Big IoT and Social Networking Data for Smart Cities - Algorithmic Improvements on Big Data Analysis in the Context of RADICAL City Applications

Psomakelis, Evangelos; Aisopos, Fotis; Λίτκε, Αντώνιος; Tserpes, Konstantinos; Kardara, Magdalini; Campo, Pablo Martínez

doi:10.5220/0005934503960405

Cited by 24 publications

(11 citation statements)

References 14 publications

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“…Therefore leveraging humans themselves arose as an exciting approach to fetch raw data from humans’ mind/virtual world. In this context, humans can be considered as sensors in their increasingly global societal environment, and are regarded as such in the literature [ 17 , 18 ]. Indeed, by analysing human activities in social media, for instance, it is possible to infer the emotions and mood of people while they consume sensed data and services [ 19 ].…”

Section: Dealing With Heterogeneity In Iotmentioning

confidence: 99%

“…Psomakelis et al [ 18 ] introduce a platform labelled RADICAL that combines citizens’ posts retrieved through smartphone applications and social networks for smart city services. RADICAL enables to collect, combine, analyse, process, visualise, and provide uniform access to big data sets of social network content, such as tweets, sensor measurements, or citizens’ smartphone reports.…”

Section: The Social Sensing Paradigmmentioning

confidence: 99%

“…On the other hand, the platforms presented in [ 18 , 29 ] are a first approach to the integration of the three sensing approaches. However, the proposed integration is quite limited and cannot offer an appropriate response to IoT latency-sensitive applications [ 49 , 50 ], besides being entirely dependent on Internet connectivity.…”

Section: Open Challenges and Research Opportunitiesmentioning

confidence: 99%

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An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet

Armando

Rodrigues

Pereira

et al. 2018

Sensors

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The Internet keeps changing at a rapid pace, driven mainly by the emerging concepts and applications that make it aware of the physical world and responsive to user context. The Internet of Things (IoT) concept is quickly giving way to more advanced and highly interactive environments that go well beyond the mere sensing of the physical world. Today, in addition to traditional electronic devices, IoT sensing/actuating includes both software and human-based entities. This paper provides an outlook on the future of sensing/actuating approaches on the Internet at large, which we see increasingly related to all kinds of socially interactive technologies. With these objectives in mind, we propose a taxonomy to deal with the heterogeneity of sensing/actuating approaches in IoT. We also analyse the state-of-the-art of Social Sensing. Finally, we identify open issues and associated research opportunities, the main ones being the integration of all sensing approaches, the combination of social sciences, engineering, and computing as enablers of context-aware, cognitive applications and, last but not least, the unified management of large sets of very heterogeneous sensors/actuators.

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Section: Dealing With Heterogeneity In Iotmentioning

confidence: 99%

Section: The Social Sensing Paradigmmentioning

confidence: 99%

Section: Open Challenges and Research Opportunitiesmentioning

confidence: 99%

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An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet

Armando

Rodrigues

Pereira

et al. 2018

Sensors

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“…These social data created by users when they interact with social media channels are usually known as digital footprints [4]. The full potential of digital footprints for generating insights about users comes from their combination with other sources, such as bank transactions [5], sensor information [6], or other social networks [3]. Several works have used many techniques for user profile matching across social networks, such as user profile similarity [7,8] as well as based on spatial, temporal, and content similarity [9].…”

Section: Introductionmentioning

confidence: 99%

Predicting Reputation in the Sharing Economy with Twitter Social Data

Prada

Iglesias

2020

Applied Sciences

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In recent years, the sharing economy has become popular, with outstanding examples such as Airbnb, Uber, or BlaBlaCar, to name a few. In the sharing economy, users provide goods and services in a peer-to-peer scheme and expose themselves to material and personal risks. Thus, an essential component of its success is its capability to build trust among strangers. This goal is achieved usually by creating reputation systems where users rate each other after each transaction. Nevertheless, these systems present challenges such as the lack of information about new users or the reliability of peer ratings. However, users leave their digital footprints on many social networks. These social footprints are used for inferring personal information (e.g., personality and consumer habits) and social behaviors (e.g., flu propagation). This article proposes to advance the state of the art on reputation systems by researching how digital footprints coming from social networks can be used to predict future behaviors on sharing economy platforms. In particular, we have focused on predicting the reputation of users in the second-hand market Wallapop based solely on their users’ Twitter profiles. The main contributions of this research are twofold: (a) a reputation prediction model based on social data; and (b) an anonymized dataset of paired users in the sharing economy site Wallapop and Twitter, which has been collected using the user self-mentioning strategy.

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“…Common examples are city traffic or emotional status of people [20]. Devices can have more than one sensor, effectively requiring multidimensional data management [8].…”

Section: Introductionmentioning

confidence: 99%

An efficient real-time architecture for collecting IoT data

Loria¹,

Toja²,

Carchiolo³

et al. 2017

Annals of Computer Science and Information Systems

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Abstract-IoT applications has some characteristics that set it apart from other fields mainly due to the multitude of different types of sensors producing data. In monitoring applications, data processing requires real-time or soft real-time responses in order to aid systems to make important decisions but also predictive analysis to leverage the potential of IoT by data mining vast datasets. This paper presents an architecture developed to efficiently process and store data coming from an huge number of distributed IoT sensors. The back-end of SeeYourBox services is currently based on the proposed architecture that has proven to be stable and meet all the requirements.

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Big IoT and Social Networking Data for Smart Cities - Algorithmic Improvements on Big Data Analysis in the Context of RADICAL City Applications

Cited by 24 publications

References 14 publications

An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet

An Outlook on Physical and Virtual Sensors for a Socially Interactive Internet

Predicting Reputation in the Sharing Economy with Twitter Social Data

An efficient real-time architecture for collecting IoT data

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