research-article

Steering Crowdsourced Signal Map Construction via Bayesian Compressive Sensing

Authors:

Kang G. ShinAuthors Info & Claims

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

Pages 1016 - 1024

https://doi.org/10.1109/INFOCOM.2018.8485972

Published: 16 April 2018 Publication History

Abstract

Mobile crowdsensing with increasing pervasiveness of smartphones has enabled a myriad of applications, including urban-scale signal map monitoring and revision. Despite the importance of its quality, due to the large size of a site to cover, dense crowdsourcing is neither cost-effective nor convenient for crowdsourcing participants, making it critical and challenging to balance between signal quality and crowdsourcing cost. To address this problem, we propose a novel incentive mechanism, BCCS, based on Bayesian Compressive Crowdsensing (BCS). BCCS iteratively determines the spatial grids for crowd-sourcing quality and predicts the remaining unexplored grids for deployment efficiency. BCS returns not only the predicted signal values, but also the confidence intervals for convergence and incentive control. A probabilistic user participation and measurement model is applied for incentive design, which is flexible for crowdsensing deployment. Our extensive evaluation based on two different data sets shows that BCCS achieves much higher prediction accuracy (often by more than 20%) with lower payments to the participants and fewer iterations (often by 30%) than existing solutions.

References

[1]

X. Zhou, Z. Zhang et al., “Practical conflict graphs in the wild,” IEEE/ACM TON, vol. 23, no. 3, pp. 824–835, June 2015.

Digital Library

[2]

Z. Li, A. Nika et al., “Identifying value in crowdsourced wireless signal measurements,” in Proc. WWW, 2017, pp. 607–616.

[3]

D. Yang, G. Xue et al., “Crowdsourcing to smartphones: Incentive mechanism design for mobile phone sensing,” in Proc. ACM MobiCom, 2012, pp. 173–184.

[4]

S. Ji, Y. Xue et al., “Bayesian compressive sensing,” IEEE TSP, vol. 56, no. 6, pp. 2346–2356, June 2008.

[5]

J. Wang, N. Tan et al., “WOLoc: WiFi-only outdoor localization using crowdsensed hotspot labels,” in Proc. IEEE INFOCOM, 2017.

[6]

B. Zhou, M. Elbadry et al., “BatMapper: Acoustic sensing based indoor floor plan construction using smartphones,” in Proc. ACM MobiSys, 2017, pp. 42–55.

[7]

F. Restuccia, S. K. Das et al., “Incentive mechanisms for participatory sensing: Survey and research challenges,” ACM TOSN, vol. 12, no. 2, pp. 13:1–13:40, Apr. 2016.

[8]

S. Liu, Z. Zheng et al., “Context-aware data quality estimation in mobile crowdsensing,” in Proc. IEEE INFOCOM, 2017.

[9]

D. Yang, G. Xue et al., “Incentive mechanisms for crowdsensing: Crowdsourcing with smartphones,” IEEE/ACM TON, vol. 24, no. 3, pp. 1732–1744, June 2016.

Digital Library

[10]

M. H. Cheung, F. Hou et al., “Make a difference: Diversity-driven social mobile crowdsensing,” in Proc. IEEE INFOCOM, 2017.

[11]

M. Tang, S. Wang et al., “MOMD: A multi-object multi-dimensional auction for crowdsourced mobile video streaming,” in Proc. IEEE INFOCOM, 2017.

[12]

H. Jin, L. Su et al., “CENTURION: Incentivizing multi-requester mobile crowd sensing,” in Proc. IEEE INFOCOM, 2017.

[13]

R. Kawajiri, M. Shimosaka et al., “Steered crowdsensing: Incentive design towards quality-oriented place-centric crowdsensing,” in Proc. ACM UbiComp, 2014, pp. 691–701.

[14]

Y. Zhao, W. Li et al., “Quantized conflict graphs for wireless network optimization,” in Proc. IEEE INFOCOM, 2015, pp. 2218–2226.

[15]

B. Yang, S. He et al., “Updating wireless signal map with Bayesian compressive sensing,” in Proc. ACM MSWiM, 2016, pp. 310–317.

[16]

C. Wu, Z. Yang et al., “Static power of mobile devices: Self-updating radio maps for wireless indoor localization,” in Proc. IEEE INFOCOM, 2015, pp. 2497–2505.

[17]

S. He, W. Lin et al., “Indoor localization and automatic fingerprint update with altered AP signals,” IEEE TMC, vol. 16, no. 7, pp. 1897–1910, July 2017.

[18]

C. Feng, W. S. A. Au et al., “Received-signal-strength-based indoor positioning using compressive sensing,” IEEE TMC, vol. 11, no. 12, pp. 1983–1993, Dec 2012.

[19]

Y. Kim, Y. Chon et al., “Mobile crowdsensing framework for a large-scale Wi-Fi fingerprinting system,” IEEE Pervasive Computing, vol. 15, no. 3, pp. 58–67, July 2016.

Digital Library

[20]

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theor., vol. 52, no. 4, pp. 1289–1306, Apr. 2006.

Digital Library

[21]

L. Wang, D. Zhang et al., “CCS-TA: Quality-guaranteed online task allocation in compressive crowdsensing,” in Proc. ACM UbiComp, 2015, pp. 683–694.

