A Survey on Compressed Sensing in Vehicular Infotainment Systems

IEEE Communications Surveys and Tutorials

Volumn 19, Issue 4, 2017, Pages 2662-2680

  Guo, Jie ^a   Song, Bin ^a   He, Ying ^b,c   Yu, Fei Richard ^b   Sookhak, Mehdi ^b  

^a XIDIAN UNIVERSITY   (China)

^b CARLETON UNIVERSITY   (Canada)

^c DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Compressed sensing; Vehicular infotainment systems

Indexed keywords

OBJECT RECOGNITION; SURVEYS;

COMPRESSIVE SENSING; DRIVING SAFETY; INFOTAINMENT SYSTEMS; OBJECT TRACKING; PRIVACY PROTECTION; RESEARCH CHALLENGES; ROAD TRAFFIC; VEHICULAR COMMUNICATIONS;

COMPRESSED SENSING;

EID: 85028991103     PISSN: None     EISSN: 1553877X     Source Type: Journal    
DOI: 10.1109/COMST.2017.2705027     Document Type: Review

References (121)

1. S. Reñe et al., "Vespa: Emulating infotainment applications in vehicular networks, " IEEE Pervasive Comput., vol. 13, no. 3, pp. 58-66, Jul./Sep. 2014.
2. W. Na, N.-N. Dao, and S. Cho, "Mitigating WiFi interference to improve throughput for in-vehicle infotainment networks, " IEEE Wireless Commun., vol. 23, no. 1, pp. 22-28, Feb. 2016.
3. H. Zhu et al., "ZOOM: Scaling the mobility for fast opportunistic forwarding in vehicular networks, " in Proc. IEEE INFOCOM, vol. 12. Turin, Italy, 2013, pp. 2832-2840.
4. X. Luan, J. Wu, S. Ren, and H. Xiang, "Compressed multi-access for MIMO-based vehicle communications network, " in Proc. 16th Int. Conf. Adv. Commun. Technol. (ICACT), 2014, pp. 966-970.
5. K. Ota, M. Dong, S. Chang, and H. Zhu, "MMCD: Cooperative down-loading for highway VANETs, " IEEE Trans. Emerg. Topics Comput., vol. 3, no. 1, pp. 34-43, Mar. 2015.
6. Y. Zhu, Z. Li, H. Zhu, M. Li, and Q. Zhang, "A compressive sensing approach to urban traffic estimation with probe vehicles, " IEEE Trans. Mobile Comput., vol. 12, no. 11, pp. 2289-2302, Nov. 2013.
7. D. L. Donoho, "Compressed sensing, " IEEE Trans. Inf. Theory, vol. 52, no. 4, pp. 1289-1306, Apr. 2006.
8. Z. Li, F. R. Yu, and M. Huang, "A distributed consensus-based cooper-ative spectrum-sensing scheme in cognitive radios, " IEEE Trans. Veh. Technol., vol. 59, no. 1, pp. 383-393, Jan. 2010.
9. S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, "Compressive sensing: From theory to applications, a survey, " J. Commun. Netw., vol. 15, no. 5, pp. 443-456, Oct. 2013.
10. M. Whaiduzzaman, M. Sookhak, A. Gani, and R. Buyya, "A sur-vey on vehicular cloud computing, " J. Netw. Comput. Appl., vol. 40, pp. 325-344, Apr. 2014.
11. S. Abdelhamid, H. S. Hassanein, and G. Takahara, "Vehicle as a resource (VaaR), " IEEE Netw., vol. 29, no. 1, pp. 12-17, Jan./Feb. 2015.
12. M. Gerla and L. Kleinrock, "Vehicular networks and the future of the mobile Internet, " Comput. Netw., vol. 55, no. 2, pp. 457-469, 2011.
13. S. Chen, J. Hu, Y. Shi, and L. Zhao, "LTE-V: A TD-LTE-based V2X solution for future vehicular network, " IEEE Internet Things J., vol. 3, no. 6, pp. 997-1005, Dec. 2016.
14. L. Ma, F. Yu, V. C. M. Leung, and T. Randhawa, "A new method to support UMTS/WLAN vertical handover using SCTP, " IEEE Wireless Commun., vol. 11, no. 4, pp. 44-51, Aug. 2004.
15. F. Yu and V. Krishnamurthy, "Optimal joint session admission control in integrated WLAN and CDMA cellular networks with vertical handoff, " IEEE Trans. Mobile Comput., vol. 6, no. 1, pp. 126-139, Jan. 2007.
