Future Internet

21 pages, 9280 KiB

Open AccessArticle

Using Metaheuristics (SA-MCSDN) Optimized for Multi-Controller Placement in Software-Defined Networking

by Neamah S. Radam, Sufyan T. Faraj Al-Janabi and Khalid Sh. Jasim

Future Internet 2023, 15(1), 39; https://doi.org/10.3390/fi15010039 - 16 Jan 2023

Cited by 3 | Viewed by 2627

The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, [...] Read more.

The multi-controller placement problem (MCPP) represents one of the most challenging issues in software-defined networks (SDNs). High-efficiency and scalable optimized solutions can be achieved for a given position in such networks, thereby enhancing various aspects of programmability, configuration, and construction. In this paper, we propose a model called simulated annealing for multi-controllers in SDN (SA-MCSDN) to solve the problem of placing multiple controllers in appropriate locations by considering estimated distances and distribution times among the controllers, as well as between controllers and switches (C2S). We simulated the proposed mathematical model using Network Simulator NS3 in the Linux Ubuntu environment to extract the performance results. We then compared the results of this single-solution algorithm with those obtained by our previously proposed multi-solution harmony search particle swarm optimization (HS-PSO) algorithm. The results reveal interesting aspects of each type of solution. We found that the proposed model works better than previously proposed models, according to some of the metrics upon which the network relies to achieve optimal performance. The metrics considered in this work are propagation delay, round-trip time (RTT), matrix of time session (TS), average delay, reliability, throughput, cost, and fitness value. The simulation results presented herein reveal that the proposed model achieves high reliability and satisfactory throughput with a short access time standard, addressing the issues of scalability and flexibility and achieving high performance to support network efficiency. Full article

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14 pages, 1597 KiB

Open AccessArticle

Blockchain, Quo Vadis? Recent Changes in Perspectives on the Application of Technology in Agribusiness

by Geneci da Silva Ribeiro Rocha, Diego Durante Mühl, Hermenegildo Almeida Chingamba, Letícia de Oliveira and Edson Talamini

Future Internet 2023, 15(1), 38; https://doi.org/10.3390/fi15010038 - 16 Jan 2023

Cited by 6 | Viewed by 3032

Abstract

Information technologies such as blockchain are developing fast, overcoming bottlenecks, and quickly taking advantage of their application. The present study analyzes recent changes concerning the benefits, disadvantages, challenges, and opportunities of blockchain applications in agribusiness. Interviews were conducted with and a questionnaire was [...] Read more.

Information technologies such as blockchain are developing fast, overcoming bottlenecks, and quickly taking advantage of their application. The present study analyzes recent changes concerning the benefits, disadvantages, challenges, and opportunities of blockchain applications in agribusiness. Interviews were conducted with and a questionnaire was applied to professionals working in the development and application of blockchain technology in agribusiness, to compare their perception of the recent advances. The results showed that the importance of blockchain technology to improve governance and information flow along supply chains has increased, and this is the main perceived benefit. The main disadvantages were removing intermediaries and the high cost of implementing the technology. The absence of a widely accepted platform in blockchain operations is the leading and growing challenge, while patterns for blockchain technology seem to be being overcome. The integration of blockchain with new technologies, and the competitiveness provided by the technology, are seen as the main and growing opportunities. Despite the study limitations, we conclude that the benefits and opportunities associated with blockchain application in agribusiness outweigh the challenges and disadvantages in number and importance, and are becoming more relevant. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)

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21 pages, 3340 KiB

Open AccessArticle

Cost-Profiling Microservice Applications Using an APM Stack

by Sjouke de Vries, Frank Blaauw and Vasilios Andrikopoulos

Future Internet 2023, 15(1), 37; https://doi.org/10.3390/fi15010037 - 13 Jan 2023

Cited by 1 | Viewed by 3411

Abstract

Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced [...] Read more.

