Privacy preserving crowd estimation for safer cities

Longo, Salvatore; Cheng, Bin

doi:10.1145/2800835.2801631

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…As an example of a service-defined data analytics, we consider crowd mobility analytics [12] that estimates the crowd levels within an area and the flow of people moving between areas. Data sources (e.g., Wi-Fi sniffers, bluetooth beacons, ambient sensors such as temperature sensors) generate observation of the real world.…”

Section: Iot Platform Capabilitiesmentioning

confidence: 99%

Embracing the Future Internet of Things

Cirillo

Solmaz³

et al. 2019

Sensors

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All of the objects in the real world are envisioned to be connected and/or represented, through an infrastructure layer, in the virtual world of the Internet, becoming Things with status information. Services are then using the available data from this Internet-of-Things (IoT) for various social and economical benefits which explain its extreme broad usage in very heterogeneous fields. Domain administrations of diverse areas of application developed and deployed their own IoT systems and services following disparate standards and architecture approaches that created a fragmentation of things, infrastructures and services in vertical IoT silos. Coordination and cooperation among IoT systems are the keys to build “smarter” IoT services boosting the benefits magnitude. This article analyses the technical trends of the future IoT world based on the current limitations of the IoT systems and the capability requirements. We propose a hyper-connected IoT framework in which “things” are connected to multiple interdependent services and describe how this framework enables the development of future applications. Moreover, we discuss the major limitations in today’s IoT and highlight the required capabilities in the future. We illustrate this global vision with the help of two concrete instances of the hyper-connected IoT in smart cities and autonomous driving scenarios. Finally, we analyse the trends in the number of connected “things” and point out open issues and future challenges. The proposed hyper-connected IoT framework is meant to scale the benefits of IoT from local to global.

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Section: Iot Platform Capabilitiesmentioning

confidence: 99%

Embracing the Future Internet of Things

Cirillo

Solmaz³

et al. 2019

Sensors

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“…The main drawback is that people with multiple devices may be counted more than once while people who are in the area but are not connected to any network activity cannot be counted [ 31 ]. Another family of approaches is based on carbon dioxide (CO 2 ) sensors [ 32 ]. CO 2 is produced by the human respiratory system; hence, the measure of CO 2 in indoor environments can be an indicator of occupancy [ 33 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

Monti

Tse

Tang

et al. 2022

Sensors

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Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.

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“…Hence, monitoring and counting these Wi-Fi frames can provide support in people counting and crowd monitoring [28]. A different process, based on the use of a single carbon dioxide sensor (CO 2 ), has also been investigated [29]. The idea behind this approach is that an indoor environment is affected by human activities and that influence can be measured by various sensors, so as to infer the density of the crowd, as described in [30], or with the use of hybrid techniques, by combining different sensors such as video camera and CO 2 sensors, as proposed in [31].…”

Section: Iot Infrastructures For Classroom Occupancy Monitoringmentioning

confidence: 99%

On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

et al. 2021

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In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations).

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Privacy preserving crowd estimation for safer cities

Cited by 10 publications

References 4 publications

Embracing the Future Internet of Things

Embracing the Future Internet of Things

Edge-Based Transfer Learning for Classroom Occupancy Detection in a Smart Campus Context

On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

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