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Mobile Crowd Sensing and Computing: New Approaches and Applications
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Mobile Crowd Sensing and Computing: New Approaches and Applications

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 13112

Special Issue Editor

Prof. Dr. Rebeca P. Díaz Redondo

E-Mail Website
Guest 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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (5 papers)

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Research

21 pages, 1005 KiB  
Article
QSMVM: QoS-Aware and Social-Aware Multimetric Routing Protocol for Video-Streaming Services over MANETS
by Efraín Palacios Jara, Ahmad Mohamad Mezher, Mónica Aguilar Igartua, Rebeca P. Díaz Redondo and Ana Fernández-Vilas
Sensors 2021, 21(3), 901; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030901 - 29 Jan 2021
Cited by 11 | Viewed by 2272
Abstract
A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Real-time multimedia services, such as video-streaming over MANETs, offers very promising applications, e.g., two members of a [...] Read more.
A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Real-time multimedia services, such as video-streaming over MANETs, offers very promising applications, e.g., two members of a group of tourists who want to share a video transmitted through the MANET they form, a video-streaming service deployed over a MANET where users watch a film, among other examples. On the other hand, social web technologies, where people actively interact online with others through social networks, are leading to a socialization of networks. Information of interaction among users is being used to provide socially-enhanced software. To achieve this, we need to know the strength of the relationship between a given user and each user they interact with. This strength of the relationship can be measured through a concept called tie strength (TS), first introduced by Mark Granovetter in 1973. In this article, we modify our previous proposal named multipath multimedia dynamic source routing (MMDSR) protocol to include a social metric TS in the decisions taken by the forwarding algorithm. We find a trade-off between the quality of service (QoS) and the trust level between users who form the forwarding path in the MANET. Our goal is to increase the trust metric while the QoS is not affected significantly. Full article
(This article belongs to the Special Issue Mobile Crowd Sensing and Computing: New Approaches and Applications)
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15 pages, 1465 KiB  
Article
Trace2trace—A Feasibility Study on Neural Machine Translation Applied to Human Motion Trajectories
by Alessandro Crivellari and Euro Beinat
Sensors 2020, 20(12), 3503; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123503 - 21 Jun 2020
Cited by 12 | Viewed by 2833
Abstract
Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring [...] Read more.
Neural machine translation is a prominent field in the computational linguistics domain. By leveraging the recent developments of deep learning, it gave birth to powerful algorithms for translating text from one language to another. This study aims to assess the feasibility of transferring the neural machine translation approach into a completely different context, namely human mobility and trajectory analysis. Building a conceptual parallelism between sentences (sequences of words) and motion traces (sequences of locations), we aspire to translate individual trajectories generated by a certain category of users into the corresponding mobility traces potentially generated by a different category of users. The experiment is inserted in the background of tourist mobility analysis, with the goal of translating the motion behavior of tourists belonging to a specific nationality into the motion behavior of tourists belonging to a different nationality. The model adopted is based on the seq2seq approach and consists of an encoder–decoder architecture based on long short-term memory (LSTM) neural networks and neural embeddings. The encoder turns an input location sequence into a corresponding hidden vector; the decoder reverses the process, turning the vector into an output location sequence. The proposed framework, tested on a real-world large-scale dataset, explores an effective attempt of motion transformation between different entities, arising as a potentially powerful source of mobility information disclosure, especially in the context of crowd management and smart city services. Full article
(This article belongs to the Special Issue Mobile Crowd Sensing and Computing: New Approaches and Applications)
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24 pages, 1662 KiB  
Article
Differentially Private Mobile Crowd Sensing Considering Sensing Errors
by Yuichi Sei and Akihiko Ohsuga
Sensors 2020, 20(10), 2785; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102785 - 14 May 2020
Cited by 7 | Viewed by 2488
Abstract
An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has [...] Read more.
An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered. Full article
(This article belongs to the Special Issue Mobile Crowd Sensing and Computing: New Approaches and Applications)
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19 pages, 5949 KiB  
Article
Research and Application of Underground WLAN Adaptive Radio Fingerprint Database
by Jiansheng Qian and Mingzhi Song
Sensors 2020, 20(4), 1182; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041182 - 21 Feb 2020
Cited by 5 | Viewed by 1989
Abstract
Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious [...] Read more.
Fingerprint positioning based on WiFi in coal mines has received much attention because of the widespread application of WiFi. Fingerprinting techniques have developed rapidly due to the efforts of many researchers. However, the off-line construction of the radio fingerprint database is a tedious and time-consuming process. When the underground environments change, it may be necessary to update the signal received signal strength indication (RSSI) of all reference points, which will affect the normal working of a personnel positioning system. To solve this problem, an adaptive construction and update method based on a quantum-behaved particle swarm optimization–user-location trajectory feedback (QPSO–ULTF) for a radio fingerprint database is proposed. The principle of ULTF is that the mobile terminal records and uploads the related dataset in the process of user’s walking, and it forms the user-location track with RSSI through the analysis and processing of the positioning system server. QPSO algorithm is used for the optimal radio fingerprint match between the RSSI of the access point (AP) contained in the dataset of user-location track and the calibration samples to achieve the adaptive generation and update of the radio fingerprint samples. The experimental results show that the radio fingerprint database generated by the QPSO–ULTF is similar to the traditional radio fingerprint database in the statistical distribution characteristics of the signal received signal strength (RSS) at each reference point. Therefore, the adaptive radio fingerprint database can replace the traditional radio fingerprint database. The comparable results of well-known traditional positioning methods demonstrate that the radio fingerprint database generated or updated by the QPSO–ULTF has a good positioning effect, which can ensure the normal operation of a personnel positioning system. Full article
(This article belongs to the Special Issue Mobile Crowd Sensing and Computing: New Approaches and Applications)
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15 pages, 4524 KiB  
Article
Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles
by Raj Bridgelall and Denver Tolliver
Sensors 2020, 20(2), 409; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020409 - 11 Jan 2020
Cited by 11 | Viewed by 2677
Abstract
Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. [...] Read more.
Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates. Full article
(This article belongs to the Special Issue Mobile Crowd Sensing and Computing: New Approaches and Applications)
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