16. L. Ma, F. R. Yu, and V. C. M. Leung, "Performance improvements of mobile SCTP in integrated heterogeneous wireless networks, " IEEE Trans. Wireless Commun., vol. 6, no. 10, pp. 3567-3577, Oct. 2007.
17. E. J. Candès, J. K. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements, " Commun. Pure Appl. Math., vol. 59, no. 8, pp. 1207-1223, 2006.
18. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit, " SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61, 1998.
19. H. Huang, S. Misra, W. Tang, H. Barani, and H. Al-Azzawi, "Applications of compressed sensing in communications networks, " Eprint Arxiv, Feb. 2014.
20. K. Engan, S. O. Aase, and J. H. Husoy, "Method of optimal directions for frame design, " in Proc. IEEE Int. Conf. Acoust. Speech Signal Process., vol. 5. Phoenix, AZ, USA, 1999, pp. 2443-2446.
21. J. Mairal, F. Bach, J. Ponce, and G. Sapiro, "Online dictionary learning for sparse coding, " in Proc. 26th Annu. Int. Conf. Mach. Learn., Montreal, QC, Canada, 2009, pp. 689-696.
22. A. C. Gilbert, M. J. Strauss, J. A. Tropp, and R. Vershynin, "One sketch for all: Fast algorithms for compressed sensing, " in Proc. 39th Annu. ACM Symp. Theory Comput., San Diego, CA, USA, 2007, pp. 237-246.
23. G. Cormode and M. Hadjieleftheriou, "Finding the frequent items in streams of data, " Commun. ACM, vol. 52, no. 10, pp. 97-105, 2009.
24. J. A. Tropp and A. C. Gilbert, "Signal recovery from random measurements via orthogonal matching pursuit, " IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655-4666, Dec. 2007.
25. D. L. Donoho, Y. Tsaig, I. Drori, and J.-L. Starck, "Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit, " IEEE Trans. Inf. Theory, vol. 58, no. 2, pp. 1094-1121, Feb. 2012.
26. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, "Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems, " IEEE J. Sel. Topics Signal Process., vol. 1, no. 4, pp. 586-597, Dec. 2007.
27. M. F. Duarte and Y. C. Eldar, "Structured compressed sensing: From theory to applications, " IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4053-4085, Sep. 2011.
28. M. L. Sichitiu and M. Kihl, "Inter-vehicle communication systems: A survey, " IEEE Commun. Surveys Tuts., vol. 10, no. 2, pp. 88-105, 2nd Quart., 2008.
29. S. Sivaraman and M. M. Trivedi, "Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior anal-ysis, " IEEE Trans. Intell. Transp. Syst., vol. 14, no. 4, pp. 1773-1795, Dec. 2013.
30. U. Lee and M. Gerla, "A survey of urban vehicular sensing platforms, " Comput. Netw., vol. 54, no. 4, pp. 527-544, 2010.
31. W. Alasmary and S. Valaee, "Compressive sensing based vehicle information recovery in vehicular networks, " in Proc. IWCMC, 2013, pp. 700-705.
32. H. Li, P. Bai, and H. Song, "Car tracking algorithm based on Kalman filter and compressive tracking, " in Proc. 7th Int. Conf. Image Signal Process. (CISP), Dalian, China, 2014, pp. 27-31.
33. K. Zhang, L. Zhang, and M.-H. Yang, "Real-time compressive tracking, " in Computer Vision-ECCV. Heidelberg, Germany: Springer, 2012, pp. 864-877.
34. Y. Gao, H. Zhou, and X. Zhang, "Enhanced fast compressive tracking based on adaptive measurement matrix, " IET Comput. Vis., vol. 9, no. 6, pp. 857-863, Dec. 2015.
