock synchronization, data aggregation, clustering, security and so on. In this study, we focus the energy efficient issue on aspects of the clock synchronization in WSN.


As mentioned earlier, Wireless Sensor Networks (WSNs) is an example of a wireless network that needs each sensor to cooperatively participate in synchronization, event detection and transmission. Such synchronization is vital for the proper coordination of the power cycles for energy conservation. Moreover, each sensor node in WSNs has its own clock. Clock synchronization provides a common clock/time frame for widely distributed sensors. However, this task is not easy to accomplish because of the unique properties of WSNs. A universal time is normally unavailable in WSNs. Therefore, traditional clock/time center-based synchronization methods or tools cannot be applied directly on each of the services at the sensor nodes (e.g., sensing, routing, group management, localization, time synchronization, power management and medium access control). Moreover, sensors often have limited processing and sensing capabilities. Therefore, sensors have to cooperate to perform a task over a wide physical region, which can be achieved with the use of information aggregated across the entire network and sensors. Hence, a low-overhead method and accurate clock synchronization are ideal for sensor-based applications. In national schemes, clock synchronization is unnecessary because clock confusion does not exist. By contrast, in disseminated systems such as WSNs, no universal memory or time exists (Stojcev et al., 2011).

Clock synchronization in WSNs has attracted extensive attention because it is a crucial issue in the operation of WSNs (Tripathi et al., 2010). It unifies different functions, such as video and voice data from dissimilar sensor nodes, wake/sleep scheduling for nodes and time-based channel sharing (Mei, 2010; Sundararaman et al., 2005).

Fig. 3: Basic block diagram of clock elements and associated timer hardware

A consistent clock/time is important for sense functions to ensure precise time stamping of sectioned information (Jacobsen et al., 2011). Clock synchronization is a complex problem that can be resolved by using the computer system of a distributed scheme. The classification of clock synchronization is shown in Fig. 1. Data Fusion is a fundamental operation in all disseminated WSNs for integrating and processing gathered data. WSNs typically span vast geographic regions and consist of many nodes because of the limitations of individual sensor nodes. One single sensor cannot capture all information and thus, information from all sensors should be obtained. Data fusion requires several or all nodes in the WSN to have a common time scale. An event can be monitored simultaneously by multiple sensors. Information at dissimilar sensors can be combined to contain extra data. Such information integration requires several or all sensors to have a common time scale. This condition is necessary when the environment under surveillance is time varying. In entity tracking, every sensor node discovers a moving entity when it enters the sensor’s vicinity. The entity can be tracked by cross-referencing its time and position as recorded by sensors along its path. The recorded time is accurate when the times of the sensors are synchronized (Hall and Llinas, 1997; Madden et al., 2002; Waltz and Llinas, 1990; Yuan et al., 2003). Power Management is unattended and are battery powered. In addition, they do not undergo regular servicing or battery changes. Most fundamental operations in WSN will adopt wake/sleep protocols to conserve energy wherever certain sensors enter a low-power sleep mode or switch off when their neighboring sensors are on duty. Therefore, network-wide clock synchronization is important because it can ensure time synchronization precision and efficient power cycling (Bekmezci and Alagoz, 2009). In contrast, transmission scheduling has many protocols that require synchronization. For example, Time Division Multiple Access (TDMA) accesses and permits multiple devices to distribute access to a common communication medium (Heidarian et al., 2012). One transmission period is classified into multiple slots in TDMA to allow transmission without collisions or interference and every slot is allocated to only one sensor node in a suitable area. Each sensor node is enabled to transmit only throughout the dedicated time slot (Bekmezci and Alagoz, 2009). Such protocols are valid only in a synchronized network. So, in the transmission scheduling there many problems happened such as deafness problem, idle listening problem, collision-free, hidden terminal problem and overhearing problem.

Importance of clock synchronization: Each sensor node maintains a local time generated by its own clock (its own concept of time). Different factors make flexible and robust clock synchronization especially important. Time in sensor nodes is typically conserved by a particular sub-scheme, as shown in Fig. 3 (Schmid et al., 2010).

