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Journal ArticleDOI

Estimation of Frequency Offset for Time Synchronization With Immediate Clock Adjustment in Multihop Wireless Sensor Networks

26 Sep 2017-IEEE Internet of Things Journal (IEEE)-Vol. 4, Iss: 6, pp 2239-2246
TL;DR: This paper analyzes time synchronization of sensor nodes with immediate clock adjustment at every cycle under multihop scenario, and presents an estimator of clock skew under the Gaussian linear delay model and the corresponding algorithms for finding the estimator in detail.
Abstract: The clock synchronization problem for wireless sensor networks (WSNs) is inherently related to parameter estimation. Nowadays, extensive studies on time synchronization have been conducted by adopting statistical signal processing methods. However, most estimation schemes do not readjust clock offset during the process of synchronization parameter estimation, and it would lead to unsatisfactory clock accuracy during synchronization. Thus, the applications of these methods are greatly limited in WSNs. This paper analyzes time synchronization of sensor nodes with immediate clock adjustment at every cycle under multihop scenario, and presents an estimator of clock skew under the Gaussian linear delay model and the corresponding algorithms for finding the estimator in detail. Simulation results verify that the proposed estimator is efficient.
Citations
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Journal ArticleDOI
02 Oct 2020-Sensors
TL;DR: This paper is a holistic overview of the available time synchronization methods for IoT deployments, including detailed derivations of the clock model and various clock relation models, and their expected performance.
Abstract: Internet of Things (IoT) is expected to change the everyday life of its users by enabling data exchanges among pervasive things through the Internet. Such a broad aim, however, puts prohibitive constraints on applications demanding time-synchronized operation for the chronological ordering of information or synchronous execution of some tasks, since in general the networks are formed by entities of widely varying resources. On one hand, the existing contemporary solutions for time synchronization, such as Network Time Protocol, do not easily tailor to resource-constrained devices, and on the other, the available solutions for constrained systems do not extend well to heterogeneous deployments. In this article, the time synchronization problems for IoT deployments for applications requiring a coherent notion of time are studied. Detailed derivations of the clock model and various clock relation models are provided. The clock synchronization methods are also presented for different models, and their expected performance are derived and illustrated. A survey of time synchronization protocols is provided to aid the IoT practitioners to select appropriate components for a deployment. The clock discipline algorithms are presented in a tutorial format, while the time synchronization methods are summarized as a survey. Therefore, this paper is a holistic overview of the available time synchronization methods for IoT deployments.

24 citations


Cites background from "Estimation of Frequency Offset for ..."

  • ...[102], where the nodes adjust their clock values before updating their clock parameter estimates....

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Journal ArticleDOI
TL;DR: In this paper, the authors proposed an asymmetric high-precision time synchronization scheme that can provide high precision time synchronization even with resource-constrained sensor nodes in multi-hop WSNs.

19 citations

Journal ArticleDOI
TL;DR: Simulations show that the synchronization performance of the proposed algorithm is better than existing similar schemes under truncated exponential delays, and the rigorous theoretical proof of the convergence of the network-wide synchronization is given.
Abstract: Clock synchronization is a significant basis for many operations in wireless sensor networks. The distributed consensus-based clock synchronization has gained popularity for its robustness and scalability. However, most consensus-based clock synchronization protocols either ignore communication delay, or consider bounded delay without distribution model. In this article, based on the consensus theory, the clock synchronization problem with truncated exponential delay is investigated. We use the maximum likelihood method to estimate relative drift between nodes. A recursive method is presented to cope with the maximum likelihood estimation, which largely reduces the computational complexity and the storage overhead. Then, the estimated relative drift is adopted in the two main parts of consensus clock synchronization: drift compensation and offset compensation. We give the rigorous theoretical proof of the convergence of the network-wide synchronization. Simulations further verify the theoretical analysis and show that the synchronization performance of the proposed algorithm is better than existing similar schemes under truncated exponential delays.

16 citations

Journal ArticleDOI
TL;DR: Both simulation and hardware experiments show that BETS algorithm makes full use of the prior information of synchronization error, hence fewer time messages are required in synchronization and the resource constraints of WSNs are satisfied.
Abstract: Clock synchronization is essential for the operation of upper layer applications in Wireless Sensor Networks. When the network hops needed for clock synchronization message transmission is large, synchronization error will accumulate and synchronization accuracy may be reduced significantly. Moreover, in the existing synchronization algorithms, large number of communication resources and node energy will be expended in sending and receiving time messages. To solve the problem, this paper proposes a Bayesian estimation-based time synchronization (BETS) algorithm which uses synchronization error compensation to reduce the amount of time message interaction in clock synchronization. The key idea of BETS is to calibrate the prior information of synchronization error with a small amount of field sampling time information, which will eliminate the impact of environment on clock synchronization accuracy. In addition, the gradient descent method is used to estimate the relative clock drift rate, which provides the reference for setting algorithm execution cycle and ensures clock synchronization during network operation time. In order to evaluate the theoretical lower bound of the performance of BETS, the Bayesian Cramer-Rao bound (BCRB) is derived. Both simulation and hardware experiments show that BETS algorithm makes full use of the prior information of synchronization error, hence fewer time messages are required in synchronization and the resource constraints of WSNs are satisfied.

