Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing

International Journal of Distributed Sensor Networks

Volumn 14, Issue 9, 2018, Pages

  Liu, Zhou Zhou ^a,b   Li, Shi Ning ^a  

^a XI'AN AERONAUTICAL UNIVERSITY   (China)

^b NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

compressive sensing; elastic collision optimization algorithm; fog computing; Sensor cloud; wireless sensor networks

Indexed keywords

COMPRESSED SENSING; DATA ACQUISITION; DATA HANDLING; ELASTIC SCATTERING; ENERGY UTILIZATION; FOG; GREEN COMPUTING; METADATA; MOTION PLANNING; NETWORK LAYERS; OPTIMIZATION; SENSOR NODES; WIRELESS SENSOR NETWORKS;

ACQUISITION STRATEGIES; BLOCK COMPRESSED SENSING; BLOCK COMPRESSIVE SENSING; COMPRESSIVE SENSING; CONSTRAINT TRANSFORMATION; INFORMATION TRANSFERS; LARGE-SCALE DATA PROCESSING; OPTIMIZATION ALGORITHMS;

FOG COMPUTING;

EID: 85054150216     PISSN: 15501329     EISSN: 15501477     Source Type: Journal    
DOI: 10.1177/1550147718802259     Document Type: Article

References (38)

1. Wang T, Zeng J, Lai Y. Data collection from WSNs to the cloud based on mobile fog elements. Future Gener Comp Sy. Epub ahead of print 27 July 2017. DOI: 10.1016/j.future.2017.07.031
2. Xuey Chang X, Zhong S. An efficient energy hole alleviating algorithm for wireless sensor networks. IEEE T Consum Electr 2014; 60(3): 347–355
3. Ebrahimi D, Assi C., Network coding-aware compressive data gathering for energy-efficient wireless sensor networks. ACM T Sensor Network 2015; 11(4): 1–24
4. Wang T, Zhang G, Bhuiyan ZA. A novel trust mechanism based on fog computing in sensor-cloud system. Future Gener Comp Sy. Epub ahead of print 26 June 2018. DOI: 10.1016/j.future.2018.05.049
5. Gu Y, Ren F, Ji Y. The evolution of sink mobility management in wireless sensor networks: a survey. IEEE Commun Surv Tut 2016; 18(3): 507–524
6. Zeng J, Wang T, Jia W. A survey on sensor-cloud. J Comput Res Dev 2017; 54(5): 925–939
7. Mell P, Grance T., The NIST definition of cloud computing (draft). NIST Spec Publ 2011; 800(145): 7
8. Dash SK, Sahoo JP, Mohapatra S. Sensor-cloud: assimilation of wireless sensor network and the cloud. In: Proceedings of the 5th international conference on computer science and information technology, Hong Kong, China, 29–30 December 2012, pp.455–464. Berlin: Springer
9. Misra S, Singh A, Chatterjee S. QoS-aware sensor allocation for target tracking in sensor-cloud. Ad Hoc Netw 2015; 33: 140–153
10. Tan KL., What’s NExT?: Sensor + Cloud!? In: Proceedings of the 7th ACM international workshop on data management for sensor network, Singapore, 13 September 2010. New York: ACM
11. Yuriyama M, Kushida T, Sensor—cloud infrastructure—physical sensor management with virtualized sensors on cloud computing. In: Proceedings of the 13th international conference on network-based information systems, Takayama, Japan, 14–16 September 2010, pp.1–8. New York: IEEE
12. Madria S, Kumar V, Dalvi R., Sensor-cloud: a cloud of virtual sensors. IEEE Software 2014; 31(2): 70–77
13. LogMeIn Inc. Xively by LogMeIn [EB/OL], http://xively.com
14. GitHub. Nimbit-promote yourself [EB/OL], http://nimibits.com
15. Qing YeeLink Information Technology Co, Ltd. YeeLink [EB/OL], http://yeelink.com
16. Bonomi F. Connected vehicles, the internet of things and fog computing. In: Proceedings of the eighth ACM international workshop on vehicular inter-networking, Las Vegas, 2 September 2011, pp.13–15. New York: ACM
17. Bonomi F, Milito R, Zhu J. Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing, Helsinki, 17 August 2012, pp.13–16. New York: ACM
