Submit to Special Issue Submit Abstract to Special Issue Review for Information Propose a Special Issue

Journal Menu

Journal Browser

Technoeconomics of the Internet of Things

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 16020

Share This Special Issue

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 547 KiB

Open AccessArticle

Cybersecurity as a Contributor Toward Resilient Internet of Things (IoT) Infrastructure and Sustainable Economic Growth

by Georgia Dede, Anastasia Maria Petsa, Stelios Kavalaris, Emmanouil Serrelis, Spyridon Evangelatos, Ioannis Oikonomidis and Thomas Kamalakis

Information 2024, 15(12), 798; https://doi.org/10.3390/info15120798 - 11 Dec 2024

Viewed by 996

Abstract

This paper investigates the contribution of the various cybersecurity domains to the United Nations’ Sustainable Development Goals (SDGs), emphasizing the critical role of cybersecurity in advancing sustainable economic growth and resilient IoT infrastructure. The paper also examines specific use cases on how cybersecurity measures and practices can contribute to achieving SDG 8 and SDG 9 focused on decent work and economic growth and industry, innovations, and infrastructure. In the context of SDG 8 the use case of a smart agriculture network was examined, whereas for SDG 9, the use case focuses on a smart factory processing raw materials. An analysis of the prioritization of the several cybersecurity domains following the MoSCoW method is also presented. This paper offers valuable insights and guidance for enhancing corporate resilience and economic benefits in the Internet of Things (IoT) aligning with the SDGs and contributing to a more sustainable and resilient future for the IoT. Full article

(This article belongs to the Special Issue Technoeconomics of the Internet of Things)

► Show Figures

Figure 1

27 pages, 426 KiB

Open AccessArticle

Choosing and Evaluating P2P Lending with Value Engineering as a Decision Support System: An Indonesian Case Study

by Sen Yung, Armein Z. R. Langi, Arry Akhmad Arman and Togar M. Simatupang

Information 2024, 15(9), 544; https://doi.org/10.3390/info15090544 - 5 Sep 2024

Viewed by 1230

Abstract

Peer-to-peer (P2P) lending has gained significant traction in the financial landscape, particularly in developing economies such as Indonesia, where access to traditional banking services remains a challenge for many. The aim of this study is to investigate the application of value engineering as a decision support system for choosing and evaluating P2P lending platforms, using Indonesia as a case study. P2P lending is a relatively new service in the digital economy for lending money to individuals through online financial intermediaries, where borrowers and lenders often have no prior relationship. Value engineering, a systematic approach to improving the value of a product or service, can be a valuable tool in the context of P2P lending. Through applying value engineering principles, P2P lending platforms can identify and prioritize the key factors that influence lending decisions, such as risk, return, and data privacy, to enhance the overall value proposition for both borrowers and lenders. Both value engineering and P2P lending are technoeconomic systems that aim to enhance the overall value and efficiency of a system or process, albeit through different approaches. This study presents a comprehensive framework for applying value engineering as a decision support system for P2P lending in Indonesia. The findings reveal that the value engineering index developed in this study effectively differentiates between P2P lending platforms based on their performance. Specifically, platforms with a high-value index were found to offer competitive interest rates, lower fees, and superior risk management, as evidenced by their non-performing loan (NPL) rates. In contrast, platforms with a low-value index were associated with higher NPLs and less favorable terms for stakeholders. These insights provide practical guidance for P2P lending platforms, regulators, and consumers; highlight the importance of a value engineering approach in optimizing platform selection; and enhance the P2P lending ecosystem’s sustainability in Indonesia. Full article

(This article belongs to the Special Issue Technoeconomics of the Internet of Things)

► Show Figures

Figure 1

22 pages, 916 KiB

Open AccessArticle

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

Cited by 4 | Viewed by 3430

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 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)

► Show Figures

Figure 1

21 pages, 1356 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1111

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 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)

► Show Figures

Figure 1

17 pages, 456 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1652

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 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)

► Show Figures

Figure 1

20 pages, 3858 KiB

Open AccessArticle

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 8 | Viewed by 5911

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 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)

► Show Figures

Journal Menu

Journal Browser

Technoeconomics of the Internet of Things

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI