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Social Sensing
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Social Sensing

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

Deadline for manuscript submissions: closed (30 October 2018) | Viewed by 23507

Special Issue Editor

Prof. Dr. Tarek F. Abdelzaher

E-Mail Website
Guest Editor
Department of Computer Science, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA
Interests: cyber–physical systems; sensor networks; social sensing; big data; Internet of things

Special Issue Information

Dear Colleagues,

The increased popularity of social media, the proliferation of smart phones, and the increased investments in smart city services give rise to social sensing; a paradigm for collecting observations regarding the physical world, either directly from human observers, or by means of crowd-sourcing the data measurement tasks using sensors in the possession of individuals, such as those on phones, wearables, cars, or homes. In such applications, humans act as sensor carriers (e.g., by carrying phones and wearables that offer an array of sensors), sensor operators (e.g., by actively taking pictures with their phones), or as sensors themselves (e.g., by sharing observations on Twitter). The proliferation of sensors in the possession of the average individual, together with the popularity of social networks that allow massive information dissemination, herald an era of social sensing that brings about new research challenges and opportunities in this emerging field. The goal of this Special Issue is to showcase original high quality results from academia, industry, and government that exemplify advances in both theoretical and experimental social sensing research. The scope includes:

  • Theory, algorithms, systems, and experiments pertaining to social sensing
  • Ubiquitous, mobile and pervasive information processing
  • Participatory and opportunistic sensing
  • Urban sensing/monitoring and smart cities
  • Analytical foundations in information theory, knowledge discovery, and signal processing,
  • Data reliability, privacy and security issues.

Prof. Dr. Tarek F. Abdelzaher
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • Social sensing
  • Cyber-physical systems
  • Internet of things
  • Smart cities
  • Mobile crowd-sensing

Published Papers (5 papers)