Understanding how the different parts of a cloud-native application contribute to its operating expenses is an important step towards optimizing this cost. However, with the adoption and rollout of microservice architectures, the gathering of the necessary data becomes much more involved and nuanced due to the distributed and heterogeneous nature of these architectures. Existing solutions for this purpose are either closed-source and proprietary or focus only on the infrastructural footprint of the applications. In response to that, in this work, we present a cost-profiling solution aimed at Kubernetes-based microservice applications, building on a popular open-source application performance monitoring (APM) stack. By means of a case study with a data engineering company, we demonstrate how our proposed solution can provide deeper insights into the cost profile of the various application components and drive informed decision-making in managing the deployment of the application. Full article

(This article belongs to the Special Issue Cloud-Native Observability)

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27 pages, 1149 KiB

Open AccessArticle

A Multi-Agent Approach to Binary Classification Using Swarm Intelligence

by Sean Grimes and David E. Breen

Future Internet 2023, 15(1), 36; https://doi.org/10.3390/fi15010036 - 12 Jan 2023

Cited by 1 | Viewed by 2826

Abstract

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a [...] Read more.

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports. Full article

(This article belongs to the Special Issue Modern Trends in Multi-Agent Systems)

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27 pages, 1792 KiB

Open AccessReview

Redactable Blockchain: Comprehensive Review, Mechanisms, Challenges, Open Issues and Future Research Directions

by Shams Mhmood Abd Ali, Mohd Najwadi Yusoff and Hasan Falah Hasan

Future Internet 2023, 15(1), 35; https://doi.org/10.3390/fi15010035 - 12 Jan 2023

Cited by 16 | Viewed by 4571

Abstract

The continuous advancements of blockchain applications impose constant improvements on their technical features. Particularly immutability, a highly secure blockchain attribute forbidding unauthorized or illicit data editing or deletion, which functions as crucial blockchain security. Nonetheless, the security function is currently being challenged due [...] Read more.

The continuous advancements of blockchain applications impose constant improvements on their technical features. Particularly immutability, a highly secure blockchain attribute forbidding unauthorized or illicit data editing or deletion, which functions as crucial blockchain security. Nonetheless, the security function is currently being challenged due to improper data stored, such as child pornography, copyright violation, and lately the enaction of the “Right to be Forgotten (RtbF)” principle disseminated by the General Data Protection Regulation (GDPR), where it requires blockchain data to be redacted to suit current applications’ urgent demands, and even compliance with the regulation is a challenge and an unfeasible practice for various blockchain technology providers owing to the immutability characteristic. To overcome this challenge, mutable blockchain is highly demanded to solve previously mentioned issues, where controlled and supervised amendments to certain content within constrained privileges granted are suggested by several researchers through numerous blockchain redaction mechanisms using chameleon and non-chameleon hashing function approaches, and methods were proposed to achieve reasonable policies while ensuring high blockchain security levels. Accordingly, the current study seeks to thoroughly define redaction implementation challenges and security properties criteria. The analysis performed has mapped these criteria with chameleon-based research methodologies, technical approaches, and the latest cryptographic techniques implemented to resolve the challenge posed by the policy in which comparisons paved current open issues, leading to shaping future research directions in the scoped field. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)

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19 pages, 5776 KiB

Open AccessArticle

Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

by Siyuan Yang, Mondher Bouazizi, Tomoaki Ohtsuki, Yohei Shibata, Wataru Takabatake, Kenji Hoshino and Atsushi Nagate

Future Internet 2023, 15(1), 34; https://doi.org/10.3390/fi15010034 - 12 Jan 2023

Cited by 3 | Viewed by 2197

Abstract

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the [...] Read more.

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep

Q

Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves. Full article

(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Japan 2022-2023)

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15 pages, 1388 KiB

Open AccessArticle

Abstracting Data in Distributed Ledger Systems for Higher Level Analytics and Visualizations

by Leny Vinceslas, Safak Dogan, Srikumar Sundareshwar and Ahmet M. Kondoz

Future Internet 2023, 15(1), 33; https://doi.org/10.3390/fi15010033 - 11 Jan 2023

Cited by 3 | Viewed by 2282

Abstract

By design, distributed ledger technologies persist low-level data, which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide an enriched [...] Read more.

