Adaptive access points deployment for indoor bluetooth positioning accuracy enhancement

被引：0

作者：

Zengshan T. ^{[1
]}

Haoliang R. ^{[1
]}

Mu Z. ^{[1
]}

机构：

[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Journal of China Universities of Posts and Telecommunications | 2019年 / 26卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Access points deployment; Fingerprint database; Greedy algorithm; Indoor positioning; Signal propagation model;

D O I：

10.19682/j.cnki.1005-8885.2019.0023

中图分类号：

学科分类号：

摘要：

The indoor positioning system based on fingerprint receives more and more attention due to its high positioning accuracy and time efficiency. In the existing positioning approaches, much consideration is given to the positioning accuracy improvement by using the angle of signal, but the optimization of access points (APs) deployment is ignored. In this circumstance, an adaptive APs deployment approach is proposed. First of all, the criterion of reference points (RPs) effective coverage is proposed, and the number of deployed APs in target environment is obtained by using the region partition algorithm and full coverage algorithm. Secondly, the wireless signal propagation model is established for target environment, and meanwhile based on the initial APs deployment, the simulation fingerprint database is constructed for the sake of establishing the discrimination function with respect to fingerprint database. Thirdly, the greedy algorithm is applied to optimize APs deployment. Finally, the extensive experiments show that the proposed approach is capable of achieving adaptive APs deployment as well as improving positioning accuracy. © 2019, Beijing University of Posts and Telecommunications. All rights reserved.

引用

页码：82 / 93

页数：11

共 32 条

[11]

Hwang C.R., Simulated annealing: Theory and applications, Acta Applicandae Mathematica, 12, 1, pp. 108-111, (1988)

[12]

Shahvari O., Logendran R., An enhanced TABU search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes, Computers and Operations Research, 77, 8, pp. 154-176, (2017)

[13]

Hiassat A., Diabat A., Rahwan I., A genetic algorithm approach for location-inventory-routing problem with perishable products, Journal of Manufacturing Systems, 42, 7, pp. 93-103, (2017)

[14]

Romoozi M., Ebrahimpour-Komleh H., A positioning method in wireless sensor networks using genetic algorithms, Physics Procedia, 33, 4, pp. 1042-1049, (2012)

[15]

Lee C.Y., Kang H.G., Cell planning with capacity expansion in mobile communications: A TABU search approach, IEEE Transactions on Vehicular Technology, 49, 5, pp. 1678-1691, (2000)

[16]

Amaldi E., Capone A., Malucelli F., Improved models and algorithms for UMTS radio planning, Proceedings of the 54Th Vehicular Technology Conference (Vtc-Fall'01), 2, pp. 920-924, (2001)

[17]

Chang W.C., Ko C.H., Lee Y.H., Et al., A novel prediction system for wireless LAN based on the genetic algorithm and neural network, Proceedings of the 24Th Conference on Local Computer Networks (LCN'99 ), pp. 258-259, (1999)

[18]

Maksuriwong K., Varavithya V., Chaiyaratana N., Wireless LAN access point placement using a multi-objective genetic algorithm, Proceedings of the 2003 IEEE International Conference on Systems, Man and Cybernetics (SMC '03 ), 2, pp. 1944-1949, (2003)

[19]

Sarkar T.K., Wicks M.C., Salazar-Palma M., Et al., Methods for Optimizing the Location of Base Stations for Indoor Wireless Communication. Smart Antennas, pp. 1481-1483, (2003)

[20]

Fang S.H., Lin T.N., A novel access point placement approach for WLAN-based location systems, Proceedings of the 2010 IEEE Wireless Communication and Networking Conference, (2010)

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