18. Sarkar S, Misra S., Theoretical modeling of fog computing: a green computing paradigm to support Iot applications. IET Netw 2016; 5(2): 23–29
19. Zhu J, Chan DS, Prabhu MS. Improving web sites performance using edge servers in fog computing framework. In: IEEE 7th international symposium on service oriented system engineering (SOSE), San Francisco Bay, CA, 25–28 March 2013, pp.320–323. New York: IEEE
20. Zao JK, Gan TT, You CK. Augmented brain computer interaction based on fog computing and linked data. In: International conference on intelligent environments (IE), Shanghai, China, 30 June–4 July 2014, pp.374–377. New York: IEEE
21. Yi S, Hao Z, Qin Z. Fog computing: platform and applications. In: Proceedings of 3rd workshop on hot topics in web systems and technologies, Washington, DC, 12–13 November 2015, pp.73–78. Piscataway, NJ: IEEE
22. Naranjo PGV, Shojafar M, Mostafaei H. P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J Supercomput 2017; 73(2): 733–755
23. Nazhad SHH, Shojafar M, Shamshirband S. An efficient routing protocol for the QoS support of large-scale MANETs. Int J Commun Syst 2017; 31(1): 1–18
24. Feng H, Luo L, Wang Y. Multi-objective data collecting strategies for wireless sensor network based on the time variable multi-salesman problem and genetic algorithm. J Commun 2017; 38(3): 112–123
25. Donoho DL., Compressed sensing. IEEE T Inform Theory 2006; 52(4): 1289–1306
26. Konstantopoulos C, Mpitziopoulos A, Gavalas D. Effective determination of mobile agent itineraries for data aggregation on sensor networks. IEEE T Knowl Data Eng 2010; 22(12): 1679–1693
27. Wu X, Xiong Y, Huang W. An efficient compressive data gathering routing scheme for large-scale wireless sensor networks. Comput Electr Eng 2013; 39(6): 1935–1946
28. Shi X, Zhang Y, Zhao S. Discrete multi-objective optimization of particle swarm optimizer algorithm for multi-agents collaborative planning. J Commun 2016; 37(6): 29–37
29. Chatterjee S, Misra S., Target tracing using sensor-cloud: sensor-target mapping in presence of overlapping coverage. IEEE Commun Lett 2014; 18(8): 1435–1438
30. Liu P, Li L, Wang H., Research on greedy reconstruction algorithms of compressed sensing based on variable metric method. J Commun 2014; 35(12): 98–105, 115
31. Mini S, Udgata S, Sabat S., Sensor deployment and scheduling for target coverage problem in wireless sensor networks. IEEE Sens J 2014; 14(3): 636–644
32. Sun B, Shan X, Wu K. Anomaly detection based secure in-network aggregation for wireless sensor networks. IEEE Syst J 2013; 7(1): 13–25
33. Zhao CH, Liu W., Block compressive sensing based image semi-fragile zero-watermarking algorithm. Acta Automat Sin 2012; 38(4): 609–617
34. He Z, Yen G, Zhang J., Fuzzy-based pareto optimality for many-objective evolutionary algorithms. IEEE T Evolut Comput 2014; 18(2): 269–285
35. Ye M, Wang Y, Dai C. A hybrid genetic algorithm for the minimum exposure path problem of wireless sensor networks based on a numerical functional extreme model. IEEE T Veh Technol 2016; 65(10): 8644–8657
36. Tian J., Improvement of quantum-behaved particle swarm optimization algorithm for high-dimensional and multi-modal functions. Control Decis 2016; 31(11): 1967–1972
37. Li P, Wang J., Data gathering scheme based on sparse projection in wireless sensor networks. J Central South Univ 2016; 47(10): 3445–3453
38. He X, Ren Z, Shi C. A cloud and fog network framework for medical big data and its distributed computing scheme. J Xi’an Jiaotong Univ 2016; 50(10): 71–77