35. X. Mei and H. Ling, "Robust visual tracking and vehicle classification via sparse representation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 11, pp. 2259-2272, Nov. 2011.
36. H. Li, C. Shen, and Q. Shi, "Real-time visual tracking using compressive sensing, " in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Colorado Springs, CO, USA, 2011, pp. 1305-1312.
37. T. Bianchi, V. Bioglio, and E. Magli, "Analysis of one-time random projections for privacy preserving compressed sensing, " IEEE Trans. Inf. Forensics Security, vol. 11, no. 2, pp. 313-327, Feb. 2016.
38. A. Orsdemir, H. O. Altun, G. Sharma, and M. F. Bocko, "On the security and robustness of encryption via compressed sensing, " in Proc. IEEE Milcom, San Diego, CA, USA, 2008, pp. 1-7.
39. Y. Rachlin and D. Baron, "The secrecy of compressed sensing measurements, " in Proc. Allerton Conf. Commun. Control Comput., Monticello, IL, USA, 2008, pp. 813-817.
40. V. Cambareri et al., "A two-class information concealing system based on compressed sensing, " in Proc. IEEE Int. Symp. Circuits Syst. (ISCAS), Beijing, China, 2013, pp. 1356-1359.
41. V. Cambareri, M. Mangia, F. Pareschi, R. Rovatti, and G. Setti, "Low-complexity multiclass encryption by compressed sensing, " IEEE Trans. Signal Process., vol. 63, no. 9, pp. 2183-2195, May 2015.
42. L. Xu, X. Hao, N. D. Lane, X. Liu, and T. Moscibroda, "More with less: Lowering user burden in mobile crowdsourcing through compressive sensing, " in Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Osaka, Japan, 2015, pp. 659-670.
43. C. Luo, F. Wu, J. Sun, and C. W. Chen, "Compressive data gathering for large-scale wireless sensor networks, " in Proc. 15th Annu. Int. Conf. Mobile Comput. Netw., Beijing, China, 2009, pp. 145-156.
44. X. Yu et al., "Cooperative sensing and compression in vehicular sensor networks for urban monitoring, " in Proc. IEEE Int. Conf. Commun. (ICC), Cape Town, South Africa, 2010, pp. 1-5.
45. F. Yu and V. C. M. Leung, "Mobility-based predictive call admission control and bandwidth reservation in wireless cellular networks, " in Proc. IEEE INFOCOM, Anchorage, AK, USA, Apr. 2001, pp. 518-526.
46. Y. He et al., "Big data analytics in mobile cellular networks, " IEEE Access, vol. 4, pp. 1985-1996, Mar. 2016.
47. X. Yu et al., "Efficient sampling and compressive sensing for urban monitoring vehicular sensor networks, " IET Wireless Sensor Syst., vol. 2, no. 3, pp. 214-221, Sep. 2012.
48. L. Xu, X. Hao, N. D. Lane, X. Liu, and T. Moscibroda, "Cost-aware compressive sensing for networked sensing systems, " in Proc. 14th Int. Conf. Inf. Process. Sensor Netw., Seattle, WA, USA, 2015, pp. 130-141.
49. S. Liu, F. Yang, W. Ding, and J. Song, "Structured compressive sensing based narrowband interference mitigation for vehicular com-munications, " in Proc. IEEE ICC Workshops, London, U.K., 2015, pp. 2375-2380.
50. S. Liu, F. Yang, W. Ding, and J. Song, "Double kill: Compressive-sensing-based narrow-band interference and impulsive noise mitigation for vehicular communications, " IEEE Trans. Veh. Technol., vol. 65, no. 7, pp. 5099-5109, Jul. 2016.
51. M. Kaneko and K. A. Agha, "Compressed sensing based protocol for efficient reconstruction of sparse superimposed data in a multi-hop wireless sensor network, " Eprint Arxiv, Aug. 2012.