The time driver stimulates the resonating component that eventually resonates and filters at a certain frequency. Software can use this hardware counter for time measurement and timers. The hardware counter utilizes a signal to increase a counting register at regular intervals.

For any two clock Ca and Cb, our study will propose the following terminologies, as shown in Table 2, which are consistent with definitions given in (Mills, 1992a, b; Moon et al., 1999; Sundararaman et al., 2005).

For existing algorithms and applications that use WSNs, we aim to know more about time synchronization, including classification according to the relative arrangement of actions that occurred in different sensor nodes, the time of the day when an event occurred in a particular sensor node and the time period among two actions that occurred in various sensor nodes.

Table 2: Components of clock terminology

Fig. 4: A sample design of clock synchronization in WSN

For the system design of applications and algorithms, clock synchronization sets a common clock/time throughout a distributed system and ensures that WSNs can perform basic operations.

Challenges to clock synchronization: In recent years, numerous algorithms/protocols have been designed to maintain synchronized clocks over computer networks. A flowchart of planned clock synchronization is presented in Fig. 4. Unfortunately, in an actual wireless network, various component delays affect message delivery. Table 3 explains the causes of each contribution and indicates the variability of the delays and randomness (Maggs et al., 2012; Sivrikaya and Yener, 2004). Ensuring time synchronization is more difficult than it appears to be.

A communication propagation sequence should be created to approximate the relative time offsets and skews between nodes. Time synchronization in WSNs should be considered when eliminating the impacts of random delays from the technique communication propagations forwarded in WSN channels.

Delay components can be classified into random and fixed delays. Random delays rely on diverse network parameters (e.g., traffic and network status). Thus, it applies to different cases and has been modeled as random delays in WSNs that contain gamma sharing, gaussian sharing, exponential sharing and weibull sharing based on different applications and validations (Bovy et al., 2002; Leon-Garcia, 1994; Papoulis, 1991).

Fixed delays are typically unfamiliar and if they are not modeled properly, they will be considered a part of the clock offset, which results in less precise timing parameter estimation (Abdel-Ghaffar, 2002). WSNs also have to deal with limited and non-rechargeable power resources in clock synchronization. Time synchronization contributes to energy consumption because of the great amount of energy used by radio propagations to transmit time data. RF requires 3 J to transmit 1 kb over a hundred meters, which is equal to the energy required to transmit to transmit to three million directions (Pottie and Kaiser, 2000). Thus, efficient synchronization algorithms can reduce communication overhead and computational power.

Fundamental approaches to clock synchronization for WSN: Transmitting sensors are the basic components involved in time synchronization, which could be accomplished by transferring timing communications to the sensor nodes; these timing communications are timestamped. Fundamental clock synchronization approaches can be classified into three timing communication signaling approaches (Maggs et al., 2012; Wu et al., 2011), as shown in Table 4.

Requirements of clock synchronization schemes for WSN: Clock synchronization requirements can be considered metrics for evaluating clock synchronization designs for WSNs (Reis and Carvalho, 2013). Compromises among the requirements of an efficient synchronization approach (Maggs et al., 2012; Sivrikaya and Yener, 2004) exist, as shown in Table 5.

Table 3: Description of message delivery affect in sensor network

Table 4: Fundamental approaches of clock synchronization in WSN

Table 5: The main requirements of clock synchronization in WSN

As a result, a single scheme may not satisfy all the requirements.

Clock synchronization protocols for WSN: Depending on network size, clock synchronization protocols can be classified into network-wide and pairwise synchronization.

Network-wide clock synchronization for WSN concentrates on sensor nodes that are organized into a multi-hop network. This type of synchronization can generally be obtained by extending pairwise clock synchronization derived from diverse communication structures, for which perfect structures must be robust, low cost and scalable. Network-wide clock synchronization also aims to develop a common time frame for a group of sensor nodes such that any two sensors have very similar clock readings (Lemmens et al., 2012).