By design, distributed ledger technologies persist low-level data, which makes conducting complex business analysis of the recorded operations challenging. Existing blockchain visualization and analytics tools such as block explorers tend to rely on this low-level data and complex interfacing to provide an enriched level of analytics. The ability to derive richer analytics could be improved through the availability of a higher level abstraction of the data. This article proposes an abstraction layer architecture that enables the design of high-level analytics of distributed ledger systems and the decentralized applications that run on top. Based on the analysis of existing initiatives and identification of the relevant user requirements, this work aims to establish key insights and specifications to improve the auditability and intuitiveness of distributed ledger systems by leveraging the development of future user interfaces. To illustrate the benefits offered by the proposed abstraction layer architecture, a regulated sector use case is explored. Full article

(This article belongs to the Special Issue Security and Privacy in Blockchains and the IoT II)

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Transaction handling in different ledger architectures [<a href="#B10-futureinternet-15-00033" class="html-bibr">10</a>]. (a) Conventional centralized ledger. (b) Decentralized ledger. Full article ">Figure 2
Screen-shots of Hyperledger Explorer [<a href="#B39-futureinternet-15-00033" class="html-bibr">39</a>]. (a) Dashboard main view; (b) block explorer view; and (c) transaction details pop-up window. Full article ">Figure 3
Abstracted visualizations. (a) Transaction-oriented abstraction; and (b) account-oriented abstraction. Full article ">Figure 4
Abstraction layer architecture. Full article ">Figure 5
Application architecture and stack structures. Full article ">Figure 6
RegNet network visualization. The information used to populate the visualization is based on the data of the HF network deployed as a proof-of-concept in the RegNet case scenario. Full article ">Figure 7
Concept design of the network topology view of the RegNet dashboard. From the left to the right column: page selection, news feed, network topology visualization and transaction insights. The analytics used to populate the dashboard are based on the data of the HF network deployed as a proof-of-concept in the RegNet case scenario. Full article ">

21 pages, 7660 KiB

Open AccessArticle

Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data

by Sandulika Abesinghe, Nayomi Kankanamge, Tan Yigitcanlar and Surabhi Pancholi

Future Internet 2023, 15(1), 32; https://doi.org/10.3390/fi15010032 - 9 Jan 2023

Cited by 9 | Viewed by 4119

Abstract

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited [...] Read more.

The image of a city represents the sum of beliefs, ideas, and impressions that people have of that city. Mostly, city images are assessed through direct or indirect interviews and cognitive mapping exercises. Such methods consume more time and effort and are limited to a small number of people. However, recently, people tend to use social media to express their thoughts and experiences of a place. Taking this into consideration, this paper attempts to explore city images through social media big data, considering Colombo, Sri Lanka, as the testbed. The aim of the study is to examine the image of a city through Lynchian elements—i.e., landmarks, paths, nodes, edges, and districts—by using community sentiments expressed and images posted on social media platforms. For that, this study conducted various analyses—i.e., descriptive, image processing, sentiment, popularity, and geo-coded social media analyses. The study findings revealed that: (a) the community sentiments toward the same landmarks, paths, nodes, edges, and districts change over time; (b) decisions related to locating landmarks, paths, nodes, edges, and districts have a significant impact on community cognition in perceiving cities; and (c) geo-coded social media data analytics is an invaluable approach to capture the image of a city. The study informs urban authorities in their placemaking efforts by introducing a novel methodological approach to capture an image of a city. Full article

(This article belongs to the Special Issue Developments of Computer Vision and Image Processing: Methodologies and Applications)

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16 pages, 5311 KiB

Open AccessArticle

Product Evaluation Prediction Model Based on Multi-Level Deep Feature Fusion

by Qingyan Zhou, Hao Li, Youhua Zhang and Junhong Zheng

Future Internet 2023, 15(1), 31; https://doi.org/10.3390/fi15010031 - 9 Jan 2023

Cited by 2 | Viewed by 2006

Abstract

Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propose a product evaluation prediction [...] Read more.

