The Leverage of Social Media and IoT

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 25788

Special Issue Editors


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Department of Computer Science, Lakehead University, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
Interests: thick data analytics; web mining; learning analytics; social networking; web services; interoperability; software agility development
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science, Lakehead University, ATAC 5013, 955 Oliver Road, Thunder Bay, ON P7B 5E1, Canada
Interests: internet of medical things; web intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Department of Computer and Information Science, University of Macau, Room 4023, E11, FST Building, Taipa, Macau 999078, China
Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Computing, University of Buckingham, Hunter Street, Buckingham MK18 1EG, UK
Interests: Image Processing (wavelet-based); Feature extraction; Image/video compression, image quality and content-based video retrieval

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Guest Editor
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Shangyuan Cun No. 3, Xizhimen Wai, Beijing 100044, China
Interests: rail transit information technology; software engineering theory and technology; software engineering application; artificial intelligence and application

Special Issue Information

Dear Colleagues,

The emerging technologies are now at the initial stage for maturing socially smart and connected things. This stage requires sociotechnical expertise that combines technological and social solutions to allow users have better control over their environment. Internet of Things (IoT) currently provides enterprises a convenient and efficient way to monitor, listen to, and analyze data gathered from social media without affecting their time and energy. This leverage allows for the realisations of Smart Environments such as Smart Cities and Smart Buildings and brings the promise of an intelligently managed space that maximises the requirements of the user while minimising resources. Social media giants such as Facebook and other organizations are now looking at the coming ten years to connect people to their devices, and gather valuable insights from this connected ecosystem. This Special Issue aims to address the convergence of social connectivity based on the rise of IoT.

Prof. Dr. Jinan Fiaidhi
Prof. Dr. Sabah Mohammed
Dr. Simon Fong
Dr. Naseer Al-Jawad
Dr. Dalin Zhang
Guest Editors

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. IoT is an international peer-reviewed open access quarterly 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 1200 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

  • Commerce of Things
  • Thick Data for Consumer Insights
  • Internet of Things
  • Social Networking
  • Big Data Aggregation and Analysis
  • Smart City applications
  • Digital Marketing
  • Social Internet of Things
  • Technology Convergence
  • Cyber-Physical-Social Systems

Published Papers (3 papers)

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Research

18 pages, 790 KiB  
Article
An Agent-Based Model of Task-Allocation and Resource-Sharing for Social Internet of Things
by Kashif Zia, Umar Farooq, Muhammad Shafi and Muhammad Arshad
IoT 2021, 2(1), 187-204; https://doi.org/10.3390/iot2010010 - 23 Mar 2021
Cited by 1 | Viewed by 3453
Abstract
The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based [...] Read more.
The things in the Internet of Things are becoming more and more socially aware. What social means for these things (more often termed as “social objects”) is predominately determined by how and when objects interact with each other. In this paper, an agent-based model for Social Internet of Things is proposed, which features the realization of various interaction modalities, along with possible network structures and mobility modes, thus providing a novel model to ask interesting “what-if” questions. The scenario used, which is the acquisition of shared resources in a common spatial and temporal world, demands agents to have ad-hoc communication and a willingness to cooperate with others. The model was simulated for all possible combinations of input parameters to study the implications of competitive vs. cooperative social behavior while agents try to acquire shared resources/services in a peer-to-peer fashion. However, the main focus of the paper was to analyze the impact of profile-based mobility, which has an underpinning on parameters of extent and scale of a mobility profile. The simulation results, in addition to others, reveal that there are substantial and systematic differences among different combinations of values for extent and scale. Full article
(This article belongs to the Special Issue The Leverage of Social Media and IoT)
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27 pages, 1780 KiB  
Article
Process Automation in an IoT–Fog–Cloud Ecosystem: A Survey and Taxonomy
by Hossein Chegini, Ranesh Kumar Naha, Aniket Mahanti and Parimala Thulasiraman
IoT 2021, 2(1), 92-118; https://doi.org/10.3390/iot2010006 - 7 Feb 2021
Cited by 81 | Viewed by 11871
Abstract
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on [...] Read more.
The number of IoT sensors and physical objects accommodated on the Internet is increasing day by day, and traditional Cloud Computing would not be able to host IoT data because of its high latency. Being challenged of processing all IoT big data on Cloud facilities, there is not enough study on automating components to deal with the big data and real-time tasks in the IoT–Fog–Cloud ecosystem. For instance, designing automatic data transfer from the fog layer to cloud layer, which contains enormous distributed devices is challenging. Considering fog as the supporting processing layer, dealing with decentralized devices in the IoT and fog layer leads us to think of other automatic mechanisms to manage the existing heterogeneity. The big data and heterogeneity challenges also motivated us to design other automatic components for Fog resiliency, which we address as the third challenge in the ecosystem. Fog resiliency makes the processing of IoT tasks independent to the Cloud layer. This survey aims to review, study, and analyze the automatic functions as a taxonomy to help researchers, who are implementing methods and algorithms for different IoT applications. We demonstrated the automatic functions through our research in accordance to each challenge. The study also discusses and suggests automating the tasks, methods, and processes of the ecosystem that still process the data manually. Full article
(This article belongs to the Special Issue The Leverage of Social Media and IoT)
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22 pages, 2578 KiB  
Article
Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning
by Ravikumar Patel and Kalpdrum Passi
IoT 2020, 1(2), 218-239; https://doi.org/10.3390/iot1020014 - 10 Oct 2020
Cited by 49 | Viewed by 9098
Abstract
In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing [...] Read more.
In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97. Full article
(This article belongs to the Special Issue The Leverage of Social Media and IoT)
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