Pairwise clock synchronization for WSN concentrates on two neighboring sensor nodes that are in every other node’s communication range, for which perfect algorithms achieve precise clock synchronization and reduce random effects caused by communication delays through communication load and minimum computation. Pairwise clock synchronization for WSN in the presence of unknown exponential delays was also considered under a two-way message exchange mechanism (Wu et al., 2012).


Sensor networks are an example of a wireless network that needs each sensor to execute events in synchronization. This synchronization is necessary to coordinate power cycles to conserve energy and to ensure the proper functioning of sensors that measure time-sensitive events. For example, the problem nature world has solved some of biological systems such as the beating of a heart and synchronized flashing of fireflies, can keep a globally synchronous oscillation based only on local observations. In the case of fireflies, this can be over significant distances. The deafness problem, which is the main problem of clock synchronization for WSNs. Deafness occurs when sensor nodes could not receive and transmit simultaneously. So, the deafness leads to longer delay, wasting energy, excessive packet drop and channel access unfairness.

Given the fact that the whole WSN communicate through a single frequency channel, concurrent communication of two or more nodes within communication range could result in packet collision and deafness. In order to avoid such a problem, a random offset is attached to the synchronization messages. Using the offset values, the receiving node can then reconstruct the required synchronization instant and thus adjust the clock rate in accordance with the received offset. In this way, the random offset selection may lead to the reception of synchronization messages in a way that may be out of order; thus causing a problem especially in the case of simple synchronization models. This problem can however, be solved using the reachback algorithm. Here, the synchronization events are collected up to the end of the period in order to attain the time information from the last period. The energy consumption plays an important role for the device lifetime in battery-powered wireless sensor networks, especially if no infrastructure is available.

As mentioned above, we can classify how to solve the impact of deafness problem on clock synchronization in WSN through see the design in Fig. 5.

Example of impacts deafness problem on clock synchronization in WSN performance are investigated several nodes of sensor network (Taniguchi et al., 2013). It was classified in Fig. 6 (Taniguchi et al., 2007; Degesys et al., 2007) based on PCO in order to the hop count.

Figure 7 shows the energy consumption in term of the number of nodes. It is found, as expected, that the consumed energy ratio of the randomization-based mechanism in term of the number of nodes is lower than that of the desynchronization-based mechanism. As a result, the desynchronization-based is appropriate for WSN applications that require high data collecting. Nevertheless, it needs added control overhead to keep away from collisions and deafness between hidden-terminal nodes. In contrast, randomization-based is appropriate for WSN applications which require simplicity of mechanism and energy efficiency instead of data collecting ratio. Even though the randomization-based does not clearly believe the hidden-terminal node problem, it is simpler and needs less control overhead.

Also, this study comparison among existing methods on energy efficient aspect of clock synchronization in WSN as depicts in Table 6.

This table presents studies that address deafness from different perspectives. Studies 1, 2, 3, 4, 6, 8 and 9 adopt the antenna perspective to avoid deafness. Studies 5, 7, 10 and 12 take the MAC protocol perspective to mitigate deafness.

Fig. 5:
Impact design of deafness problem on clock synchronization in sensor network

Fig. 6: Classified of pulse coupled oscillator in deafness problem

Table 6: Comparison of existing mechanisms on energy efficient aspect of clock synchronization in WSN

Fig. 7:
Energy consumptions relative to the No. of node in WSN (Taniguchi et al., 2013)


This study presents previous studies of the impact of deafness problem on clock synchronization in a wireless sensor network. A comparison among methods is, presented in Table 6, which can facilitate the process of ensuring clock synchronization in WSNs. In addition, this study can significantly help not only in ensuring efficient sensor network use but also in reducing energy consumption and therefore, increasing the potential for increasing the lifespan of a sensor network. This work is a useful source on clock synchronization that can provide WSN researchers and novices with a better understanding of the deafness problem to promote effective designs and systems.

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