Traditional product evaluation research is to collect data through questionnaires or interviews to optimize product design, but the whole process takes a long time to deploy and cannot fully reflect the market situation. Aiming at this problem, we propose a product evaluation prediction model based on multi-level deep feature fusion of online reviews. It mines product satisfaction from the massive reviews published by users on e-commerce websites, and uses this model to analyze the relationship between design attributes and customer satisfaction, design products based on customer satisfaction. Our proposed model can be divided into the following four parts: First, the DSCNN (Depthwise Separable Convolutions) layer and pooling layer are used to combine extracting shallow features from the primordial data. Secondly, CBAM (Convolutional Block Attention Module) is used to realize the dimension separation of features, enhance the expressive ability of key features in the two dimensions of space and channel, and suppress the influence of redundant information. Thirdly, BiLSTM (Bidirectional Long Short-Term Memory) is used to overcome the complexity and nonlinearity of product evaluation prediction, output the predicted result through the fully connected layer. Finally, using the global optimization capability of the genetic algorithm, the hyperparameter optimization of the model constructed above is carried out. The final forecasting model consists of a series of decision rules that avoid model redundancy and achieve the best forecasting effect. It has been verified that the method proposed in this paper is better than the above-mentioned models in five evaluation indicators such as MSE, MAE, RMSE, MAPE and SMAPE, compared with Support Vector Regression (SVR), DSCNN, BiLSTM and DSCNN-BiLSTM. By predicting customer emotional satisfaction, it can provide accurate decision-making suggestions for enterprises to design new products. Full article

(This article belongs to the Topic Multimodal Sentiment Analysis Based on Deep Learning Methods Such as Convolutional Neural Networks)

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15 pages, 2893 KiB

Open AccessArticle

Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network

by Ke Zhao, Rui Han and Xu Wang

Future Internet 2023, 15(1), 30; https://doi.org/10.3390/fi15010030 - 7 Jan 2023

Cited by 4 | Viewed by 2360

Abstract

The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of [...] Read more.

The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of traffic and a significant amount of network latency. This brings great challenges to 5G-ICN mobile cache. It appears urgent to improve the cache placement strategy. This paper suggests a hybrid caching strategy called time segmentation-based hybrid caching (TSBC) strategy, based on the 5G-ICN bearer network infrastructure. A base station’s access frequency can change throughout the course of the day due to the “tidal phenomena” of mobile networks. To distinguish the access frequency, we split each day into periods of high and low liquidity. To maintain the diversity of cache copies during periods of high liquidity, we replace the path’s least-used cache copy. We determine the cache value of each node in the path and make caching decisions during periods of low liquidity to make sure users can access the content they are most interested in quickly. The simulation results demonstrate that the proposed strategy has a positive impact on both latency and the cache hit ratio. Full article

(This article belongs to the Section Internet of Things)

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19 pages, 1501 KiB

Open AccessArticle

Role of Attention and Design Cues for Influencing Cyber-Sextortion Using Social Engineering and Phishing Attacks

by Brent Pethers and Abubakar Bello

Future Internet 2023, 15(1), 29; https://doi.org/10.3390/fi15010029 - 7 Jan 2023

Cited by 7 | Viewed by 3792

Abstract

Cyber sextortion attacks are security and privacy threats delivered to victims online, to distribute sexual material in order to force the victim to act against their will. This continues to be an under-addressed concern in society. This study investigated social engineering and phishing [...] Read more.

