Acta Metallurgica Sinica(English letters) ›› 2013, Vol. 20 ›› Issue (1): 26-36.doi: 10.1016/S1005-8885(13)60004-7

• Networks • Previous Articles     Next Articles

Leveraging data fusion to improve barrier coverage in wireless sensor networks


  1. 1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China 2. Graduate School of the Chinese Academy of Sciences, Beijing 100190, China
  • Received:2012-08-02 Revised:2012-09-22 Online:2013-02-28 Published:2013-02-28
  • Contact: Zhaoliang Zhang
  • Supported by:

    This work was supported by the National Basic Research Program of China (2011CB302803), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA060307000), and the National Natural Science Foundation of China (61003293), and the IoT Development Project of MIIT and MoF under title ‘Research & Development of IoT Application Middleware and Its Industrialization’


Intruder detection and border surveillance are amongst the most promising applications of wireless sensor networks. Barrier coverage formulates these problems as constructing barriers in a long-thin region to detect intruders that cross the region. Existing studies on this topic are not only based on simplistic binary sensing model but also neglect the collaboration employed in many systems. In this paper, we propose a solution which exploits the collaboration of sensors to improve the performance of barrier coverage under probabilistic sensing model. First, the network width requirement, the sensor density and the number of barriers are derived under data fusion model when sensors are randomly distributed. Then, we present an efficient algorithm to construct barriers with a small number of sensors. The theoretical comparison shows that our solution can greatly improve barrier coverage via collaboration of sensors. We also conduct extensive simulations to demonstrate the effectiveness of our solution.

Key words:

wireless sensor networks, coverage, deployment, data fusion, percolation theory

CLC Number: