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Technoeconomics of the Internet of Things
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Technoeconomics of the Internet of Things

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3299

Special Issue Editors

Dr. Christos Michalakelis

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 9, Omirou Str., Tavros, 17778 Athens, Greece
Interests: technoeconomics; ICT markets; IoT
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mara Nikolaidou

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 9, Omirou Str., 17778 Athens, Tavros
Interests: digital libraries & repositories; system integration; knowledge management and ontologies; system modelling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Evangelia Filiopoulou

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, Athens, Greece
Interests: technoeconomics; ICT markets; IoT

Special Issue Information

Dear Colleagues,

The MDPI Information Journal invites submissions for a Special Issue on the “Technoeconomics of the Internet of Things”.

The Internet of Things has been around for a while; however, its core components are only now becoming more accessible to consumers, which has made this technology incredibly appealing, globally. This evolution raises the need for studying the corresponding IoT market, from a technoeconomic standpoint and in terms of emerging business models, as well as financial, economic, cost benefit, risk and uncertainty, etc., analysis, while developing new pricing schemes, revenue and brokering models and market mechanism design.

Moreover, collecting data from a variety of IoT sources, combining that information with data from other sources and applying big data analytics are all steps that can be taken to arrive at decisions and take actions that have the potential to have significant economic, social, ecological and environmental implications. 

The successful deployment of IoT depends on its economic viability; thus, the technoeconomic analysis of IoT is a topic that holds important research attention. The technoeconomic analysis could promote potential economic possibilities, obstacles, operation objectives for process improvement and acknowledge further research requirements of the IoT ecosystem.

The aim of this Special Issue, “Technoeconomics of the Internet of Things", is to attract original and innovative research results from the application of technoeconomic assessment to the Internet of Things. 

Topics of interest include, but are not limited to, the following:

  • Technoeconomic assessment.
  • Cost and capital considerations: capital expenditures (CAPEX), operational expenditures (OPEX), total cost of ownership (TCO), etc.
  • Business case assessment.
  • Business models and strategies.
  • Financial, economic, cost benefit, etc. models and analysis.
  • Uncertainty and risk analysis.
  • Pricing schemes and revenue models.
  • Economic efficiency.
  • Decision support.
  • Market mechanisms, auctions models, etc.
  • Data analytics.

Dr. Christos Michalakelis
Prof. Dr. Mara Nikolaidou
Dr. Evangelia Filiopoulou
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. Information is an international peer-reviewed open access monthly 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 1600 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

  • Internet of Things
  • technoeconomics
  • technoeconomic assessment
  • business models
  • IoT costing
  • IoT pricing
  • risk analysis
  • economic efficiency
  • decision support
  • market mechanisms
  • data analytics

Published Papers (1 paper)

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Research

20 pages, 3858 KiB  
Article
Customer Shopping Behavior Analysis Using RFID and Machine Learning Models
by Ganjar Alfian, Muhammad Qois Huzyan Octava, Farhan Mufti Hilmy, Rachma Aurya Nurhaliza, Yuris Mulya Saputra, Divi Galih Prasetyo Putri, Firma Syahrian, Norma Latif Fitriyani, Fransiskus Tatas Dwi Atmaji, Umar Farooq, Dat Tien Nguyen and Muhammad Syafrudin
Information 2023, 14(10), 551; https://doi.org/10.3390/info14100551 - 08 Oct 2023
Cited by 1 | Viewed by 2667
Abstract
Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID [...] Read more.
Analyzing customer shopping habits in physical stores is crucial for enhancing the retailer–customer relationship and increasing business revenue. However, it can be challenging to gather data on customer browsing activities in physical stores as compared to online stores. This study suggests using RFID technology on store shelves and machine learning models to analyze customer browsing activity in retail stores. The study uses RFID tags to track product movement and collects data on customer behavior using receive signal strength (RSS) of the tags. The time-domain features were then extracted from RSS data and machine learning models were utilized to classify different customer shopping activities. We proposed integration of iForest Outlier Detection, ADASYN data balancing and Multilayer Perceptron (MLP). The results indicate that the proposed model performed better than other supervised learning models, with improvements of up to 97.778% in accuracy, 98.008% in precision, 98.333% in specificity, 98.333% in recall, and 97.750% in the f1-score. Finally, we showcased the integration of this trained model into a web-based application. This result can assist managers in understanding customer preferences and aid in product placement, promotions, and customer recommendations. Full article
(This article belongs to the Special Issue Technoeconomics of the Internet of Things)
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