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Research

11 pages, 535 KiB  
Article
@choo: Tracking Pollen and Hayfever in the UK Using Social Media
by Sophie Cowie, Rudy Arthur and Hywel T. P. Williams
Sensors 2018, 18(12), 4434; https://doi.org/10.3390/s18124434 - 14 Dec 2018
Cited by 10 | Viewed by 4620
Abstract
Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to [...] Read more.
Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to manage their condition and minimise adverse effects. Current pollen forecasts in the UK are based on a sparse network of pollen monitoring stations. Here, we explore the use of “social sensing” (analysis of unsolicited social media content) as an alternative source of pollen and hayfever observations. We use data from the Twitter platform to generate a dynamic spatial map of pollen levels based on user reports of hayfever symptoms. We show that social sensing alone creates a spatiotemporal pollen measurement with remarkable similarity to measurements taken from the established physical pollen monitoring network. This demonstrates that social sensing of pollen can be accurate, relative to current methods, and suggests a variety of future applications of this method to help hayfever sufferers manage their condition. Full article
(This article belongs to the Special Issue Social Sensing)
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14 pages, 1711 KiB  
Article
UISTD: A Trust-Aware Model for Diverse Item Personalization in Social Sensing with Lower Privacy Intrusion
by Hongchen Wu, Mingyang Li and Huaxiang Zhang
Sensors 2018, 18(12), 4383; https://doi.org/10.3390/s18124383 - 11 Dec 2018
Cited by 2 | Viewed by 2455
Abstract
Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats [...] Read more.
Privacy intrusion has become a major bottleneck for current trust-aware social sensing, since online social media allows anybody to largely disclose their personal information due to the proliferation of the Internet of Things (IoT). State-of-the-art social sensing still suffers from severe privacy threats since it collects users’ personal data and disclosure behaviors, which could raise user privacy concerns due to data integration for personalization. In this paper, we propose a trust-aware model, called the User and Item Similarity Model with Trust in Diverse Kinds (UISTD), to enhance the personalization of social sensing while reducing users’ privacy concerns. UISTD utilizes user-to-user similarities and item-to-item similarities to generate multiple kinds of personalized items with common tags. UISTD also applies a modified k-means clustering algorithm to select the core users among trust relationships, and the core users’ preferences and disclosure behaviors will be regarded as the predicted disclosure pattern. The experimental results on three real-world data sets demonstrate that target users are more likely to: (1) follow the core users’ interests on diverse kinds of items and disclosure behaviors, thereby outperforming the compared methods; and (2) disclose more information with lower intrusion awareness and privacy concern. Full article
(This article belongs to the Special Issue Social Sensing)
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17 pages, 1047 KiB  
Article
End-User Feedback on a Low-Cost Portable Air Quality Sensor System—Are We There Yet?
by Johanna Amalia Robinson, David Kocman, Milena Horvat and Alena Bartonova
Sensors 2018, 18(11), 3768; https://doi.org/10.3390/s18113768 - 04 Nov 2018
Cited by 19 | Viewed by 5102
Abstract
Low-cost sensors are a current trend in citizen science projects that focus on air quality. Until now, devices incorporating such sensors have been tested primarily for their technical capabilities and limitations, whereas their usability and acceptability amongst the public rarely goes beyond proof [...] Read more.
Low-cost sensors are a current trend in citizen science projects that focus on air quality. Until now, devices incorporating such sensors have been tested primarily for their technical capabilities and limitations, whereas their usability and acceptability amongst the public rarely goes beyond proof of concept, leaving user experience (UX) unstudied. The authors argue that UX should be taken into account to make sure that products and services are fit for purpose. Nineteen volunteers tested and evaluated a prototype device and provided feedback through semi-structured interviews and during focus group sessions. Their UX was then coded using mixed coding methods regarding device functionality and recommendations for future product development. The results indicate that UX can identify potentially problematic design aspects while giving deeper insights into user needs. For example, UX recognized that one of the most important aspects of user involvement and motivation was successful data harvesting, which frequently failed. This study recommends that future developers of low-cost portable air quality sensor systems prioritize reliable data transmission to minimize data loss. This will ensure an efficient and positive UX that supports user engagement in citizen science based research where collecting sensor-based data is the primary objective. Full article
(This article belongs to the Special Issue Social Sensing)
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21 pages, 3445 KiB  
Article
Using Stigmergy to Distinguish Event-Specific Topics in Social Discussions
by Mario G. C. A. Cimino, Alessandro Lazzeri, Witold Pedrycz and Gigliola Vaglini
Sensors 2018, 18(7), 2117; https://doi.org/10.3390/s18072117 - 02 Jul 2018
Cited by 3 | Viewed by 2768
Abstract
In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in [...] Read more.
In settings wherein discussion topics are not statically assigned, such as in microblogs, a need exists for identifying and separating topics of a given event. We approach the problem by using a novel type of similarity, calculated between the major terms used in posts. The occurrences of such terms are periodically sampled from the posts stream. The generated temporal series are processed by using marker-based stigmergy, i.e., a biologically-inspired mechanism performing scalar and temporal information aggregation. More precisely, each sample of the series generates a functional structure, called mark, associated with some concentration. The concentrations disperse in a scalar space and evaporate over time. Multiple deposits, when samples are close in terms of instants of time and values, aggregate in a trail and then persist longer than an isolated mark. To measure similarity between time series, the Jaccard’s similarity coefficient between trails is calculated. Discussion topics are generated by such similarity measure in a clustering process using Self-Organizing Maps, and are represented via a colored term cloud. Structural parameters are correctly tuned via an adaptation mechanism based on Differential Evolution. Experiments are completed for a real-world scenario, and the resulting similarity is compared with Dynamic Time Warping (DTW) similarity. Full article
(This article belongs to the Special Issue Social Sensing)
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17 pages, 3823 KiB  
Article
Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization
by Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Karina Toscano-Medina, Victor Martinez-Hernandez, Hector Perez-Meana, Jesus Olivares-Mercado and Victor Sanchez
Sensors 2018, 18(5), 1380; https://doi.org/10.3390/s18051380 - 29 Apr 2018
Cited by 48 | Viewed by 7725
Abstract
In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate [...] Read more.
In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ 1 regularization. Full article
(This article belongs to the Special Issue Social Sensing)
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