Maximum likelihood trend estimation in exponential noise

Trump, Tonu

doi:10.1109/78.942636

Cited by 16 publications

(14 citation statements)

References 12 publications

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“…Unfortunately, the clock skew is not considered in these works, resulting in potentially frequent re-synchronization. On the other hand, the joint estimation of clock skew and fixed delay is studied in [15] with the clock offset left uncompensated, and the scheme is effective for clock synchronization when the peer clocks are initially started at the same time, i.e., the clock offset is zero. Apparently, by considering the fixed delay as a part of the clock offset, the algorithm in [15] can be adopted to jointly estimate clock skew and clock offset under the assumption that there is no fixed delay.…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Leng¹,

Wu²

2011

IEEE Trans. Signal Process.

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In this paper, the clock synchronization for wireless sensor networks in the presence of unknown exponential delay is investigated under the two-way message exchange mechanism. The maximum-likelihood estimator for joint estimation of clock offset, clock skew and fixed delay is first cast into a linear programming problem. Based on novel geometric analyses of the feasible domain, a low-complexity maximum likelihood estimator is then proposed. Complexities of the proposed estimators and existing algorithms are compared analytically and numerically. Simulation results further demonstrate that our proposed algorithms have advantages in terms of both performance and computational complexities.Index Terms-Clock synchronization, exponential delays, two-way message exchange mechanism, wireless sensor networks.

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Section: Introductionmentioning

confidence: 99%

Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Leng¹,

Wu²

2011

IEEE Trans. Signal Process.

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“…This model of (1) has been extensively studied in a slightly different context of trend [11] or clock skew [12] estimation where not only frequency ratio but also phase difference between the clocks are to be estimated. Because we need to estimate only the ratio R for SCFR, however, we can reformulate (1) as a linear regression through the origin (RTO) model without a constant term (i.e., α) [13] by subtracting initial values from arrival times, timestamps, and packet delays: For k ≥ 1,…”

Section: A Regression Through the Origin (Rto) Modelmentioning

confidence: 99%

Asynchronous Source Clock Frequency Recovery through Aperiodic Packet Streams

Kim

2013

IEEE Commun. Lett.

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Abstract-We consider the most general case of source clock frequency recovery (SCFR) in packet networks, i.e., asynchronous SCFR through aperiodic packet streams, where there is neither a common reference clock nor any relation between packet generation intervals and a source clock frequency. We formulate the problem of asynchronous SCFR with timestamps as a linear case of regression through the origin (RTO) and propose two schemes, one based on recursive least squares (RLS) method and the other based on simple heuristics of cumulative ratio of interarrival and interdeparture times, which provide better estimates of the source clock frequency with faster convergence than conventional phase-locked loop (PLL)-based schemes.

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“…The results of SCFR at the sensor node and measurement time estimation at the head node (i.e., the estimation of the 5 This is the interval of beacons for the proposed scheme and the interval of two-way message exchanges for others. Fig.…”

Section: B Performance Of Measurement Time Estimation and Energy Effmentioning

confidence: 99%

“…For the analysis of the performance of the proposed time synchronization scheme, we run simulations for three different values of synchronization interval (SI) 5 -i.e., 100 s, 1 s and 10 ms-to investigate the effect of the time difference T m between the last time synchronization message from the head node and the measurement occurrence at the sensor node. The propagation delay is modeled as an i.i.d.…”

Section: B Performance Of Measurement Time Estimation and Energy Effmentioning

confidence: 99%

“…In the literature, time synchronization is formulated as the problem of estimating clock parameters, often including node distances, in a pairwise (distributed) or a network-wide (global) manner [5]- [8]. Few works, however, have focused on the mode of operation (i.e., time vs. clock synchronization discussed above) and the way of implementation related with energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Energy-Efficient Time Synchronization Based on Asynchronous Source Clock Frequency Recovery and Reverse Two-Way Message Exchanges in Wireless Sensor Networks

Kim

Lee

Lim

2016

IEEE Trans. Commun.

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Abstract-We consider energy-efficient time synchronization in a wireless sensor network where a head node (i.e., a gateway between wired and wireless networks and a center of data fusion) is equipped with a powerful processor and supplied power from outlet, and sensor nodes (i.e., nodes measuring data and connected only through wireless channels) are limited in processing and battery-powered. It is this asymmetry that our study focuses on; unlike most existing schemes to save the power of all network nodes, we concentrate on battery-powered sensor nodes in minimizing energy consumption for time synchronization. We present a time synchronization scheme based on asynchronous source clock frequency recovery and reverse two-way message exchanges combined with measurement data report messages, where we minimize the number of message transmissions from sensor nodes, and carry out the performance analysis of the estimation of both measurement time and clock frequency with lower bounds for the latter. Simulation results verify that the proposed scheme outperforms the schemes based on conventional two-way message exchanges with and without clock frequency recovery in terms of the accuracy of measurement time estimation and the number of message transmissions and receptions at sensor nodes as an indirect measure of energy efficiency.

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Maximum likelihood trend estimation in exponential noise

Cited by 16 publications

References 12 publications

Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Asynchronous Source Clock Frequency Recovery through Aperiodic Packet Streams

Energy-Efficient Time Synchronization Based on Asynchronous Source Clock Frequency Recovery and Reverse Two-Way Message Exchanges in Wireless Sensor Networks

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