Smart scanning for mobile devices in WLANs

Lee, Soo-Yong; Kim, Myungchul; Kang, Sungwon; Lee, Kyung‐Hee; Jung, Il-Gu

doi:10.1109/icc.2012.6364601

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…It is also usual that mobile STAs periodically perform active background scanning to discover available APs, and then accelerate an eventual roaming operation [21]. In this case, the STA (already associated to an AP and exchanging data) goes periodically off-channel and sends probe requests across other channels.…”

Section: ) Wifi Scanning Modesmentioning

confidence: 99%

Channel Load Aware AP / Extender Selection in Home WiFi Networks Using IEEE 802.11k/v

et al. 2021

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Next-generation Home WiFi networks have to step forward in terms of performance. New applications such as on-line games, virtual reality or high quality video contents will further demand higher throughput levels, as well as low latency. Beyond physical (PHY) and medium access control (MAC) improvements, deploying multiple access points (APs) in a given area may significantly contribute to achieve those performance goals by simply improving average coverage and data rates. However, it opens a new challenge: to determine the best AP for each given station (STA). This article studies the achievable performance gains of using secondary APs, also called Extenders, in Home WiFi networks in terms of throughput and delay. To do that, we introduce a centralized, easily implementable channel load aware selection mechanism for WiFi networks that takes full advantage of IEEE 802.11k/v capabilities to collect data from STAs, and distribute association decisions accordingly. These decisions are completely computed in the AP (or, alternatively, in an external network controller) based on an AP selection decision metric that, in addition to RSSI, also takes into account the load of both access and backhaul wireless links for each potential STA-AP/Extender connection. Performance evaluation of the proposed channel load aware AP and Extender selection mechanism has been first conducted in a purpose-built simulator, resulting in an overall improvement of the main analyzed metrics (throughput and delay) and the ability to serve, at least, 35% more traffic while keeping the network uncongested when compared to the traditional RSSI-based WiFi association. This trend was confirmed when the channel load aware mechanism was tested in a real deployment, where STAs were associated to the indicated AP/Extender and total throughput was increased by 77.12%.

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Section: ) Wifi Scanning Modesmentioning

confidence: 99%

Channel Load Aware AP / Extender Selection in Home WiFi Networks Using IEEE 802.11k/v

et al. 2021

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“…These messages are sent out on all channels one after another. This is the preferred method for mobile devices due to lower energy-consumption and shorter discovery time of access points [9]. Empirical test with different mobile devices show that an active scan is performed at least once within two minutes, despite the case that the test device was associated to an access point or not [4].…”

Section: Wi-fimentioning

confidence: 99%

Estimating Crowd Densities and Pedestrian Flows Using Wi-Fi and Bluetooth

Schauer¹,

Werner²,

Marcus³

2014

Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

156

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The rapid deployment of smartphones as all-purpose mobile computing systems has led to a wide adoption of wireless communication systems such as Wi-Fi and Bluetooth in mobile scenarios. Both communication systems leak information to the surroundings during operation. This information has been used for tracking and crowd density estimations in literature. However, an estimation of pedestrian flows has not yet been evaluated with respect to a known ground truth and, thus, a reliable adoption in real world scenarios is rather difficult. With this paper, we fill in this gap. Using ground truth provided by the security check process at a major German airport, we discuss the quality and feasibility of pedestrian flow estimations for both Wi-Fi and Bluetooth captures. We present and evaluate three approaches in order to improve the accuracy in comparison to a naive count of captured MAC addresses. Such counts only showed an impractical Pearson correlation of 0.53 for Bluetooth and 0.61 for Wi-Fi compared to ground truth. The presented extended approaches yield a superior correlation of 0.75 in best case. This indicates a strong correlation and an improvement of accuracy. Given these results, the presented approaches allow for a practical estimation of pedestrian flows.

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“…These messages are sent out on all channels one after another. This is the preferred method for mobile devices due to lower energy-consumption and shorter discovery time of access points [10]. Empirical test with different mobile devices show that an active scan is performed at least once within two minutes, despite the case that the test device was associated to an access point or not [4].…”

Section: Wi-fimentioning

confidence: 99%

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

Schauer¹,

Werner²

2015

ICST Transactions on Ubiquitous Environments

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The rapid deployment of smartphones has led to a wide adoption of wireless communication systems such as Wi-Fi and Bluetooth. Both techniques leak information to the surroundings during operation. This information has been used in literature for estimating pedestrian flows, but the correlation to ground truth has not yet been evaluated. Thus, a reliable deployment in real world scenarios is rather difficult. To fill in this gap, we use ground truth provided by the security check process at a major airport and evaluate the quality of crowd information gathered from Wi-Fi and Bluetooth captures. We analyze estimated pedestrian flows and present three approaches improving the accuracy compared to a naive count of captured MAC addresses. Such counts only showed an impractical Pearson correlation of 0.53 for Bluetooth and 0.61 for Wi-Fi. The presented approaches yield a better correlation and allow for a practical estimation of pedestrian flows.

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Smart scanning for mobile devices in WLANs

Cited by 13 publications

References 9 publications

Channel Load Aware AP / Extender Selection in Home WiFi Networks Using IEEE 802.11k/v

Channel Load Aware AP / Extender Selection in Home WiFi Networks Using IEEE 802.11k/v

Estimating Crowd Densities and Pedestrian Flows Using Wi-Fi and Bluetooth

Analyzing Pedestrian Flows Based on Wi-Fi and Bluetooth Captures

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