A Parameter Optimization Tool and Its Application to Throughput Estimation Model for Wireless LAN

Funabiki, Nobuo; Chihiro, Taniguchi; Lwin, Kyaw Soe; Zaw, Khin Khin; Kao, Wen Chung

doi:10.1007/978-3-319-61566-0_65

“…P 1 , α, and W k are parameters to be tuned. To consider the multipath effect, the indirect path is also considered by selecting a diffraction point for each AP/host pair and select the larger RSS between the direct and indirect signals for sigmoid function, Lwin et al (2017). It is noted that α can be replaced by α inc (enhanced path loss exponent factor) for d ≥ d thr (distance threshold) to improve the estimation accuracy because the prediction accuracy can be poor at longer distances from the transmitter, Sarkar et al (2003)-Cheung et al (1998.…”

Section: Signal Strength Estimationmentioning

confidence: 99%

“…The throughput estimation model has several parameters whose values determine the estimation accuracy. In our approach, they are optimized by use of the parameter optimization tool, Funabiki et al (2017), which adopts a local search algorithm that combines the tabu table and the hill climbing procedure to avoid a local minimum. This tool actually can be used for a variety of algorithms/logics that have parameters to be optimized.…”

Section: Parameter Optimization Toolmentioning

confidence: 99%

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

Kuribayashi

¹

,

Kao

²

,

Sudibyo

³

et al. 2019

IJSSC

Self Cite

1

0

View full text Add to dashboard Cite

As a flexible and cost-efficient Internet access network, the IEEE 802.11 wireless local-area network (WLAN) has been broadly deployed around the world. Previously, to improve the IEEE 802.11n WLAN performance, we proposed the four-step minimax access-point (AP) setup optimization approach: 1) link throughputs between the AP and hosts in the network field are measured manually, 2) the throughput estimation model is tuned using the measurement results, 3) the bottleneck host suffering the least throughput is estimated using this model, and 4) the AP setup is optimized to maximize the throughput of the bottleneck host. Unfortunately, this approach has drawbacks: 1) a lot of manual throughput measurements are necessary to tune the model, 2) the shift of the AP location is not considered, and 3) IEEE 802.11ac devices at 5 GHz are not evaluated, although they can offer faster transmissions. In this paper, we present the three enhancements: 1) the number of measurement points is reduced while keeping the model accuracy, 2) the coordinate of the AP setup is newly adopted as the optimization parameter, and 3) the AP device with IEEE 802.11ac at 5GHz is considered with slight modifications. The effectiveness is confirmed by extensive experiments in three network fields.

show abstract

“…To consider the multipath effect, the indirect path is also considered by selecting a diffraction point for each AP/host pair and select the larger RSS between the direct and indirect signals for sigmoid function, Lwin et al (2017).…”

Section: Signal Strength Estimationmentioning

confidence: 99%

“…Previously, we proposed the minimax AP setup optimization approach for IEEE 802.11n WLAN, Lwin et al (2017). This approach searches for the best setup condition of the AP suffering the minimum throughput, called bottleneck host, such that it maximizes the throughput of the host.…”

Section: Introductionmentioning

confidence: 99%

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

Lwin

¹

,

Funabiki

²

,

Debnath

³

et al. 2019

IJSSC

Self Cite

1

0

View full text Add to dashboard Cite

As a flexible and cost-efficient Internet access network, the IEEE 802.11 wireless local-area network (WLAN) has been broadly deployed around the world. Previously, to improve the IEEE 802.11n WLAN performance, we proposed the four-step minimax access-point (AP) setup optimization approach: 1) link throughputs between the AP and hosts in the network field are measured manually, 2) the throughput estimation model is tuned using the measurement results, 3) the bottleneck host suffering the least throughput is estimated using this model, and 4) the AP setup is optimized to maximize the throughput of the bottleneck host. Unfortunately, this approach has drawbacks: 1) a lot of manual throughput measurements are necessary to tune the model, 2) the shift of the AP location is not considered, and 3) IEEE 802.11ac devices at 5 GHz are not evaluated, although they can offer faster transmissions. In this paper, we present the three enhancements: 1) the number of measurement points is reduced while keeping the model accuracy, 2) the coordinate of the AP setup is newly adopted as the optimization parameter, and 3) the AP device with IEEE 802.11ac at 5GHz is considered with slight modifications. The effectiveness is confirmed by extensive experiments in three network fields.

show abstract

“…In this paper, we present the fingerprint optimization method for FILS15.4 to automatically optimize the number of fingerprints and their values for every room, by employing the existing parameter optimization tool in [10]. The procedure of calculating the score to evaluate the optimality of the current fingerprint selection is newly defined to determine the validity of the parameter changes in the method.…”

Section: Introductionmentioning

confidence: 99%

A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

Huo

¹

,

Puspitaningayu

²

,

Funabiki

³

et al. 2022

Self Cite

View full text Add to dashboard Cite

Nowadays, human indoor localization services inside buildings or on underground streets are in strong demand for various location-based services. Since conventional GPS cannot be used, indoor localization systems using wireless technologies have been extensively studied. Previously, we studied a fingerprint-based indoor localization system using IEEE802.15.4 devices, called FILS15.4, to allow use of inexpensive, tiny, and long-life transmitters. However, due to the narrow channel band and the low transmission power, the link quality indicator (LQI) used for fingerprints easily fluctuates by human movements and other uncontrollable factors. To improve the localization accuracy, FILS15.4 restricts the detection granularity to one room in the field, and adopts multiple fingerprints for one room, considering fluctuated signals, where their values must be properly adjusted. In this paper, we present a fingerprint optimization method for finding the proper fingerprint parameters in FILS15.4 by extending the existing one. As the training phase using the measurement LQI, it iteratively changes fingerprint values to maximize the newly defined score function for the room detecting accuracy. Moreover, it automatically increases the number of fingerprints for a room if the accuracy is not sufficient. For evaluations, we applied the proposed method to the measured LQI data using the FILS15.4 testbed system in the no. 2 Engineering Building at Okayama University. The validation results show that it improves the average detection accuracy (at higher than 97%) by automatically increasing the number of fingerprints and optimizing the values.

show abstract

A Parameter Optimization Tool and Its Application to Throughput Estimation Model for Wireless LAN

Cited by 9 publications

References 4 publications

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

Enhancements of minimax access-point setup optimisation approach for IEEE 802.11 WLAN

A Proposal of the Fingerprint Optimization Method for the Fingerprint-Based Indoor Localization System with IEEE 802.15.4 Devices

Contact Info

Product

Resources

About