52. S. Bu, F. R. Yu, Y. Cai, and P. Liu, "When the smart grid meets energy-efficient communications: Green wireless cellular networks powered by the smart grid, " IEEE Trans. Wireless Commun., vol. 11, no. 8, pp. 3014-3024, Aug. 2012.
53. R. Xie, F. R. Yu, H. Ji, and Y. Li, "Energy-efficient resource allocation for heterogeneous cognitive radio networks with femtocells, " IEEE Trans. Wireless Commun., vol. 11, no. 11, pp. 3910-3920, Nov. 2012.
54. F. R. Yu, P. Zhang, W. Xiao, and P. Choudhury, "Communication systems for grid integration of renewable energy resources, " IEEE Netw., vol. 25, no. 5, pp. 22-29, Sep./Oct. 2011.
55. C.-L. Huang, Y. P. Fallah, R. Sengupta, and H. Krishnan, "Adaptive intervehicle communication control for cooperative safety systems, " IEEE Netw., vol. 24, no. 1, pp. 6-13, Jan./Feb. 2010.
56. Z. Li, Y. Zhu, H. Zhu, and M. Li, "Compressive sensing approach to urban traffic sensing, " in Proc. 31st Int. Conf. Distrib. Comput. Syst. (ICDCS), Minneapolis, MN, USA, 2011, pp. 889-898.
57. T. T. Cai, L. Wang, and G. Xu, "Shifting inequality and recov-ery of sparse signals, " IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1300-1308, Mar. 2010.
58. V. Meena and G. Abhilash, "Robust recovery algorithm for compressed sensing in the presence of noise, " IET Signal Process., vol. 10, no. 3, pp. 227-236, May 2016.
59. H. Liu, B. Song, F. Tian, and H. Qin, "Joint sampling rate and bit-depth optimization in compressive video sampling, " IEEE Trans. Multimedia, vol. 16, no. 6, pp. 1549-1562, Oct. 2014.
60. H. Zheng, S. Xiao, X. Wang, X. Tian, and M. Guizani, "Capacity and delay analysis for data gathering with compressive sensing in wireless sensor networks, " IEEE Trans. Wireless Commun., vol. 12, no. 2, pp. 917-927, Feb. 2013.
61. S. Pudlewski, A. Prasanna, and T. Melodia, "Compressed-sensing-enabled video streaming for wireless multimedia sensor networks, " IEEE Trans. Mobile Comput., vol. 11, no. 6, pp. 1060-1072, Jun. 2012.
62. N. Cen, Z. Guan, and T. Melodia, "Multi-view wireless video streaming based on compressed sensing: Architecture and network optimization, " in Proc. 16th ACM Int. Symp. Mobile Ad Hoc Netw. Comput., Hangzhou, China, 2015, pp. 137-146.
63. N. M. Freris, O. Oçal, and M. Vetterli, "Compressed sensing of streaming data, " in Proc. 51st Annu. Allerton Conf. Commun. Control Comput., Monticello, IL, USA, 2013, pp. 1242-1249.
64. V. Cevher et al., "Compressive sensing for background subtraction, " in Proc. Comput. Vis. (ECCV), Marseille, France, 2008, pp. 155-168.
65. X. Zhang, Z. Dong, and C. Crawford, "A novel method for driving condition recognition based on compressed sensing, " in Proc. Int. Design Eng. Tech. Conf. Comput. Inf. Eng. Conf., 2013, Art. no. V001T01A014.
66. D. A. Ross, J. Lim, R.-S. Lin, and M.-S. Yang, "Incremental learn-ing for robust visual tracking, " Int. J. Comput. Vis., vol. 77, nos. 1-3, pp. 125-141, 2008.
67. H. Grabner, M. Grabner, and H. Bischof, "Real-time tracking via on-line boosting, " in Proc. Brit. Mach. Vis. Conf. (BMVC), vol. 1. Edinburgh, U.K., 2006, pp. 47-56.
68. R. T. Collins, Y. Liu, and M. Leordeanu, "Online selection of discriminative tracking features, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 10, pp. 1631-1643, Oct. 2005.
69. B. Babenko, M. Yang, and S. Belongie, "Robust object tracking with online multiple instance learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1619-1632, Aug. 2011.
