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Coverage-enhancing approach in multimedia directional sensor networks for smart transportation

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Abstract

Smart transportation has a significantly impact on city management and city planning, which has received extensive attentions from academic and industrial communities. Different from omni-directional sensing system, as a directional sensing system, the multimedia-directional sensor network holds the special coverage scheme, which is usually used for smart cities, smart transportation, and harsh environment surveillance, for instance, nuclear-pollution regions where are inhospitable for people. This paper advances Virtual Stream Artificial Fish-swarm based Coverage-Enhancing Algorithm (VSAFCEA) as a coverage-enhancing means in multimedia directional sensor networks. Firstly, a concept of virtual streams, based on traditional artificial fish-swarm algorithm, is proposed. Then, the traditional behaviors of fishes in artificial fish-swarm algorithm are modified and expanded with several new behaviors. Finally, the presented VSAFCEA is adopted for coverage-enhancing issue in the situation of directional sensor networks with rotational direction-adjustable model. With a sequence of steps of artificial fishes in virtual stream, the presented VSAFCEA can figure out the approximation to the highest area coverage rate. Based on comparison of these simulation results (results of presented VSAFCEA and that of other typical coverage-enhancing ways in directional sensor networks), the conclusion can be drawn that VSAFCEA could attain higher area coverage rate of directional sensor networks with fewer iterative computing times.

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References

  1. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114

    Article  Google Scholar 

  2. Guo C, Guo Q, Jin M, Lv Z (2015) Dynamic systems based on preference graph and distance. Discrete Contin Dyn Syst Ser S 8(6):1139–1154

    Article  MathSciNet  MATH  Google Scholar 

  3. Guo C, Liu X, Jin M et al (2015) The research on optimization of auto supply chain network robust model under macroeconomic fluctuations. Chaos, Solitons Fractals

  4. Howard A, Matarić MJ, Sukhatme GS (2002) Mobile sensor network deployment using potential field: a distributed scalable solution to the area coverage problem. In Proceedings of the 6th Int’l Symp. on Distributed Autonomous Robotics Systems (pp.299–308)

  5. Ian FA, Tommaso M, Kaushik RC (2008) Wireless multimedia sensor networks: applications and testbeds. Proc IEEE 96(10):1588–1605

    Article  Google Scholar 

  6. Jiang D, Wang Y, Yao C et al (2015) An effective dynamic spectrum access algorithm for multi-hop cognitive wireless networks. Comput Netw 84(19):1–16

    Article  Google Scholar 

  7. Jiang D, Xu Z, Chen Z et al (2011) Joint time-frequency sparse estimation of large-scale network traffic. Comput Netw 55(10):3533–3547

    Article  Google Scholar 

  8. Jiang D, Xu Z, Li W et al (2014) Topology control-based collaborative multicast routing algorithm with minimum energy consumption. International Journal of Communication Systems, online available.

  9. Jiang D, Xu Z, Li W et al (2015) Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks. J Syst Softw 104:152–165

    Article  Google Scholar 

  10. Jiang D, Xu Z, Liu J et al (2015) An optimization-based robust routing algorithm to energy-efficient networks for cloud computing. Telecommunication Systems, online available

  11. Jiang D, Xu Z, Wang W et al (2015) A collaborative multi-hop routing algorithm for maximum achievable rate. J Netw Comput Appl 57:182–191

    Article  Google Scholar 

  12. Jiang D, Xu Z, Zhang P et al (2014) A transform domain-based anomaly detection approach to network-wide traffic. J Netw Comput Appl 40(2):292–306

    Article  Google Scholar 

  13. Jiang D, Xu Z, Xu H (2015) A novel hybrid prediction algorithm to network traffic. Ann Telecommun 70(9):427–439

    Article  Google Scholar 

  14. Jiang D, Xu Z, Zhihan Lv (2015) An multicast delivery approach with minimum energy consumption for wireless multi-hop networks. Telecommunication Systems, online available

  15. Jiang D, Yao C, Xu Z et al (2015) Multi-scale anomaly detection for high-speed network traffic. Trans Emerg Telecommun Technol 26(3):308–317

    Article  Google Scholar 

  16. Jiang D, Ying X, Han Y et al (2015) Collaborative multi-hop routing in cognitive wireless networks. Wireless Personal Communications, online available

  17. Kirkpatrick S, Gelatt C, Vecchi P (1983) Optimaization by simulated annealing. Science 220(4598):671–679

    Article  MathSciNet  MATH  Google Scholar 

  18. Kumar S, Lobiyal DK (2013) An Advanced DV-Hop Localization Algorithm for Wireless Sensor Networks. Wirel Pers Commun 71(2):1365–1385

    Article  Google Scholar 

  19. Li XL, Shao ZJ, Qian JX (2002) An optimizing method based on autonomous animates: fish-swarm algorithm. Syst Eng Theory Pract 11:34–38 (in Chinese)

    Google Scholar 

  20. Lin Y, Yang J, Lv Z et al (2015) A self-assessment stereo capture model applicable to the internet of things. Sensors 15(8):20925–20944

    Article  Google Scholar 

  21. Liu CB, Wang HJ, Luo ZP (2009) QoS multicast routing problem based on artificial fish-swarm algorithm. In International Workshop on Education Technology and Computer Science (pp. 814–817)

