Wireless Networks

, Volume 25, Issue 1, pp 355–365 | Cite as

Probabilistic coverage in directional sensor networks

  • Pengju SiEmail author
  • Chengdong Wu
  • Yunzhou Zhang
  • Hao Chu
  • He Teng


Correct and realistic sensing model is of great significance for sensor networks to perform environmental monitoring. However, the sensing capabilities of directional sensors are usually affected by environmental factors and intrinsic properties. As a result, it is not suitable to use simple binary sensing model for directional sensor networks (DSNs). In this paper, we consider probabilistic sensing models for DSNs to have practical considerations. Firstly, an exponential decay probabilistic sensing model for DSNs is considered reference to omni-directional probabilistic sensing model for traditional sensor. Secondly, we propose a fault-tolerant probabilistic sensing model with respect to orientation error and rotation error. Furthermore, combined the exponential decay probability with the fault-tolerant probability, a hybrid probabilistic sensing model is formed for DSNs. Then, based on our probability sensing models, a probabilistic coverage algorithm is introduced for random deployment to compute the coverage probability of DSNs. Performance results demonstrate that the proposed algorithm with probabilistic sensing models for DSNs is capable of providing desirable surveillance level.


Directional sensor networks Probabilistic sensing model Coverage probability Random deployment Algorithm 



This research was supported in parts by Supported by National Natural Science Foundation of China (61471110, 61273078); Fundamental Research Funds for the Central Universities (N160413002, N160404003, N162610004); Foundation of Liaoning Provincial Department of Education (L2014090).


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Pengju Si
    • 1
    Email author
  • Chengdong Wu
    • 1
  • Yunzhou Zhang
    • 1
  • Hao Chu
    • 1
  • He Teng
    • 1
  1. 1.Faculty of Robot Science and EngineeringNortheastern UniversityShenyangChina

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