70. A. M. Abdulghani and E. Rodriguez-Villegas, "Compressive sensing: From 'compressing while sampling' to 'compressing and securing while sampling, "' in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), Buenos Aires, Argentina, 2010, pp. 1127-1130.
71. D. Takhar et al., "A new compressive imaging camera architecture using optical-domain compression, " in Proc. Int. Soc. Opt. Photon. Electron. Imag., San Jose, CA, USA, 2006, Art. no. 606509.
72. S. Consolvo et al., "Activity sensing in the wild: A field trial of ubi-fit garden, " in Proc. SIGCHI Conf. Human Factors Comput. Syst., Florence, Italy, 2008, pp. 1797-1806.
73. L. M. Rea and R. A. Parker, Designing and Conducting Survey Research: A Comprehensive Guide. New York, NY, USA: Wiley, 2014.
74. M. Matthaiou, D. I. Laurenson, and C.-X. Wang, "Capacity study of vehicle-to-roadside MIMO channels with a line-of-sight component, " in Proc. IEEE WCNC, Las Vegas, NV, USA, 2008, pp. 775-779.
75. M. Bramberger, J. Brunner, B. Rinner, and H. Schwabach, "Real-time video analysis on an embedded smart camera for traffic surveillance, " in Proc. IEEE Real Time Embedded Technol. Appl. Symp., Toronto, ON, Canada, 2004, pp. 174-181.
76. G. J. Sullivan, J. R. Ohm, W.-J. Han, and T. Wiegand, "Overview of the high efficiency video coding (HEVC) standard, " IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649-1668, Dec. 2012.
77. L. Wang, T. Liu, G. Wang, K. L. Chan, and Q. Yang, "Video tracking using learned hierarchical features, " IEEE Trans. Image Process., vol. 24, no. 4, pp. 1424-1435, Apr. 2015.
78. J. Ding, Y. Huang, W. Liu, and K. Huang, "Severely blurred object tracking by learning deep image representations, " IEEE Trans. Circuits Syst. Video Technol., vol. 26, no. 2, pp. 319-331, Feb. 2016.
79. J. Liu, F. R. Yu, C.-H. Lung, and H. Tang, "Optimal combined intrusion detection and biometric-based continuous authentication in high security mobile ad hoc networks, " IEEE Trans. Wireless Commun., vol. 8, no. 2, pp. 806-815, Feb. 2009.
80. A. Attar, H. Tang, A. Vasilakos, F. R. Yu, and V. C. M. Leung, "A survey of security challenges in cognitive radio networks: Solutions and future research directions, " Proc. IEEE, vol. 100, no. 12, pp. 3172-3186, Dec. 2012.
81. N. Lathia, K. K. Rachuri, C. Mascolo, and P. J. Rentfrow, "Contextual dissonance: Design bias in sensor-based experience sampling methods, " in Proc. ACM Int. Joint Conf. Pervasive Ubiquitous Comput., Zürich, Switzerland, 2013, pp. 183-192.
82. R. LiKamWa, Y. Liu, N. D. Lane, and L. Zhong, "Moodscope: Building a mood sensor from smartphone usage patterns, " in Proc. 11th Annu. Int. Conf. Mobile Syst. Appl. Services, Taipei, Taiwan, 2013, pp. 389-402.
83. I. Singh, M. Butkiewicz, H. V. Madhyastha, S. V. Krishnamurthy, and S. Addepalli, "Twitsper: Tweeting privately, " IEEE Security Privacy, vol. 11, no. 3, pp. 46-50, May/Jun. 2013.
84. M. Xia, L. Gong, Y. Lyu, Z. Qi, and X. Liu, "Effective real-time android application auditing, " in Proc. IEEE Symp. Security Privacy (SP), San Jose, CA, USA, 2015, pp. 899-914.
85. C. Y. T. Ma, D. K. Y. Yau, N. K. Yip, and N. S. V. Rao, "Privacy vulnerability of published anonymous mobility traces, " IEEE/ACM Trans. Netw., vol. 21, no. 3, pp. 720-733, Jun. 2013.
