TY - JOUR
T1 - Full-view area coverage in camera sensor networks
T2 - Dimension reduction and near-optimal solutions
AU - He, Shibo
AU - Shin, Dong Hoon
AU - Zhang, Junshan
AU - Chen, Jiming
AU - Sun, Youxian
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61402405 and Grant 61528105; by the Fundamental Research Funds for the Central Universities under Grant 2015FZA5011; and by the U.S. National Science Foundation under Grant CNS-1457278, Grant CNS-1422277, and Grant ECCS-1408409.
Publisher Copyright:
© 2015 IEEE.
PY - 2016/9
Y1 - 2016/9
N2 - We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the "contribution" of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.
AB - We study the problem of minimum-number full-view area coverage in camera sensor networks, i.e., how to select the minimum number of camera sensors to guarantee the full-view coverage of a given region. Full-view area coverage is challenging because the full-view coverage of a 2-D continuous domain has to be considered. To tackle this challenge, we first study the intrinsic geometric relationship between the full-view area coverage and the full-view point coverage and prove that the full-view area coverage can be guaranteed, as long as a selected full-view ensuring set of points is full-view covered. This leads to a significant dimension reduction for the full-view area coverage problem. Next, we prove that the minimum-number full-view point coverage is NP-hard and propose two approximation algorithms to solve it from two different perspectives, respectively: 1) By introducing a full-view coverage ratio function, we quantify the "contribution" of each camera sensor to the full-view coverage through which we transform the full-view point coverage into a submodular set cover problem and propose a greedy algorithm (GA); and 2) by studying the geometric relationship between the full-view coverage and the traditional coverage, we propose a set-cover-based algorithm (SCA). We analyze the performance of these two approximation algorithms and characterize their approximation ratios. Furthermore, we devise two distributed algorithms that obtain the same approximation ratios as GA and SCA, respectively. Finally, we provide extensive simulation results to validate our analysis.
KW - Camera sensor network
KW - Distributed algorithms
KW - Full-view area coverage
KW - Full-view point coverage
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U2 - 10.1109/TVT.2015.2498281
DO - 10.1109/TVT.2015.2498281
M3 - Article
AN - SCOPUS:84990931786
SN - 0018-9545
VL - 65
SP - 7448
EP - 7461
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 9
M1 - 7321040
ER -