Cyber sextortion attacks are security and privacy threats delivered to victims online, to distribute sexual material in order to force the victim to act against their will. This continues to be an under-addressed concern in society. This study investigated social engineering and phishing email design and influence techniques in susceptibility to cyber sextortion attacks. Using a quantitative methodology, a survey measured susceptibility to cyber sextortion with a focus on four different email design cues. One-way repeated measures ANOVA, post hoc comparison tests, Friedman nonparametric test, and Spearman correlation tests were conducted with results indicating that attention to email source and title/subject line significantly increased individuals’ susceptibility, while attention to grammar and spelling, and urgency cues, had lesser influence. As such, the influence of these message-related factors should be considered when implementing effective security controls to mitigate the risks and vulnerabilities to cyber sextortion attacks. Full article

(This article belongs to the Special Issue Cybersecurity and Cybercrime in the Age of Social Media)

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12 pages, 1103 KiB

Open AccessArticle

Adapting Recommendations on Environmental Education Programs

by Katerina Kabassi, Anastasia Papadaki and Athanasios Botonis

Future Internet 2023, 15(1), 28; https://doi.org/10.3390/fi15010028 - 4 Jan 2023

Cited by 1 | Viewed by 1846

Abstract

Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of [...] Read more.

Stakeholders in Environmental Education (EE) often face difficulties identifying and selecting programs that best suit their needs. This is due, in part, to the lack of expertise in evaluation knowledge and practice, as well as to the absence of a unified database of Environmental Education Programs (EEPs) with a defined structure. This article presents the design and development of a web application for evaluating and selecting EEPs. The certified users of the application can insert, view, and evaluate the registered EEPs. At the same time, the application creates and maintains for each user an individual and dynamic user model reflecting their personal preferences. Finally, using all the above information and applying a combination of Multi-Criteria Decision-Making Methods (MCDM), the application provides a comparative and adaptive evaluation in order to help each user to select the EEPs that best suit his/her needs. The personalized recommendations are based on the information about the user stored in the user model and the results of the EEPs evaluations by the users that have applied them. As a case study, we used the EEPs from the Greek Educational System. Full article

(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)

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21 pages, 14593 KiB

Open AccessArticle

Transfer Functions and Linear Distortions in Ultra-Wideband Channels Faded by Rain in GeoSurf Satellite Constellations

by Emilio Matricciani and Carlo Riva

Future Internet 2023, 15(1), 27; https://doi.org/10.3390/fi15010027 - 3 Jan 2023

Cited by 3 | Viewed by 1917

Abstract

Because of rain attenuation, the equivalent baseband transfer function of large bandwidth radio-links will not be ideal. We report the results concerning radio links to/from satellites orbiting in GeoSurf satellite constellations located at Spino d’Adda, Prague, Madrid, and Tampa, which are all sites [...] Read more.

Because of rain attenuation, the equivalent baseband transfer function of large bandwidth radio-links will not be ideal. We report the results concerning radio links to/from satellites orbiting in GeoSurf satellite constellations located at Spino d’Adda, Prague, Madrid, and Tampa, which are all sites in different climatic regions. By calculating rain attenuation and phase delay with the Synthetic Storm Technique, we have found that in a 10-GHz bandwidth centered at 80 GHz (W-Band)—to which we refer to as “ultra-wideband-, both direct and orthogonal channels will introduce significant amplitude and phase distortions, which increase with rain attenuation. Only “narrow-band” channels (100~200 MHz) will not be affected. The ratio between the probability of bit error with rain attenuation and the probability of bit error with no rain attenuation increases with rain attenuation. The estimated loss in the signal-to-noise ratio can reach 3~4 dB. All results depend on the site, Tampa being the worst. To confirm these findings, future work will need a full Monte Carlo digital simulation. Full article

(This article belongs to the Special Issue 6G Wireless Communication Systems: Applications, Opportunities and Challenges II)

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25 pages, 2214 KiB

Open AccessArticle

Clinical Screening Prediction in the Portuguese National Health Service: Data Analysis, Machine Learning Models, Explainability and Meta-Evaluation

by Teresa Gonçalves, Rute Veladas, Hua Yang, Renata Vieira, Paulo Quaresma, Paulo Infante, Cátia Sousa Pinto, João Oliveira, Maria Cortes Ferreira, Jéssica Morais, Ana Raquel Pereira, Nuno Fernandes and Carolina Gonçalves

Future Internet 2023, 15(1), 26; https://doi.org/10.3390/fi15010026 - 3 Jan 2023

Viewed by 2407

Abstract

This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for [...] Read more.