86. G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.-L. Tan, "Private queries in location based services: Anonymizers are not necessary, " in Proc. ACM SIGMOD Int. Conf. Manag. Data, Vancouver, BC, Canada, 2008, pp. 121-132.
87. H. Zang and J. Bolot, "Anonymization of location data does not work: A large-scale measurement study, " in Proc. 17th Annu. Int. Conf. Mobile Comput. Netw., Las Vegas, NV, USA, 2011, pp. 145-156.
88. H. Kido, Y. Yanagisawa, and T. Satoh, "An anonymous communication technique using dummies for location-based services, " in Proc. Int. Conf. Pervasive Services, 2005, pp. 88-97.
89. M. Gruteser and D. Grunwald, "Anonymous usage of location-based services through spatial and temporal cloaking, " in Proc. 1st Int. Conf. Mobile Syst. Appl. Services, San Francisco, CA, USA, 2003, pp. 31-42.
90. B. Hoh and M. Gruteser, "Protecting location privacy through path confusion, " in Proc. 1st Int. Conf. Security Privacy Emerg. Areas Commun. Netw., Athens, Greece, 2005, pp. 194-205.
91. D. Quercia, I. Leontiadis, L. McNamara, C. Mascolo, and J. Crowcroft, "Spotme if you can: Randomized responses for location obfusca-tion on mobile phones, " in Proc. 31st Int. Conf. Distrib. Comput. Syst. (ICDCS), Minneapolis, MN, USA, 2011, pp. 363-372.
92. R. R. Coifman and M. Maggioni, "Diffusion wavelets, " Appl. Comput. Harmonic Anal., vol. 21, no. 1, pp. 53-94, 2006.
93. B. Coifman, "Estimating travel times and vehicle trajectories on freeways using dual loop detectors, " Transp. Res. A Policy Pract., vol. 36, no. 4, pp. 351-364, 2002.
94. D. B. Work, S. Blandin, O.-P. Tossavainen, B. Piccoli, and A. M. Bayen, "A traffic model for velocity data assimilation, " Appl. Math. Res. Exp., vol. 2010, no. 1, pp. 1-35, 2010.
95. J. C. Herrera and A. M. Bayen, "Traffic flow reconstruction using mobile sensors and loop detector data, " in Proc. 85th TRB Annu. Meeting, 2007.
96. A. I. Bejan and R. J. Gibbens, "Evaluation of velocity fields via sparse bus probe data in urban areas, " in Proc. IEEE Int. Conf. Intell. Transp. Syst. (ITSC), Washington, DC, USA, 2011, pp. 746-753.
97. M. A. Ferman, D. E. Blumenfeld, and X. Dai, "An analytical evaluation of a real-time traffic information system using probe vehicles, " J. Intell. Transp. Syst., vol. 9, no. 1, pp. 23-34, 2005.
98. H. J. van Zuylen, F. Zheng, and Y. Chen, "Using probe vehicle data for traffic state estimation in signalized urban networks, " in Traffic Data Collection and Its Standardization. New York, NY, USA: Springer, 2010, pp. 109-127.
99. Q.-J. Kong, Z. Li, Y. Chen, and Y. Liu, "An approach to urban traffic state estimation by fusing multisource information, " IEEE Trans. Intell. Transp. Syst., vol. 10, no. 3, pp. 499-511, Sep. 2009.
100. J. Yuan, Y. Zheng, X. Xie, and G. Sun, "Driving with knowledge from the physical world, " in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Disc. Data Min., San Diego, CA, USA, 2011, pp. 316-324.
101. R. Herring, A. Hofleitner, P. Abbeel, and A. Bayen, "Estimating arterial traffic conditions using sparse probe data, " in Proc. 13th Int. IEEE Conf. Intell. Transp. Syst. (ITSC), Funchal, Portugal, 2010, pp. 929-936.
102. U. Lee et al., "Mobeyes: Smart mobs for urban monitoring with a vehicular sensor network, " IEEE Wireless Commun., vol. 13, no. 5, pp. 52-57, Oct. 2006.
