Synchronization and Localization
                  in Wireless Networks

Etzlinger, Bernhard; Wymeersch, Henk

doi:10.1561/2000000096

Cited by 19 publications

(15 citation statements)

References 34 publications

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“… [ 112 ] Channel Prediction and Estimation Improve flexibility, scalability, and generality. [ 112 , 113 , 114 , 115 ] Channel Coding [ 116 , 117 ] Synchronization, Localization [ 118 , 119 , 120 ] Beamforming [ 121 , 122 ] Data Link Layer Dynamic Spectrum Sharing Make use of the dynamic environment to tune transmission parameters to improve channel throughput and user sum rate. [ 112 ] Network Layer Traffic and Mobility Prediction Improve Prediction Accuracy.…”

Section: Enabling Technologies and Challenges For 6gmentioning

confidence: 99%

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Imoize

Adedeji

Tandiya

et al. 2021

Sensors

155

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The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

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Section: Enabling Technologies and Challenges For 6gmentioning

confidence: 99%

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Imoize

Adedeji

Tandiya

et al. 2021

Sensors

155

View full text Add to dashboard Cite

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“…In this paper, the affine clock model [29] is applied. The local clock time of wireless node i can be presented as:…”

Section: Ranging Schemes Based On Tof and Sensor Network Model For Localization A Twr And Sds-twr Ranging Schemementioning

confidence: 99%

A Compensated Multi-Anchors TOF-Based Localization Algorithm for Asynchronous Wireless Sensor Networks

et al. 2019

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Tag localization for asynchronous wireless sensor networks requires the development of a scheme for clock synchronization. This remains a difficult and open problem since the performance of tag localization can be adversely affected by complications such as reply time and relative clock skew. Joint clock synchronization and a tag localization algorithm that implements a multi-anchor compensated timeof-flight (TOF) to the asynchronous wireless sensor network is a possible and viable solution. Although previous methods that leverage TOF measurements are effective and easily conducted, their performance is not always superior due to the relative clock skew. In this paper, we propose to extend the joint clock/tag synchronization/localization algorithm by introducing a compensation factor that can cancel relative clock skews from multi-tag anchor pairs. We apply a least squares estimation (LSE) algorithm to both the time of emission (TOE) and time of arrival (TOA) for the clock synchronization step. Under the assumption of a Gaussian measurement noise model, the tag localization problem is approximately solved by maximum likelihood estimation (MLE). To assess the performance of our algorithm, we derive the mean square error (MSE) of both relative clock skew and tag location and numerically evaluate the Cramér-Rao lower bound (CRLB) as a benchmark. The simulation results show that the accuracy of the relative clock skew-based estimation and tag localization are significantly improved over traditional algorithms when the appropriate reply time is selected. This is what our proposed algorithm focuses on: it is robust to tag mobility to some extent. We test the performance of proposed algorithm using a well-designed experiment. Based on the experiment results, the localization algorithm can achieve high accuracy without an additional restriction on the reply time and the clock skew.INDEX TERMS Source localization, wireless sensor network (WSN), time-of-flight (TOF), symmetric double-sided two-way ranging (SDS-TWR), relative clock skew.

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“…In the range-based localization algorithms, a small errors deviating from the true value in time can result in a major error in location. Thus, the clock model we used is the affine clock model [37]. The display time of node i is:…”

Section: A Clock Modelmentioning

confidence: 99%

“…T i (t) is the local time of node i. (1 + α i ) and β i represent the clock skew and the starting clock offset, respectively, [37] indicates that the affine clock model is commonly used. It can be seen in [38] that the affine clock model is superior to other clock models (e.g., clock-offset model and discrete-valued clock model) when implementing a range-based algorithm (aimed at localization).…”

Section: A Clock Modelmentioning

confidence: 99%

A Hybrid Localization Algorithm Based on TOF and TDOA for Asynchronous Wireless Sensor Networks

et al. 2019

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Accuracy and energy consumption are two crucial assessment standards for localization systems. The positioning algorithm used in a single technique may not balance the accuracy and power problem due to its limitations. Moreover, the performance of the existing combined localization method is unsatisfactory. In this paper, taking the relative clock skew into account, we investigate the biased time-of-flight (TOF) compensation problem in the symmetric double-sided two-way ranging (SDS-TWR) method. We first estimate the relative clock skew among sensor nodes to improve the accuracy of the ranging result. We then combine with the time-difference-of-arrival (TDOA) algorithm and reduce the number of transmissions and receptions on the tag (a node that needs to be located) side. Finally, the tag location is determined by Newton's iteration method. A simulation is implemented to validate our theoretical analysis and the results show that our proposed hybrid localization algorithm improves the locating accuracy significantly, compared with that of the C-TDOA method. Furthermore, the proposed hybrid localization algorithm can overcome the shortcoming of high power cost in the conventional TOF-based algorithm. INDEX TERMS Wireless sensor network, hybrid localization algorithm, time-difference-of-arrival (TDOA), time-of-flight (TOF), clock skew estimation.

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Synchronization and Localization in Wireless Networks

Cited by 19 publications

References 34 publications

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

A Compensated Multi-Anchors TOF-Based Localization Algorithm for Asynchronous Wireless Sensor Networks

A Hybrid Localization Algorithm Based on TOF and TDOA for Asynchronous Wireless Sensor Networks

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