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Wireless multimedia sensor network video object detection method using dynamic clustering algorithm

  • Yilin ShaoEmail author
Article
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Abstract

Most of the traditional tracking algorithms use the Kalman filter to predict the tracking process. Although the tracking accuracy is relatively high, the calculation is large and the time complexity is high. Based on this research, this paper proposes a dynamic clustering target tracking algorithm for motion trends. The algorithm forms a dynamic cluster in the network, and the cluster head dynamically schedules the nodes to achieve collaborative tracking of the targets. The tracking strategy is mainly divided into two stages: First, the cluster head establishes a “neighbor node set” within its communication range, and selects the neighbor node in the “neighbor node set” according to the distance between the node and the target to construct the “intra-cluster member set” to perform the target on the target. Tracking; as the target moves continuously, the cluster head updates the members in the cluster at regular intervals, removes the nodes that have lost the target monitoring from the cluster, and adds the new nodes to the cluster; secondly, elects a new cluster head; if current When the cluster head is no longer suitable to continue to serve as the cluster head, the current cluster head selects the node in the “intra-cluster member set” as the new cluster head of the next work cycle; according to the moving direction of the target, selects the node with the best moving tendency of the target For the new cluster head, this allows the new cluster head to have a longer duty cycle and avoid frequent replacement of the cluster head; the new cluster head continues to set up the dynamic cluster to track the target until the target moves out of the monitoring area. The simulation results show that the proposed algorithm is more efficient than the traditional target tracking method.

Keywords

Target detection Wireless multimedia Sensor network video Dynamic clustering algorithm 

Notes

References

  1. 1.
    Alassery F, Ahmed WKM, Sarraf M et al (2014) A novel scheme for power saving in wireless sensor networks with packet collision[C]. Wirel Microwave Technol Conf IEEE 1–4Google Scholar
  2. 2.
    Alhilal MS, Soudani A, Al-Dhelaan A (2014) Low power scheme for image based object identification in wireless multimedia sensor networks[C]. Int Conf Multimed Comput Syst IEEE 927–932Google Scholar
  3. 3.
    Asurti N, Jinwala CD (2015) A static code and dynamic data attestation based intrusion detection system for wireless sensor networks[J]. Int J Comput Appl 119(18):18–24Google Scholar
  4. 4.
    Gao L, Bourke AK, Nelson J (2012) Activity recognition using dynamic multiple sensor fusion in body sensor networks[C]. Eng Med Biol Soc Conf Proc IEEE Eng Med Biol Soc 1077Google Scholar
  5. 5.
    Ghadi M, Laouamer L, Moulahi T (2016) Securing data exchange in wireless multimedia sensor networks: perspectives and challenges[J]. Multimed Tools Appl 75(6):1–27CrossRefGoogle Scholar
  6. 6.
    Kamal ARM, Bleakley CJ, Dobson S (2014) Failure detection in wireless sensor networks:a sequence-based dynamic approach[J]. ACM Trans Sens Netw 10(2):1–29CrossRefGoogle Scholar
  7. 7.
    Koulali MA, Kobbane A, Koutbi ME et al (2012) Dynamic power control for energy harvesting wireless multimedia sensor networks[J]. Eurasip J Wirel Commun Netw 2012(1):1–8CrossRefGoogle Scholar
  8. 8.
    Liu Y, Zhang Y et al (2013) A real-time dynamic key management for hierarchical wireless multimedia;sensor network[J]. Multimed Tools Appl 67(1):97–117CrossRefGoogle Scholar
  9. 9.
    Maheswari PU, Kumar PG (2016) Dynamic detection and prevention of clone attack in wireless sensor networks[J]. Wirel Pers Commun 94(4):1–12Google Scholar
  10. 10.
    Masazade E, Niu R, Varshney PK (2012) Dynamic bit allocation for object tracking in wireless sensor networks[J]. IEEE Trans Signal Process 60(10):5048–5063MathSciNetCrossRefGoogle Scholar
  11. 11.
    Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Rajendra Acharya U (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput Appl 1–7.  https://doi.org/10.1007/s00521-018-3689-5
  12. 12.
    Rajendra Achary U, Hagiwara Y, Deshpande SN, Suren S, Koh JEW, Oh SL, Arunkumar N, Ciaccio EJ, Lim CM (2019) Characterization of focal EEG signals: a review. Futur Gener Comput Syst 91:290–299CrossRefGoogle Scholar
  13. 13.
    Salunke A, Ambawade D (2015) Dynamic sequence number thresholding protocol for detection of blackhole attack in wireless sensor network[C]. Int Conf Commun Inf Comput Technol IEEE 1–4Google Scholar
  14. 14.
    Spachos P, Toumpakaris D, Hatzinakos D (2015) QoS and energy-aware dynamic routing in Wireless Multimedia Sensor Networks[C]. IEEE Int Conf Commun IEEE 6935–6940Google Scholar
  15. 15.
    Wu T, Cheng Q (2013) Bandwidth-efficient dynamic event region detection and reconstruction in wireless sensor networks[C]. Int Symp Wirel Pers Multimed Commun IEEE 1–5Google Scholar
  16. 16.
    Wu H, Cao J, Fan X (2016) Dynamic collaborative in-network event detection in wireless sensor networks[J]. Telecommun Syst 62(1):1–16CrossRefGoogle Scholar
  17. 17.
    Zappi P, Roggen D, Farella E et al (2012) Network-level power-performance trade-off in wearable activity recognition: a dynamic sensor selection approach[J]. ACM Trans Embed Comput Syst 11(3):68CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of ComputingShangqiu PolytechnicShangqiuChina

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