A model for WLAN signal attenuation of the human body

Fet, Ngewi; Handte, Marcus; Marrón, Pedro José

doi:10.1145/2493432.2493459

Cited by 30 publications

(22 citation statements)

References 25 publications

(24 reference statements)

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“…[10] quantifies these findings: when a person is standing 1m away from an access point facing the device, the RSSI of the WiFi signal drops over 40 dBm when receiving the signal behind that person compared to receiving it in front of it. In a distance of 10m however, there is no noticeable difference anymore.…”

Section: E the Effect Of Dense Crowdsmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging human mobility in smartphone based Ad-Hoc information distribution in crowd management scenarios

Franke

Negele

Kampis

et al. 2015

2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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We propose a novel approach for Ad-Hoc WiFi based distribution of information within large crowds of mobile users. The work is motivated by civil protection scenarios where infrastructure based communication often breaks down in cases of emergency. We follow a basic opportunistic networking ap proach by making use of the smartphones' built-in WiFi hotspot functionality which in combination with the devices switching between access point and client modes facilitates the propagation of messages on a multi-hop basis. We make three contributionswith respect to previous work on this topic. First, we empirically determine core boundary conditions given by the performance of modern smartphones. To maximize system performance under such circumstances we propose novel heuristics for a mode switching strategy based on client mobility instead of random strategies that have mainly been utilized so far. Finally, we compare its performance to a random role switching strategy in a large-scale simulation based on a real dataset consisting of movement traces from 28'000 people during a three day festival in Zurich. Within the simulation we investigate the influence of various parameters on the system's behavior. I. MOTIV ATIONManaging crowds at large scale events and during everyday life in urban areas is a complex and highly dynamic problem. Up to date knowledge about the current state of a crowd (e.g. with respect to its density) is absolutely vital for operational personnel. This includes the earliest possible detection of potential hot spots and the means to communicate with the crowd in an efficient manner to solve problems before they turn into critical situations.Over the past years, we therefore developed a smartphone app-based crowd management system, which analyzes smart phone sensor data voluntarily contributed by visitors of large scale events and creates a real time overview about crowd conditions such as crowd density, crowd pressure and crowd turbulences [1], [2]. More importantly from a communication perspective, the system also provides the means for event organizers and emergency forces to cOlmnunicate with the crowd in a targeted fashion utilizing location based messages [3].Until today, this system has been deployed during numer ous international events and was consequently used by over 100.000 users who contributed well over 50 million data points in total. The deployment during the Zurich festival [4] resulted in the creation of the -according to our knowledge -largest GPS based mobility dataset collected at a public event at that time.A key concern that has emerged during virtually all de ployments was the ability to deal with network outages. Such outages must be expected during emergency situations and are also not uncommon at large enough public gatherings under normal circumstances. Clearly, a network outage during an emergency would mean that the system functionality (sending messages containing safety advice) would become unavailable at the very moment when it is most needed. As a consequence we have ...

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Section: E the Effect Of Dense Crowdsmentioning

confidence: 99%

“…Fet et al presented a model for WiFi signal attenuation of the human body [10] in 20l3. Since our scenario usually in volves large (and mostly dense) crowds of people, we consider their findings within our system performance parameters.…”

Section: Related Workmentioning

confidence: 99%

Leveraging human mobility in smartphone based Ad-Hoc information distribution in crowd management scenarios

Franke

Negele

Kampis

et al. 2015

2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

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“…The obstacle we study is specific to human body blocking effect as mobile devices are carried by people. Existing work [9]- [12] has observed that human body has a significant impact on WiFi signals (in both 2.4 GHz and 5 GHz frequencies). Since Bluetooth also uses 2.4 GHz frequency, we infer that similar obstacle effect of human body on WiFi signals is also applicable to Bluetooth signals.…”

Section: A Path Loss Effects On Bluetooth Signalsmentioning

confidence: 99%

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

Han

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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People in social environments often appear in groups to have face-to-face interactions, automatically recognizing these groups will facilitate many applications while being unobtrusive. In this work, we focus on recognizing multiple concurrent intentional groups of people in proximity using a real-time distributed approach running on commodity mobile devices. Specifically, we study Bluetooth signal strength probability distribution for proximity estimation and model the group probability distribution under Bluetooth signal strength. Further, we develop a realtime distributed group determination algorithm. We implement a prototype system on iOS platforms which includes both the group recognition service and a mobile social application using the service. Both our simulation and prototype results show that the proposed approach can recognize proximity-based intentional groups with high accuracy.

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“…When the channels are considered separately, small fluctuations are visible in each, however these are lost when considering them in aggregate. There are a variety of studies that investigate the effect of human bodies on fingerprinting-based and range-based positioning [10][11][12]. Early work approximated the attenuation due to body shadowing with a Rician distribution [10].…”

Section: Introductionmentioning

confidence: 99%

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

Naghdi

O’Keefe

2020

Sensors

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One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.

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A model for WLAN signal attenuation of the human body

Cited by 30 publications

References 25 publications

Leveraging human mobility in smartphone based Ad-Hoc information distribution in crowd management scenarios

Leveraging human mobility in smartphone based Ad-Hoc information distribution in crowd management scenarios

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

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