Using Mobile Technology and a Participatory Sensing Approach for Crowd Monitoring and Management During Large-Scale Mass Gatherings

Wirz, Martin; Mitleton–Kelly, Eve; Franke, Tobias; Camilleri, Vanessa; Montebello, Matthew; Roggen, Daniel; Lukowicz, Paul; Tröster, Gerhard

doi:10.1007/978-3-642-36614-7_4

Cited by 11 publications

(9 citation statements)

References 17 publications

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“…For example, in Nericell [19], the research combines the speed information from community to determine the traffic delay and honking levels of users. Similarly, Martin Wirz et al [32] utilize location information including GPS positioning and WiFi/GSM-fingerprinting from all festival attendee to measure the crowd situation around users. The measuring scale is in large area while cannot reach high accuracy of the users surrounding context.…”

Section: Context Awareness For Mobile Computing Sensingmentioning

confidence: 99%

Mobile crowd-sensing context aware based fine-grained access control mode

Mei

Shang

et al. 2015

Multimed Tools Appl

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The present of smart mobile devices have provided unprecedented flexibility to humankind, with which people are able to access kinds of system resource through internet everywhere, including confidential data, nevertheless. While the traditional computing environment is always considered to be static and security-guarded, the context of mobile computing is much more variable, complex, and risk-hidden. To provide appropriate protection on mobile devices, we proposed a context-aware model combined with crowd-sensing paradigm to achieve fine-grained measurement of user's current context. Corresponding to the context-aware model, we categorize the context by kinds of attributes and proposed Attributetree based Context-Aware Access Control model to protect user's privacy and confidential information. The experimental result indicates that our proposed model is fine-grained, efficient and flexible to apply to different mobile platforms.

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Section: Context Awareness For Mobile Computing Sensingmentioning

confidence: 99%

Mobile crowd-sensing context aware based fine-grained access control mode

Mei

Shang

et al. 2015

Multimed Tools Appl

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“…Moreover, through the user interaction with a mobile device, so-called virtual and social sensors can be defined to detect active applications, user activities, social network connections, privacy preferences, etc., but also to provide user-generated content (videos, photos, sounds, texts, speech messages) referenced in space and time. All these sensors enable crowdsourcing approaches for sharing data about the environment among users (Dobre & Xhafa, 2014;Reddy et al, 2009;Wirz et al, 2013;.…”

Section: Participatory Sensing For Semantics Of Mobilitymentioning

confidence: 99%

“…For example, in some situations the task of automatic place labeling can be considered as a multi-class classification task (Do & Gatica-Perez, 2014). Moreover, the aggregation and clustering of trajectories (e.g., see Dodge et al, 2012) are of importance to understand the meaning behind the trajectories and try to correlate them with people's trends, habits and behavior patterns, or to understand the behavior of crowds (Wirz et al, 2013). According to Pejovic and Musolesi (2013), ''As devices become increasingly intelligent, their capabilities evolve beyond inferring context to anticipating it.''…”

Section: Analysis and Mining Of Semantic Mobility Datamentioning

confidence: 99%

“…with others, but also share such information/ knowledge through geo-social networks (Shankar, Huang, Castro, Nath, & Iftode, 2012;Vicente, Freni, Bettini, & Jensen, 2011), social media services, and geospatial information services, enabling a broad range of applications and services, such as urban dynamic mining, public safety, and environment monitoring. The exchange of sensor and user-generated data and the provision of Volunteered Geographic Information (VGI) (Sui, Elwood, & Goodchild, 2012) emphasize a collaborative participatory sensing and crowdsourcing environment (Reddy et al, 2009;Wirz et al, 2013) that is expected to play a key role in this context.…”

Section: Introductionmentioning

confidence: 99%

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Semantic management of moving objects: A vision towards smart mobility

Ilarri

Stojanović

Ray

2015

Expert Systems with Applications

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. b s t r a c tThis position paper presents our vision for the semantic management of moving objects. We argue that exploiting semantic techniques in mobility data management can bring valuable benefits to many domains characterized by the mobility of users and moving objects in general, such as traffic management, urban dynamics analysis, ambient assisted living, emergency management, m-health, etc. We present the state-of-the-art in the domain of management of semantic locations and trajectories, and outline research challenges that need to be investigated to enable a full-fledged and intelligent semantic management of moving objects and location-based services that support smarter mobility. We propose a distributed framework for the semantic enrichment and management of mobility data and analyze the potential deployment and exploitation of such a framework.

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“…By the large usability of smart electronics devices and different non-visual sensors, so that non-visual sensing-based research persons have been done their research on crowd analysis [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Crowd Counting and Profiling Through Visual and Non-Visual Sensors

Gupta¹,

Gupta²

2019

Acta inform. Malays.

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In this paper we present a comparative critical study of visual and non-visual sensors used in crowd behavior analysis. The understanding of crowd has main impact of the analysis how much they support the system is the key factor of the analysis. The surveillance and security prospects are the main feature for crowd that can make system easy and gives better result. The visual sensors that have been widely used are wireless sensor network, computer vision, smart camera, sensor fusion and few more; and the non-visual sensors are regarding the call, IEEE 802, 11 signals measurement, Smart Evactrack, Social Network and Bluetooth etc. This comparative study identified the different analysis of crowd behavior and after analysis we show which technique is better to another one. The smart devices are used now days for surveillance and gives better result in crowd behavior analysis.

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Using Mobile Technology and a Participatory Sensing Approach for Crowd Monitoring and Management During Large-Scale Mass Gatherings

Cited by 11 publications

References 17 publications

Mobile crowd-sensing context aware based fine-grained access control mode

Mobile crowd-sensing context aware based fine-grained access control mode

Semantic management of moving objects: A vision towards smart mobility

A Comparative Study of Crowd Counting and Profiling Through Visual and Non-Visual Sensors

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