103. R. K. Balan, K. X. Nguyen, and L. Jiang, "Real-time trip information service for a large taxi fleet, " in Proc. 9th Int. Conf. Mobile Syst. Appl. Services, Bethesda, MD, USA, 2011, pp. 99-112.
104. B. Hoh et al., "Virtual trip lines for distributed privacy-preserving traffic monitoring, " in Proc. 6th Int. Conf. Mobile Syst. Appl. Services, Breckenridge, CO, USA, 2008, pp. 15-28.
105. B. Hoh, M. Gruteser, H. Xiong, and A. Alrabady, "Enhancing security and privacy in traffic-monitoring systems, " IEEE Pervasive Comput., vol. 5, no. 4, pp. 38-46, Oct./Dec. 2006.
106. T. B. Petros and M. S. Asif, "Compressive sensing for streaming signals using the streaming greedy pursuit, " in Proc. IEEE Milcom, 2010, pp. 1205-1210.
107. E. J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
108. M. Rabbat, J. Haupt, A. Singh, and R. Nowak, "Decentralized compression and predistribution via randomized gossiping, " in Proc. 5th Int. Conf. Inf. Process. Sensor Netw., Nashville, TN, USA, 2006, pp. 51-59.
109. S. Lee, S. Pattem, M. Sathiamoorthy, B. Krishnamachari, and A. Ortega, "Spatially-localized compressed sensing and routing in multi-hop sensor networks, " in Proc. Int. Conf. GeoSensor Netw., Oxford, U.K., 2009, pp. 11-20.
110. L. Kong et al., "Privacy-preserving compressive sensing for crowdsens-ing based trajectory recovery, " in Proc. IEEE 35th Int. Conf. Distrib. Comput. Syst. (ICDCS), Columbus, OH, USA, 2015, pp. 31-40.
111. X. Wu, P. Yang, S. Tang, X. Zheng, and Y. Xiong, "Privacy preserving RSS map generation for a crowdsensing network, " IEEE Wireless Commun., vol. 22, no. 4, pp. 42-48, Aug. 2015.
112. E. Van Den Berg and M. P. Friedlander, "Theoretical and empirical results for recovery from multiple measurements, " IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2516-2527, May 2010.
113. T. T. Do, L. Gan, N. Nguyen, and T. D. Tran, "Sparsity adaptive matching pursuit algorithm for practical compressed sensing, " in Proc. 42th Asilomar Conf. Signals Syst. Comput., Pacific Grove, CA, USA, 2008, pp. 581-587.
114. H. W. Chen, L. W. Kang, and C. S. Lu, "Dynamic measurement rate allocation for distributed compressive video sensing, " in Proc. Visual Commun. Image Process., 2010, Art. no. 77440I.
115. S. Patterson, Y. C. Eldar, and I. Keidar, "Distributed compressed sensing for static and time-varying networks, " IEEE Trans. Signal Process., vol. 62, no. 19, pp. 4931-4946, Oct. 2014.
116. H. Liu, B. Song, F. Tian, H. Qin, and X. Liu, "Optimal-correlation-based reconstruction for distributed compressed video sensing, " J. Vis. Commun. Image Represent., vol. 31, pp. 197-207, Aug. 2015.
117. Compressed Sensing: The Big Picture. Accessed on Jun. 18, 2012. [Online]. Available: https://sites. google.com/site/igorcarron2
118. T. Strohmer, "Measure what should be measured: Progress and challenges in compressive sensing, " IEEE Signal Process. Lett., vol. 19, no. 12, pp. 887-893, Dec. 2012.
119. Y. Chi, L. L. Scharf, A. Pezeshki, and A. R. Calderbank, "Sensitivity to basis mismatch in compressed sensing, " IEEE Trans. Signal Process., vol. 59, no. 5, pp. 2182-2195, May 2011.
120. S. Ji, D. Dunson, and L. Carin, "Multitask compressive sensing, " IEEE Trans. Signal Process., vol. 57, no. 1, pp. 92-106, Jan. 2009.
121. J. Mairal, F. Bach, and J. Ponce, "Task-driven dictionary learning, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 791-804, Apr. 2012.