16 citations


Cites background from "Estimation of Frequency Offset for ..."

  • ...In [12], time synchronizationwith immediate clock adjustment (TSICA) is proposed....

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Journal ArticleDOI
TL;DR: A general ROS model under the exponential delay assumption, with clock skews taken into consideration and all timing messages utilized, is introduced and a three-step method to jointly estimate clock offsets and skews of both active and silent nodes, as well as other unknown parameters is developed.
Abstract: In this article, we consider the receiver-only-based time synchronization in underwater wireless sensor networks with exponential delays emerging during the message exchanges. This article is motivated by the fact that 25% of timing messages are discarded in the existing receiver-only-synchronization (ROS)-based schemes to cope with the interaction between the discontinuity of the likelihood function and the unknown clock skews, which would result in a massive waste of energy. To account for this issue, a general ROS model under the exponential delay assumption, with clock skews taken into consideration and all timing messages utilized, is introduced. Following the proposed ROS model, we develop a three-step method to jointly estimate clock offsets and skews of both active and silent nodes, as well as other unknown parameters. Specifically, an estimation on clock offset and skew for the active node using support vector machine is followed by a joint maximum-likelihood estimation of the clock skew and clock offset for the silent node as well as clock offset for the active node, and a minimum variance unbiased estimation of the offsets for both active and silent nodes. Such method further improves the estimation accuracy of the proposed ROS model. The effectiveness and robustness of the proposed method is verified by simulations results.

9 citations


Cites background from "Estimation of Frequency Offset for ..."

  • ...The clocks of nodes in a sensor network, however, have slightly different offsets and running frequencies caused by initial setting errors and imperfect quartz crystals [21], which makes time synchronization a prerequisite and a crucial support technique for UWSNs [22]....

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References
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Journal ArticleDOI
09 Dec 2002
TL;DR: Reference Broadcast Synchronization (RBS) as discussed by the authors is a scheme in which nodes send reference beacons to their neighbors using physical-layer broadcasts, and receivers use their arrival time as a point of reference for comparing their clocks.
Abstract: Recent advances in miniaturization and low-cost, low-power design have led to active research in large-scale networks of small, wireless, low-power sensors and actuators. Time synchronization is critical in sensor networks for diverse purposes including sensor data fusion, coordinated actuation, and power-efficient duty cycling. Though the clock accuracy and precision requirements are often stricter than in traditional distributed systems, strict energy constraints limit the resources available to meet these goals.We present Reference-Broadcast Synchronization, a scheme in which nodes send reference beacons to their neighbors using physical-layer broadcasts. A reference broadcast does not contain an explicit timestamp; instead, receivers use its arrival time as a point of reference for comparing their clocks. In this paper, we use measurements from two wireless implementations to show that removing the sender's nondeterminism from the critical path in this way produces high-precision clock agreement (1.85 ± 1.28μsec, using off-the-shelf 802.11 wireless Ethernet), while using minimal energy. We also describe a novel algorithm that uses this same broadcast property to federate clocks across broadcast domains with a slow decay in precision (3.68 ± 2.57μsec after 4 hops). RBS can be used without external references, forming a precise relative timescale, or can maintain microsecond-level synchronization to an external timescale such as UTC. We show a significant improvement over the Network Time Protocol (NTP) under similar conditions.

2,537 citations

Proceedings ArticleDOI
03 Nov 2004
TL;DR: The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update and comprehensive error compensation including clock skew estimation, which is markedly better than that of the existing RBS and TPSN algorithms.
Abstract: Wireless sensor network applications, similarly to other distributed systems, often require a scalable time synchronization service enabling data consistency and coordination. This paper describes the Flooding Time Synchronization Protocol (FTSP), especially tailored for applications requiring stringent precision on resource limited wireless platforms. The proposed time synchronization protocol uses low communication bandwidth and it is robust against node and link failures. The FTSP achieves its robustness by utilizing periodic flooding of synchronization messages, and implicit dynamic topology update. The unique high precision performance is reached by utilizing MAC-layer time-stamping and comprehensive error compensation including clock skew estimation. The sources of delays and uncertainties in message transmission are analyzed in detail and techniques are presented to mitigate their effects. The FTSP was implemented on the Berkeley Mica2 platform and evaluated in a 60-node, multi-hop setup. The average per-hop synchronization error was in the one microsecond range, which is markedly better than that of the existing RBS and TPSN algorithms.

