DISTY: Dynamic Stochastic Time Synchronization for Wireless Sensor Networks

Masood, Wasif; Schmidt, Jorge F.; Brandner, Günther; Bettstetter, Christian

doi:10.1109/tii.2016.2618348

Cited by 36 publications

(24 citation statements)

References 16 publications

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“…In the work by Kim et al [ 64 ], the authors have studied higher order autoregressive models for clock skew, where they have also validated the model order with well-known model selection methods. The same line of reasoning has motived Masood et al [ 65 ] to study alternative models with both open-loop and feedback terms. Such high order extensions cannot be easily linked to the well known physical clock parameters.…”

Section: Clock Modelsmentioning

confidence: 89%

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Yi̇ği̇tler

Badihi

Jäntti

2020

Sensors

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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.

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Section: Clock Modelsmentioning

confidence: 89%

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Yi̇ği̇tler

Badihi

Jäntti

2020

Sensors

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“…and set the time interval between two synchronizations to be the period of sending packets from the controller. It is worth noting that there exist several industrial technologies for timing synchronization in networks [19], [20]. For example, the Network Time Protocol (NTP) can provide a synchronization accuracy at the level of 1 to 50 ms [20], which is well suited for the considered application in this work.…”

Section: A Overview Of Loose Timing Synchronizationmentioning

confidence: 99%

Power Peak Shaving With Data Transmission Delays for Thermal Management in Smart Buildings

Yuan

Yao

Zhu

et al. 2018

IEEE Trans. Ind. Inf.

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“…Taking into account these two parameters, a drift rate is calculated and applied. This approach, with minor variations, has been widely used in the literature (see [13], [18], [19], for instance). This method requires short time updates to avoid large temperature fluctuations between corrections.…”

Section: Related Workmentioning

confidence: 99%

Accurate Clock Discipline For Long-Term Synchronization Intervals

2017

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Efficient spectrum usage and optimized energy consumption directly depend on the radio duty cycle of the communicating devices. Tight synchronization of communicating nodes enables optimal orchestration of the access to the medium as nodes turn their radios on precisely when needed. Achieving synchronization, however, requires a reference time source to synchronize with and a periodic discipline mechanism to cope with the inherent clock drift. Packet-based synchronization or external time sources, such as GPS, are usually required to achieve that goal. When low-power operation is combined with bandwidth limitations and scale, as in the case of low power wide area networks, the overhead of such approaches is not affordable and advanced clock discipline mechanisms are required. In this paper, we propose a novel adaptive mechanism to discipline clocks in order to guarantee a 1 ppm drift with minimal communication overhead.

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DISTY: Dynamic Stochastic Time Synchronization for Wireless Sensor Networks

Cited by 36 publications

References 16 publications

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Power Peak Shaving With Data Transmission Delays for Thermal Management in Smart Buildings

Accurate Clock Discipline For Long-Term Synchronization Intervals

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