[22]

L. Xu, X. Hao et al., “More with less: Lowering user burden in mobile crowdsourcing through compressive sensing,” in Proc. ACM UbiComp, 2015, pp. 659–670.

[23]

X. Hao, L. Xu et al., “Density-aware compressive crowdsensing,” in Proc. ACM/IEEE IPSN, 2017, pp. 29–39.

[24]

T. Liu, Y. Zhu et al., “Incentive design for air pollution monitoring based on compressive crowdsensing,” in Proc. IEEE GLOBECOM, 2016, pp. 1–6.

[25]

R. K. Rana, C. T. Chou et al., “Ear-phone: An end-to-end participatory urban noise mapping system,” in Proc. ACM/IEEE IPSN, 2010, pp. 105–116.

[26]

L. Wang, D. Zhang et al., “Sparse mobile crowdsensing: Challenges and opportunities,” IEEE Commun. Mag., vol. 54, no. 7, pp. 161–167, July 2016.

Digital Library

[27]

F. Yin and F. Gunnarsson, “Distributed recursive Gaussian Processes for RSS map applied to target tracking,” IEEE J-STSP, vol. 11, no. 3, pp. 492–503, April 2017.

[28]

H. C. Yen and C. C. Wang, “Cross-device Wi-Fi map fusion with Gaussian processes,” IEEE TMC, vol. 16, no. 1, pp. 44–57, Jan. 2017.

[29]

S. Nikitaki, G. Tsagkatakis et al., “Efficient multi-channel signal strength based localization via matrix completion and Bayesian sparse learning,” IEEE TMC, vol. 14, no. 11, pp. 2244–2256, Nov. 2015.

[30]

M. Roughan, Y. Zhang et al., “Spatio-temporal compressive sensing and internet traffic matrices (extended version),” IEEE/ACM TON, vol. 20, no. 3, pp. 662–676, June 2012.

Digital Library

[31]

Y. Zhu, Z. Li et al., “A compressive sensing approach to urban traffic estimation with probe vehicles,” IEEE TMC, vol. 12, no. 11, pp. 2289–2302, Nov. 2013.

[32]

L. Kong, M. Xia et al., “Data loss and reconstruction in sensor networks,” in Proc. IEEE INFOCOM, April 2013, pp. 1654–1662.

[33]

J. Wang, S. Tang et al., “Data gathering in wireless sensor networks through intelligent compressive sensing,” in Proc. IEEE INFOCOM, 2012, pp. 603–611.

[34]

C. Meng, H. Xiao et al., “Tackling the redundancy and sparsity in crowd sensing applications,” in Proc. ACM SenSys, 2016, pp. 150–163.

[35]

M. E. Tipping, “Sparse Bayesian learning and the relevance vector machine,” J. Mach. Learn. Res., vol. 1, pp. 211–244, Sep. 2001.

Digital Library

[36]

P. Micholia, M. Karaliopoulos et al., “Mobile crowdsensing incentives under participation uncertainty,” in Proc. ACM MSCC, 2016, pp. 29–34.

[37]

R. Kohavi et al., “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Proc. IJCAI, vol. 14, no. 2, 1995, pp. 1137–1145.

[38]

S. Li, X. Tao et al., “A variational Bayesian EM approach to structured sparse signal reconstruction,” in Proc. IEEE VTC-Fall, Sept. 2014, pp. 1–5.

[39]

C. Phillips, R. Senior et al., “Robust coverage and performance testing for large area networks,” in Springer AccessNets, 2008.

[40]

J. Torres-Sospedra, R. Montoliu et al., “UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems,” in Proc. IPIN, 2014, pp. 261–270.

Cited By

Thakur TMarchang N(2023)PAMDIJournal of Ambient Intelligence and Smart Environments10.3233/AIS-22047515:1(19-46)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/AIS-220475
Liang TChen LHuang MDeng XZhang SXiong NLiu A(2023)RLTDApplied Soft Computing10.1016/j.asoc.2023.110369143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110369
Wang HGuo BWang SHe TZhang D(2021)CSMCProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949595:4(1-22)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3494959

Index Terms

Steering Crowdsourced Signal Map Construction via Bayesian Compressive Sensing
1. Networks
  1. Network types
    1. Mobile networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Image compressive sensing via Truncated Schatten-p Norm regularization

Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in ...
Robust 1-bit compressive sensing via variational Bayesian algorithm

In a compressive sensing (CS) framework, a sparse signal can be stably reconstructed at a reduced sampling rate. Quantization and noise corruption are inevitable in practical applications. Recent studies have shown that using only the sign information ...
Compressive sensing via nonlocal low-rank tensor regularization

The aim of Compressing sensing (CS) is to acquire an original signal, when it is sampled at a lower rate than Nyquist rate previously. In the framework of CS, the original signal is often assumed to be sparse and correlated in some domain. Recently, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Guide Proceedings

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

Apr 2018

2776 pages

Copyright © 2018.

Publisher

IEEE Press

Publication History

Published: 16 April 2018

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 04 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Thakur TMarchang N(2023)PAMDIJournal of Ambient Intelligence and Smart Environments10.3233/AIS-22047515:1(19-46)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/AIS-220475
Liang TChen LHuang MDeng XZhang SXiong NLiu A(2023)RLTDApplied Soft Computing10.1016/j.asoc.2023.110369143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110369
Wang HGuo BWang SHe TZhang D(2021)CSMCProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949595:4(1-22)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3494959

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents