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Mobile Crowd Sensing and Computing: New Approaches and Applications (Volume II)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 5848

Special Issue Editor

AttlanTTIC Research Center, University of Vigo, 36310 Vigo, Spain
Interests: distributed and collaborative data analysis; fog computing; IoT; outlier detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technological developments have allowed humans and our devices to be considered as moving sensors. These devices include smartphones and tablets, but also the increasingly popular wearables, such as smartwatches. As these devices are always interconnected, they are a constant source of information. If appropriately processed, this information brings, without a doubt, interesting knowledge with great potential applications in different fields. The data collected by these devices are very diverse, but they are usually classified into two types. On the one hand, there is the information generated automatically by the sensors integrated into our devices, such as location, noise level or movement. On the other hand, there is information that is explicitly created and shared by the users, such as posts on social media. When users are proactive about generating and sharing information, they participate in participatory crowdsensing. When there is no need for the user’s active intervention and the information can be collected even without their explicit knowledge, we refer to this as opportunistic crowdsensing. Concern about managing user data and guaranteeing their privacy has increased in tandem with the growing popularity of these sensorized devices.

The aim of this Special Issue is to provide a global vision of the state-of-the-art in this field. It includes the description of the technology involved in data collection, their (sometimes distributed) processing and its potential applications in different fields. Topics of interest include, but are not limited to, the following:

  • Sensing on the move
  • Human sensors
  • Distributed data analysis: architectures and algorithms
  • Mobile crowd sensing: applications
  • Mobile crowd sensing: privacy

Prof. Dr. Rebeca P. Díaz Redondo
Guest Editor

Manuscript Submission Information

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Keywords

  • mobile sensing
  • human sensors
  • distributed data analysis: architectures and algorithms
  • mobile crowd sensing: applications
  • mobile crowd sensing: privacy

Published Papers (2 papers)

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Research

15 pages, 4234 KiB  
Article
A Convolutional Neural-Network-Based Training Model to Estimate Actual Distance of Persons in Continuous Images
by Yu-Shiuan Tsai, Alvin V. Modales and Hung-Ta Lin
Sensors 2022, 22(15), 5743; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155743 - 01 Aug 2022
Cited by 5 | Viewed by 1404
Abstract
Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisions and accidents immediately and is very important in various AI applications. Image-based distance detection usually relies on the correctness of geometric [...] Read more.
Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisions and accidents immediately and is very important in various AI applications. Image-based distance detection usually relies on the correctness of geometric information. However, the geometric features will be lost when the object is rotated or the camera lens image is distorted. This study proposes a training model based on a convolutional neural network, which uses a single-lens camera to estimate humans’ distance in continuous images. We can partially restore depth information loss using built-in camera parameters that do not require additional correction. The normalized skeleton feature unit vector has the same characteristics as time series data and can be classified very well using a 1D convolutional neural network. According to our results, the accuracy for the occluded leg image is over 90% at 2 to 3 m, 80% to 90% at 4 m, and 70% at 5 to 6 m. Full article
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27 pages, 2162 KiB  
Article
Intra-Company Crowdsensing: Datafication with Human-in-the-Loop
by Jaroslaw Domaszewicz and Dariusz Parzych
Sensors 2022, 22(3), 943; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030943 - 26 Jan 2022
Cited by 3 | Viewed by 3680
Abstract
Every day employees learn about things happening in their company. This includes plain facts witnessed while on the job, related or not to one’s job responsibilities. Many of these facts, which we call “occurrence data”, are known by employees but remain unknown to [...] Read more.
Every day employees learn about things happening in their company. This includes plain facts witnessed while on the job, related or not to one’s job responsibilities. Many of these facts, which we call “occurrence data”, are known by employees but remain unknown to the company. We suppose that some of them are valuable and may improve the company’s situational awareness. In the spirit of mobile crowdsensing, we propose intra-company crowdsensing (ICC), a method of “extracting” occurrence data from employees. In ICC, an employee occasionally responds to sensing requests, each about one plain fact. We elaborate the concept of ICC, proposing a model of human-system interaction, a system architecture, and an organizational process. We position ICC with respect to related concepts from information technology, and we look at it from selected organizational and managerial viewpoints. Finally, we conducted a survey, in which we presented the concept of ICC to employees of different companies and asked for their evaluation. Respondents positive about ICC outnumbered skeptics by a wide margin. The survey also revealed some concerns, mostly related to ICC being perceived as another employee surveillance tool. However, useful and acceptable sensing requests are likely to be found in every organization. Full article
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