IJRCS – Volume 1 Issue 4 Paper 4


Author’s Name : Shefin A J | R Sujitha

Volume 01 Issue 04  Year 2014  ISSN No:  2349-3828  Page no:  15-17



This paper focuses on the abnormal nodes detection of poisonous gas in wireless sensor networks, namely, Finding these nodes whose concentrations are higher than the threshold. In order to detect abnormal nodes, we had better collect sensory data from all nodes. However, this strategy requires much more energy consumption, so we should try to wakeup these nodes near the abnormal ?led. Based on this observation, we propose a novel energy efficient — method to wake them up. The main idea is to let abnormal nodes send out control packets to activate their one-hop neighbor nodes, then neighbor nodes continue detecting, and Finally, all abnormal nodes send information to the sink node through the shortest paths. Thereafter, we further propose to handle these information in the sink node, including extracting boundary nodes, drawing isolines, estimating the location of leakage source. To extract boundary nodes, we divide all abnormal nodes into different intervals in an ascending or descending order, then find two nodes with minimum and maximum in each interval, so these nodes are regarded as boundary nodes.


  1. D. ALANIS, P. BOTSINIS, S. X. NG, and L. HANZO, “Quantum-assisted routing optimization for self-organizing networks,” IEEE Access, vol. 2, pp. 614– 632, 2014.
  2. Y. Liu, J.-S. Fu, and Z. Zhang, “K-nearest neighbors tracking in wireless sensor networks with coverage holes,” Personal and Ubiquitous Computing, pp. 1–16, 2016.
  3. S. C. Tu, G. Y. Chang, J. P. Sheu, W. Li, and K. Y. Hsieh, “Scalable continuous object detection and tracking in sensor networks,” Journal of Parallel and Distributed Computing, vol. 70, no. 3, pp. 212–224, 2010.
  4. J. Zhu and Y. Zou, “Cognitive network cooperation for green cellular networks,” IEEE Access, vol. 4, pp. 849–857, 2016.
  5. B. Huang, W. Wu, and T. Zhang, “An improved connectivity based boundary detection algorithm in wireless sensor networks,” in IEEE Conference on Local Computer Networks, 332–335, 2013.
  6. K. A. M. K. Azmi, W. A. T. Wan Abdullah, and Z. Ibrahim, “Hough transform method for track finding in center drift chamber,” AIP Conference Proceedings, vol. 1704, 2016.
  7. B. Zhou and Y. He, “Fast circle detection using spatial decomposition of hough transform,” International Journal of Pattern Recognition and Artificial Intelligence, 2016.
  8. W. Cho and J. Jeon, “Edge detection in wavelet transform domain,” in The Workshop on Frontiers of Computer Vision, pp. 1–4, 2015.
  9. H. Yang, L. I. Xunbo, Z. Wang, Y. U. Wenjie, and B. Huang, “A novel sensor deployment method based on image processing and wavelet transform to optimize the surface coverage in wsns,” Chinese Journal of Electronics, vol. 25, no. 3, pp. 495–502, 2016.
  10. H. Du, Z. Huang, and J. He, “The research of image edge detection based on wavelet-transform,” Automation and Instrumentation, 2016.
  11. X. Wu, H. Chen, Y. Wang, L. Shu, and G. Liu, “Bp neural network based continuous objects distribution detection in wsns,” Wireless Networks, vol. 22, no. 6, pp. 1917–1929, 2016.
  12. F. U. Guangjie, D. Zhao, Q. Zhao, and S. Wang, “Research on detection and location system of pipeline leakage based on acoustic wave technology,” Modern Electronics Technique, 2015.