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Critical density for coverage and connectivity in non-orientation directional sensor network
Author(s) -
Lin Kang,
Zengshou Dong,
YanJie Qi
Publication year - 2020
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147720925758
Subject(s) - computer science , wireless sensor network , orientation (vector space) , percolation (cognitive psychology) , poisson point process , component (thermodynamics) , connected component , point (geometry) , process (computing) , field (mathematics) , topology (electrical circuits) , distributed computing , poisson distribution , computer network , artificial intelligence , mathematics , geometry , physics , statistics , combinatorics , neuroscience , pure mathematics , biology , operating system , thermodynamics
Both coverage and connectivity are important problems in wireless sensor networks. As more and more non-orientation sensors are continuously added into the region of interest, the size of covered component and connected component increases; at some point, the network can achieve an entire coverage and full connectivity after which the network percolates. In this article, we analyze the critical density in non-orientation directional sensor network in which the orientations of the sensors are random and the sensors are deployed according to the Poisson point process. We propose an approach to compute the critical density in such a network. A collaborating path is proposed with the sum of field-of-view angles of two collaborating sensors being π. Then a correlated model of non-orientation directional sensing sectors for percolation is proposed to solve the coverage and connectivity problems together. The numerical simulations confirm that percolation occurs on the estimated critical densities. It is worth mentioning that the theoretical analysis and simulation results give insights into the design of directional sensor network in practice.

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