2,267 citations

Proceedings ArticleDOI
05 Nov 2003
TL;DR: It is argued that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes and use simulations to verify its accuracy over large-scale networks.
Abstract: Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are time-stamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks.In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20ms. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.

2,215 citations


"Estimation of Frequency Offset for ..." refers background in this paper

  • ...of a sensor network is to generate a hierarchical structure in the network as the timing-sync protocol for sensor networks (TPSN) in [29], which is one of the most widely used proto-...

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  • ...1(b), due to it does not estimate clock skew of nodes, the continuous readjustment is necessary [29]....

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Book
27 May 2005
TL;DR: This book discusses the design principles for wireless sensor networks, and the many faces of forwarding and routing, and some of the approaches to combining hierarchical topologies and power control used in these networks.
Abstract: Preface. List of Abbreviations. A guide to the book. 1. Introduction. 1.1 The vision of Ambient Intelligence. 1.2 Application examples. 1.3 Types of applications. 1.4 Challenges for WSNs. 1.5 Why are sensor networks different? 1.6 Enabling technologies. PART I: ARCHITECTURES. 2. Single node architecture. 2.1 Hardware components. 2.2 Energy consumption of sensor nodes. 2.3 Operating systems and execution environments. 2.4 Some examples of sensor nodes. 2.5 Conclusion. 3. Network architecture. 3.1 Sensor network scenarios. 3.2 Optimization goals & figures of merit. 3.3 Design principles for WSNs. 3.4 Service interfaces of WSNs. 3.5 Gateway concepts. 3.6 Conclusion. PART II: COMMUNICATION PROTOCOLS. 4. Physical Layer. 4.1 Introduction. 4.2 Wireless channel and communication fundamentals. 4.3 Physical layer & transceiver design considerations in WSNs. 4.4 Further reading. 5. MAC Protocols 133 5.1 Fundamentals of (wireless) MAC protocols. 5.2 Low duty cycle protocols and wakeup concepts. 5.3 Contention-based protocols. 5.4 Schedule-based protocols. 5.5 The IEEE 802.15.4 MAC protocol. 5.6 How about IEEE 802.11 and Bluetooth? 5.7 Further reading. 5.8 Conclusion. 6. Link Layer Protocols. 6.1 Fundamentals: Tasks and requirements. 6.2 Error control. 6.3 Framing. 6.4 Link management. 6.5 Summary. 7. Naming and Addressing. 7.1 Fundamentals. 7.2 Address and name management in wireless sensor networks. 7.3 Assignment of MAC addresses. 7.4 Distributed assignment of locally unique addresses. 7.5 Content-based and geographic addressing. 7.6 Summary. 8. Time Synchronization. 8.1 Introduction to the time synchronization problem. 8.2 Protocols based on sender/receiver synchronization. 8.3 Protocols based on receiver/receiver synchronization. 8.4 Further reading. 9. Localization and Positioning. 9.1 Properties of positioning. 9.2 Possible approaches. 9.3 Mathematical basics for the lateration problem. 9.4 Single-hop localization. 9.5 Positioning in multi-hop environments. 9.6 Impact of anchor placement. 9.7 Further reading. 9.8 Conclusion. 10. Topology control 295 10.1 Motivation and basic ideas. 10.2 Flat network topologies. 10.3 Hierarchical networks by dominating sets. 10.4 Hierarchical networks by clustering. 10.5 Combining hierarchical topologies and power control. 10.6 Adaptive node activity. 10.7 Conclusions. 11. Routing protocols. 11.1 The many faces of forwarding and routing. 11.2 Gossiping and agent-based unicast forwarding. 11.3 Energy-efficient unicast. 11.4 Broadcast and multicast. 11.5 Geographic routing. 11.6 Mobile nodes. 11.7 Conclusions. 12. Data-centric and content-based networking 395. 12.1 Introduction. 12.2 Data-centric routing. 12.3 Data aggregation. 12.4 Data-centric storage. 12.5 Conclusions. 13. Transport Layer and Quality of Service. 13.1 The transport layer and QoS in wireless sensor networks. 13.2 Coverage and deployment. 13.3 Reliable data transport. 13.5 Block delivery. 13.6 Congestion control and rate control. 14. Advanced application support. 14.1 Advanced in-network processing. 14.2 Security. 14.3 Application-specific support. Bibliography. Index.

1,894 citations


"Estimation of Frequency Offset for ..." refers methods in this paper

  • ...In [32], the sensor node’s clock is corrected at every synchronization cycle [as with Fig....

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