  22. Lv Z, Halawani A, Fen S et al (2015) Touch-less Interactive Augmented Reality Game on Vision Based Wearable Device. Pers Ubiquit Comput 19(3):551–567

    Article  Google Scholar 

  23. Lv Z, Tek A, Da Silva F et al (2013) Game on, science-how video game technology may help biologists tackle visualization challenges. PLoS One 8(3):57990

    Article  Google Scholar 

  24. Lv Z, Yin T, Han Y et al (2011) WebVR——web virtual reality engine based on P2P network. J Netw 6(7):990–998

    Google Scholar 

  25. Ma HD, Zhang X, Ming AL (2009) A coverage-enhancing method for 3D directional sensor networks. In Proc. of IEEE INFOCOM, (pp.2791-9795)

  26. Mostafaei H, Meybodi MR (2013) Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata. Wirel Pers Commun 71(2):1461–1477

    Article  Google Scholar 

  27. Poduri S, Sukhatme GS (2004) Constrained coverage for mobile sensor networks. In Proceedings of the IEEE Int’l Conf. on Robotics & Automation (pp.165−171)

  28. Shan XJ, Jiang MY, Li JP (2006) The routing optimization based on improved artificial fish swarm algorithm. In Proceedings of the 6th World Congress on Intelligent Control and Automation (pp. 3658–3662)

  29. Sheng G, Dang S, Hossain N, et al (2015) Modeling of mobile communication systems by electromagnetic theory in the direct and single reflected propagation scenario. Applications and Techniques in Information Security. Springer Berlin Heidelberg, 280–290

  30. Srinivas M, Patnaik LM (1994) Genetic algorithm: a survey. IEEE Comput 27(6):17–26

    Article  Google Scholar 

  31. Tao D, Ma HD, Liu L (2007) A virtual potential field based coverage-enhancing algorithm for directional sensor networks. J Softw 18(5):1152–1163 (in Chinese)

    Article  Google Scholar 

  32. Wang LG, Hong Y (2008) A multiagent artificial fish swarm algorithm. In Proceedings of the 7th World Congress on Intelligent Control and Automation (pp.3161–3166)

  33. Wang K, Liu N, Sadooghi I et al (2015) Overcoming hadoop scaling limitations through distributed task execution. 2015 I.E. International Conference on Cluster Computing (CLUSTER), IEEE. 236–245

  34. Wang Y, Su Y, Agrawal G (2015) A novel approach for approximate aggregations over arrays. Proceedings of the 27th International Conference on Scientific and Statistical Database Management

  35. Wang JW, Wang XW, Huang M (2008) Tabu artificial fish swarm algorithm based intelligent QoS multicast routing algorithm. In International Conference on Computational Intelligence and Security (pp.56–60)

  36. Yan Y, Yang Y, Meng D, Liu G, Tong W, Hauptmann A, Sebe N (2015) Event Oriented Dictionary Learning for Complex Event Detection. IEEE Trans Image Process 24(6):1867–1878

    Article  MathSciNet  Google Scholar 

  37. Yang J, Chen B, Zhou J et al (2015) A low-power and portable biomedical device for respiratory monitoring with a stable power source. Sensors 15(8):19618–19632

    Article  Google Scholar 

  38. Yang J, He S, Lin Y et al (2015) Multimedia cloud transmission and storage system based on internet of things. Multimed Tools Appl 1–16

  39. Yang J, Zhou J, Lv Z et al (2015) A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks. Sensors 15(11):29535–29546

    Article  Google Scholar 

  40. Zhang X, Han Y, Hao DS et al (2015) ARPPS: Augmented reality pipeline prospect system. Neural Inf Process 647–656

  41. Zhang BL, Yu FQ (2010) LSWD: Localization Scheme for Wireless Sensor Networks using Directional Antenna. IEEE Trans Consum Electron 56(4):2208–2216

    Article  Google Scholar 

  42. Zhang K, Zhang W, Dai CY, Zeng JZ (2010) Artificial fish-swarm based coverage-enhancing algorithm for visible light sensor networks. Optoelectron Lett 6(3):229–231

    Article  Google Scholar 

Download references

Acknowledgments

The paper is partially supported by Fundamental Research Funds for the Central Universities (ZYGX2014J099), the National Natural Science Foundation of China (Nos. 61571104, 61071124), the General Project of Scientific Research of the Education Department of Liaoning Province (No. L20150174), and the Program for New Century Excellent Talents in University (No. NCET-11-0075),.

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Authors and Affiliations

  1. School of Communication and Information Engineering, UESTC, Chengdu, 611731, China

    Ke Zhang & Haitao Jia

  2. Qingdao Huanhai Marine Engineering Prospecting Institute, Qingdao, 266033, China

    Haibin Lv

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  1. Ke Zhang
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  2. Haitao Jia
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  3. Haibin Lv
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Correspondence to Ke Zhang.

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Zhang, K., Jia, H. & Lv, H. Coverage-enhancing approach in multimedia directional sensor networks for smart transportation. Multimed Tools Appl 75, 17593–17615 (2016). https://doi.org/10.1007/s11042-016-3586-9

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  • DOI: https://doi.org/10.1007/s11042-016-3586-9

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