A WLAN planning method for indoor positioning system

Zhang, Zhaoyu; Di, Xin; Tian, Junfang; Chen, Pei

doi:10.1109/icoin.2016.7427081

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…However, a physical site survey consumes more time and requires a highly expert to gather accurate results. WLAN physical site survey focuses more on signal strength and data rate as the factors to determine the number of needed WLAN devices and the placement of the devices [15], [16]. The suggestions for the required devices are commonly made based on previously conducted survey experiences.…”

Section: A Wireless Area Network (Wlan)mentioning

confidence: 99%

“…Recent studies discuss different approaches used to perform WLAN planning: artificial intelligence and mathematical approaches. Studies have suggested signal strength, coverage, location of AP placement, transmission power, speed, latency as well and throughput are among the parameters that should be highlighted as the preliminary input of network planning [15], [19]- [21].…”

Section: B Wlan Planning Approachesmentioning

confidence: 99%

“…Most artificial intelligence approaches focus on optimizing the WLAN performance by predicting the best location of AP placement and channel assignment [15], [20], [22]. The transmission power and placement of each access point were optimized using a hybrid swarm intelligent optimization algorithm termed PSO-Lévy-DFOA in another study by Liu et al [19], providing efficient coverage while reducing interference.…”

Section: B Wlan Planning Approachesmentioning

confidence: 99%

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Predictive Wireless Received Signal Strength Using Friis Transmission Technique

Rawi,

Mohd Isa,

Ismail

et al. 2024

JOIV : Int. J. Inform. Visualization

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A good WLAN performance is crucial in determining the quality of experience (QoE) among the campus community. Proper WLAN planning and design should be done beforehand to ensure good WLAN performance. Various studies have discussed different methods of conducting WLAN planning to predict WLAN's best performance, including using artificial intelligence and mathematical approaches. One of the processes involved in performing WLAN planning is measuring performance parameters. Signal strength is one of the vital parameters to be measured in determining the excellent performance of WLAN in a particular area. When deploying a WLAN design in two different environments, the signal strength outcomes can differ due to various factors, including obstacles and path loss propagation issues within the deployment area. Higher Learning Institutions (HLIs) present a unique challenge as their building designs vary to accommodate student needs. As a result, the selection of materials used will also be different, affecting the WLAN performance. A detailed study should investigate the effect of path loss propagation and the type of obstacle that affects WLAN performance in HLI. Thus, this study focuses on predicting received signal strength using Friis Transmission and studying the effect of path loss propagation on WLAN performance. The simulated model significantly affects signal strength when the signal passes through different types of building material (non-LOS) and line-of-sight (NLOS), where concrete walls substantially affect the received signal strength between transmitters. The proposed model can assist network planners in designing robust WLAN infrastructure by improving signal strength, particularly in the HLI WLAN environment.Â

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Section: A Wireless Area Network (Wlan)mentioning

confidence: 99%

Section: B Wlan Planning Approachesmentioning

confidence: 99%

Section: B Wlan Planning Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Wireless Received Signal Strength Using Friis Transmission Technique

Rawi,

Mohd Isa,

Ismail

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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“…In [20], an AP planning method for indoor positioning system has been proposed, together with the GDOP evaluation, in order to decrease the position error. In [21], a map-assisted AP placement algorithm has been proposed, and an accuracy within 3 m of 71% has been achieved.…”

Section: Introductionmentioning

confidence: 99%

“…In [22], AP topology has been evaluated and optimized utilizing Cramér-Rao Lower Bound calculation method. The deploying schemes in [20,21,22] have an instructional significance to the implementation of APs when a new indoor WLAN based positioning system is set up. However, the APs’ deployments in most positioning conditions are generally fixed together with the construction of these infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Liu

Deng

2016

Sensors

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Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

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