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 6491

Special Issue Editors


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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

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

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 (4 papers)

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Research

22 pages, 916 KiB  
Article
Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data
by S. M. Nuruzzaman Nobel, Shirin Sultana, Sondip Poul Singha, Sudipto Chaki, Md. Julkar Nayeen Mahi, Tony Jan, Alistair Barros and Md Whaiduzzaman
Information 2024, 15(6), 298; https://doi.org/10.3390/info15060298 - 23 May 2024
Viewed by 731
Abstract
Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by [...] Read more.
Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry. Full article
(This article belongs to the Special Issue Technoeconomics of the Internet of Things)
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21 pages, 1356 KiB  
Article
Technoeconomic Analysis for Deployment of Gait-Oriented Wearable Medical Internet-of-Things Platform in Catalonia
by Marc Codina, David Castells-Rufas, Maria-Jesus Torrelles and Jordi Carrabina
Information 2024, 15(5), 288; https://doi.org/10.3390/info15050288 - 18 May 2024
Viewed by 503
Abstract
The Internet of Medical Things (IoMT) extends the concept of eHealth and mHealth for patients with continuous monitoring requirements. This research concentrates on the use of wearable devices based on the use of inertial measurement units (IMUs) that account for a gait analysis [...] Read more.
The Internet of Medical Things (IoMT) extends the concept of eHealth and mHealth for patients with continuous monitoring requirements. This research concentrates on the use of wearable devices based on the use of inertial measurement units (IMUs) that account for a gait analysis for its use in three health cases, equilibrium evaluation, fall prevention and surgery recovery, that impact a large elderly population. We also analyze two different scenarios for data capture: supervised by clinicians and unsupervised during activities of daily life (ADLs). The continuous monitoring of patients produces large amounts of data that are analyzed in specific IoMT platforms that must be connected to the health system platforms containing the health records of the patients. The aim of this study is to evaluate the factors that impact the cost of the deployment of such an IoMT solution. We use population data from Catalonia together with an IoMT deployment model for costs from the current deployment of connected devices for monitoring diabetic patients. Our study reveals the critical dependencies of the proposed IoMT platforms: from the devices and cloud cost, the size of the population using these services and the savings from the current model under key parameters such as fall reduction or rehabilitation duration. Future research should investigate the benefit of continuous monitoring in improving the quality of life of patients. Full article
(This article belongs to the Special Issue Technoeconomics of the Internet of Things)
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17 pages, 456 KiB  
Article
Cloud Broker: Customizing Services for Cloud Market Requirements
by Evangelia Filiopoulou, Georgios Chatzithanasis, Christos Michalakelis and Mara Nikolaidou
Information 2024, 15(4), 232; https://doi.org/10.3390/info15040232 - 19 Apr 2024
Viewed by 927
Abstract
Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that [...] Read more.
Cloud providers offer various purchasing options to enable users to tailor their costs according to their specific requirements, including on-demand, reserved instances, and spot instances. On-demand and spot instances satisfy short-term workloads, whereas reserved instances fulfill long-term instances. However, there are workloads that fall outside of either long-term or short-term categories. Consequently, there is a notable absence of services specifically tailored for medium-term workloads. On-demand services, while offering flexibility, often come with high costs. Spot instances, though cost-effective, carry the risk of termination. Reserved instances, while stable and less expensive, may have a remaining period that extends beyond the duration of users’ tasks. This gap underscores the need for solutions that address the unique requirements and challenges associated with medium-term workloads in the cloud computing landscape. This paper introduces a new cloud broker that introduces IaaS services for medium-term workloads. On one hand, this broker strategically reserves resources from providers, and on the other hand, it interacts with users. Its interaction with users is twofold. It collects users’ preferences regarding commitment term for medium-term workloads and then transforms the leased resources based on commitment term, aligning with the requirements of most users. To ensure profitability, the broker sells these services utilizing an auction algorithm. Hence, in this paper, an auction algorithm is introduced and developed, which treats cloud services as virtual assets and integrates the depreciation over time. The findings affirm the lack of services that fulfill medium workloads while ensuring the financial viabilty and profitability of the broker, given that the estimated return on investment (ROI) is acceptable. Full article
(This article belongs to the Special Issue Technoeconomics of the Internet of Things)
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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 - 8 Oct 2023
Cited by 1 | Viewed by 3479
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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