11,685 research outputs found
Are You in the Line? RSSI-based Queue Detection in Crowds
Crowd behaviour analytics focuses on behavioural characteristics of groups of
people instead of individuals' activities. This work considers human queuing
behaviour which is a specific crowd behavior of groups. We design a
plug-and-play system solution to the queue detection problem based on
Wi-Fi/Bluetooth Low Energy (BLE) received signal strength indicators (RSSIs)
captured by multiple signal sniffers. The goal of this work is to determine if
a device is in the queue based on only RSSIs. The key idea is to extract
features not only from individual device's data but also mobility similarity
between data from multiple devices and mobility correlation observed by
multiple sniffers. Thus, we propose single-device feature extraction,
cross-device feature extraction, and cross-sniffer feature extraction for model
training and classification. We systematically conduct experiments with
simulated queue movements to study the detection accuracy. Finally, we compare
our signal-based approach against camera-based face detection approach in a
real-world social event with a real human queue. The experimental results
indicate that our approach can reach minimum accuracy of 77% and it
significantly outperforms the camera-based face detection because people block
each other's visibility whereas wireless signals can be detected without
blocking.Comment: This work has been partially funded by the European Union's Horizon
2020 research and innovation programme within the project "Worldwide
Interoperability for SEmantics IoT" under grant agreement Number 72315
Group-In: Group Inference from Wireless Traces of Mobile Devices
This paper proposes Group-In, a wireless scanning system to detect static or
mobile people groups in indoor or outdoor environments. Group-In collects only
wireless traces from the Bluetooth-enabled mobile devices for group inference.
The key problem addressed in this work is to detect not only static groups but
also moving groups with a multi-phased approach based only noisy wireless
Received Signal Strength Indicator (RSSIs) observed by multiple wireless
scanners without localization support. We propose new centralized and
decentralized schemes to process the sparse and noisy wireless data, and
leverage graph-based clustering techniques for group detection from short-term
and long-term aspects. Group-In provides two outcomes: 1) group detection in
short time intervals such as two minutes and 2) long-term linkages such as a
month. To verify the performance, we conduct two experimental studies. One
consists of 27 controlled scenarios in the lab environments. The other is a
real-world scenario where we place Bluetooth scanners in an office environment,
and employees carry beacons for more than one month. Both the controlled and
real-world experiments result in high accuracy group detection in short time
intervals and sampling liberties in terms of the Jaccard index and pairwise
similarity coefficient.Comment: This work has been funded by the EU Horizon 2020 Programme under
Grant Agreements No. 731993 AUTOPILOT and No.871249 LOCUS projects. The
content of this paper does not reflect the official opinion of the EU.
Responsibility for the information and views expressed therein lies entirely
with the authors. Proc. of ACM/IEEE IPSN'20, 202
Traffic Instabilities in Self-Organized Pedestrian Crowds
In human crowds as well as in many animal societies, local interactions among
individuals often give rise to self-organized collective organizations that
offer functional benefits to the group. For instance, flows of pedestrians
moving in opposite directions spontaneously segregate into lanes of uniform
walking directions. This phenomenon is often referred to as a smart collective
pattern, as it increases the traffic efficiency with no need of external
control. However, the functional benefits of this emergent organization have
never been experimentally measured, and the underlying behavioral mechanisms
are poorly understood. In this work, we have studied this phenomenon under
controlled laboratory conditions. We found that the traffic segregation
exhibits structural instabilities characterized by the alternation of organized
and disorganized states, where the lifetime of well-organized clusters of
pedestrians follow a stretched exponential relaxation process. Further analysis
show that the inter-pedestrian variability of comfortable walking speeds is a
key variable at the origin of the observed traffic perturbations. We show that
the collective benefit of the emerging pattern is maximized when all
pedestrians walk at the average speed of the group. In practice, however, local
interactions between slow- and fast-walking pedestrians trigger global
breakdowns of organization, which reduce the collective and the individual
payoff provided by the traffic segregation. This work is a step ahead toward
the understanding of traffic self-organization in crowds, which turns out to be
modulated by complex behavioral mechanisms that do not always maximize the
group's benefits. The quantitative understanding of crowd behaviors opens the
way for designing bottom-up management strategies bound to promote the
emergence of efficient collective behaviors in crowds.Comment: Article published in PLoS Computational biology. Freely available
here:
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100244
Toward understanding crowd mobility in smart cities through the Internet of Things
Understanding crowd mobility behaviors would be a key enabler for crowd management in smart cities, benefiting various sectors such as public safety, tourism and transportation. This article discusses the existing challenges and the recent advances to overcome them and allow sharing information across stakeholders of crowd management through Internet of Things (IoT) technologies. The article proposes the usage of the new federated interoperable semantic IoT platform (FIESTA-IoT), which is considered as "a system of systems". The platform can support various IoT applications for crowd management in smart cities. In particular, the article discusses two integrated IoT systems for crowd mobility: 1) Crowd Mobility Analytics System, 2) Crowd Counting and Location System (from the SmartSantander testbed). Pilot studies are conducted in Gold Coast, Australia and Santander, Spain to fulfill various requirements such as providing online and offline crowd mobility analyses with various sensors in different regions. The analyses provided by these systems are shared across applications in order to provide insights and support crowd management in smart city environments.The pilot study in Gold Coast is conducted in collaboration with NEC Australia. This work has been partially funded by the Spanish Government (MINECO) under Grant Agreement No. TEC2015-71329-C2-1-R ADVICE (Dynamic Provisioning of Connectivity in High Density 5G Wireless Scenarios) project and by the EU Horizon 2020 Programme under Grant Agreements No. 731993 AUTOPILOT (Automated Driving Progressed by Internet Of Things), 643943 FIESTAIoT (Federated Interoperable Semantic IoT Testbeds and Applications),
and 643275 FESTIVAL (Federated Interoperable Smart ICT Services Development and Testing Platforms)
projects and the joint project by NEC Laboratories Europe and Technische Universität Dortmund. The content of this paper does not reflect the official opinion of the Spanish Government or European Union. Responsibility for the information and views expressed therein lies entirely with the authors
A privacy-aware crowd management system for smart cities and smart buildings
Cities are growing at a dizzying pace and they require improved methods to manage crowded areas. Crowd management stands for the decisions and actions taken to supervise and control densely populated spaces and it involves multiple challenges, from recognition and assessment to application of actions tailored to the current situation. To that end, Wi-Fi-based monitoring systems have emerged as a cost-effective solution for the former one. The key challenge that they impose is the requirement to handle large datasets and provide results in near real-time basis. However, traditional big data and event processing approaches have important shortcomings while dealing with crowd management information. In this paper, we describe a novel system architecture for real-time crowd recognition for smart cities and smart buildings that can be easily replicated. The described system proposes a privacy-aware platform that enables the application of artificial intelligence mechanisms to assess crowds' behavior in buildings employing sensed Wi-Fi traces. Furthermore, the present paper shows the implementation of the system in two buildings, an airport and a market, as well as the results of applying a set of classification algorithms to provide crowd management information.This work was supported in part by the Spanish Government (MINECO) by means of the Project Future Internet Enabled Resilient CitiEs (FIERCE) under Grant RTI2018-093475-A-I00, and in part by the European Union’s Horizon 2020 Programme through the European project Federated CPS Digital Innovation Hubs for the Smart Anything Everywhere Initiative (FED4SAE) under Grant 761708
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