Advertisement

Scheduling algorithms for K-barrier coverage to improve transmission efficiency in WSNs

  • Yujun Zhu
  • Meng MeiEmail author
  • Zetian Zheng
Article
  • 3 Downloads

Abstract

K-barrier coverage optimization problem is concentrating on how to select sensor nodes from the monitoring area of the wireless sensor networks (WSNs) to form the highest quality of k-barrier coverage. Sink-connected barrier coverage optimization problem (SCBCOP) focuses on how to choose the minimum number of forwarding nodes to make each detecting node sink-connected for the security requirements of the belt monitoring region. However, the existing algorithm, such as optimal node selection algorithm(ONSA), can find the optimized k-barrier coverage of sink-connected, but it may form a large number of data packets interference (or collisions) and cannot transfer the information to sink nodes in time because the different detecting nodes can transmit invasive information at the same time. The purpose of this paper is to discuss how to reduce interference among the nodes and select routing path to optimize k-barrier coverage and satisfy sink-connected. In this paper a scheduling algorithm is proposed to build the routing path and maintain sink-connected. The algorithms is present in detail through forwarding routing tree and multi-channel scheduling to further reduce the interference of packet transmission among sensor nodes. Moreover, the comparison between other approaches and our proposal is mentioned through the simulation to show the potential efficiency and better performance of interference of packet transmission among sensor nodes with the lower packet loss rate, the shorter packet delay and the larger network average throughput.

Keywords

Wireless sensor networks K-barrier coverage Sink-connected Forwarding routing tree Multi-channel scheduling 

Notes

Acknowledgments

The research is supported by the National Science Foundation for Young Scientists of China(No. 61702011) and the National Natural Science Foundation of China(No. 61872006).

References

  1. 1.
    Bhatia A, Hansdah RC (2014) A fast and fault-tolerant distributed algorithm for near-optimal TDMA scheduling in WSNs[C]. In: IEEE international conference on distributed computing in sensor systems. IEEE, pp 294–301Google Scholar
  2. 2.
    Elsts A, Duquennoy S, Fafoutis X, Oikonomou G, Craddock I (2017) Microsecond-accuracy time synchronization using the IEEE 802.15.4 TSCH protocol[C]. In: Local computer networks workshops. IEEEGoogle Scholar
  3. 3.
    Fujimoto M, Ozaki H, Suzuki T, Koyamashita H, Okada H (2013) Effective barrier coverage constructions for improving border security in wireless sensor networks. IEICE Trans Commun E96.B(12):3007–3016CrossRefGoogle Scholar
  4. 4.
    Ghosal A, Halder S, Dasbit S (2012) A dynamic TDMA based scheme for securing query processing in WSN[J]. Wirel Netw 18(2):165–184CrossRefGoogle Scholar
  5. 5.
    Hadi A, Wahidah I (2017) Delay estimation using compressive sensing on WSN IEEE 802.15.4[C]. In: International conference on control, electronics, renewable energy and communications. IEEE, pp 192–197Google Scholar
  6. 6.
    Haghighi MS, Xiang Y, Varadharajan V, Quinn B (2015) A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks[J]. IEEE Trans Comput 64(3):627–639MathSciNetCrossRefGoogle Scholar
  7. 7.
    Huynh TT, Dinh-Duc AV, Tran CH (2014) Energy efficient delay-aware routing in multi-tier architecture for wireless sensor networks[C]. In: International Conference on Advanced Technologies for Communications. IEEE, pp 439–444Google Scholar
  8. 8.
    Luo J, Ren X, Zou S (2016) A decentralized K-barriers construction approach based on nearest neighbors rule for two-dimensional rectangular region[J]. Wirel Netw:1–11Google Scholar
  9. 9.
    Mehrjoo S, Shanbehzadeh J, Pedram MM (2010) A novel intelligent energy-efficient delay-aware routing in WSN, based on compressive sensing[C]. In: International symposium on telecommunications. IEEE, pp 415–420Google Scholar
  10. 10.
    Ren X, Liang W, Xu W (2014) Data collection maximization in renewable sensor networks via time-slot scheduling[J]. IEEE Trans Comput 64(7):1870–1883MathSciNetCrossRefGoogle Scholar
  11. 11.
    Santoso F (2010) Sub-optimal decentralised control algorithms for blanket and k-barrier coverage in autonomous robotic wireless sensor networks[J]. IET Commun 4(17):2041–2057MathSciNetCrossRefGoogle Scholar
  12. 12.
    Shimada N (2016) Study on Acces control method of IEEE802.15.4 in wireless LAN Coexistense[J]. Health Serv Res 42(1 Pt 2):577–586Google Scholar
  13. 13.
    Wang Z, Cao Q, Qi H et al (2017) Cost-effective barrier coverage formation in heterogeneous wireless sensor networks[J]. Ad Hoc Netw 64:65–79CrossRefGoogle Scholar
  14. 14.
    Yamamoto K, Ozaki H, Suzuki T, Wada T, Mutsuura K, Okada H (2011) Barrier coverage constructions for border security systems using wireless sensors[C]. In: 2011 international conference on parallel processing workshops, Taipei, Taiwan, pp 50–56Google Scholar
  15. 15.
    Yoo SE, Chong PK, Kim D et al (2010) Guaranteeing real-time Services for Industrial Wireless Sensor Networks with IEEE 802.15.4[J]. IEEE Trans Ind Electron 57(11):3868–3876CrossRefGoogle Scholar
  16. 16.
    Zhang Y, Sun X, Wang B (2016) Efficient algorithm for k-barrier coverage based on integer linear programming[J]. China Commun 13(7):16–23CrossRefGoogle Scholar
  17. 17.
    Zhuang Y, Wu C, Zhang Y et al (2016) Compound event barrier coverage in wireless sensor networks under multi-constraint conditions[J]. Sensors 17(12):1–17Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.School of Computer and InformationAnhui Normal UniversityWuhuChina
  2. 2.School of Electronics and Information EngineeringTongji UniversityShanghaiChina

Personalised recommendations