This paper presents an analysis of the calls made to the Portuguese National Health Contact Center (SNS24) during a three years period. The final goal was to develop a system to help nurse attendants select the appropriate clinical pathway (from 59 options) for each call. It examines several aspects of the calls distribution like age and gender of the user, date and time of the call and final referral, among others and presents comparative results for alternative classification models (SVM and CNN) and different data samples (three months, one and two years data models). For the task of selecting the appropriate pathway, the models, learned on the basis of the available data, achieved F1 values that range between 0.642 (3 months CNN model) and 0.783 (2 years CNN model), with SVM having a more stable performance (between 0.743 and 0.768 for the corresponding data samples). These results are discussed regarding error analysis and possibilities for explaining the system decisions. A final meta evaluation, based on a clinical expert overview, compares the different choices: the nurse attendants (reference ground truth), the expert and the automatic decisions (2 models), revealing a higher agreement between the ML models, followed by their agreement with the clinical expert, and minor agreement with the reference. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Smart Living and Public Health)

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13 pages, 1866 KiB

Open AccessArticle

A V2V Identity Authentication and Key Agreement Scheme Based on Identity-Based Cryptograph

by Qiang Li

Future Internet 2023, 15(1), 25; https://doi.org/10.3390/fi15010025 - 3 Jan 2023

Cited by 8 | Viewed by 2430

Abstract

Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path [...] Read more.

Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path data, user driving habits data, and so on. It is necessary to ensure the opposite equipment is registered C-V2X equipment (installed in the vehicle), and the data transmitted between the equipment is secure. This paper proposes a V2V identity authentication and key agreement scheme based on identity-based cryptograph (IBC). The C-V2X equipment use its vehicle identification (VID) as its public key. The key management center (KMC) generates a private key for the C-V2X equipment according to its VID. The C-V2X equipment transmit secret data encrypted with the opposite equipment public key to the other equipment, they authenticate each other through a challenge response protocol based on identity-based cryptography, and they negotiate the working key used to encrypt the communication data. The scheme can secure the V2V communication with low computational cost and simple architecture and meet the lightweight and efficient communication requirements of the C-V2X system. Full article

(This article belongs to the Special Issue Security for Vehicular Ad Hoc Networks)

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24 pages, 2851 KiB

Open AccessArticle

Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service

by Jinze Du, Chengtai Gao and Tao Feng

Future Internet 2023, 15(1), 24; https://doi.org/10.3390/fi15010024 - 31 Dec 2022

Cited by 6 | Viewed by 4566

Abstract

The Data Distribution Service (DDS) for real-time systems is an industrial Internet communication protocol. Due to its distributed high reliability and the ability to transmit device data communication in real-time, it has been widely used in industry, medical care, transportation, and national defense. [...] Read more.

The Data Distribution Service (DDS) for real-time systems is an industrial Internet communication protocol. Due to its distributed high reliability and the ability to transmit device data communication in real-time, it has been widely used in industry, medical care, transportation, and national defense. With the wide application of various protocols, protocol security has become a top priority. There are many studies on protocol security, but these studies lack a formal security assessment of protocols. Based on the above status, this paper evaluates and improves the security of the DDS protocol using a model detection method combining the Dolev–Yao attack model and the Coloring Petri Net (CPN) theory. Because of the security loopholes in the original protocol, a timestamp was introduced into the original protocol, and the shared key establishment process in the original protocol lacked fairness and consistency. We adopted a new establishment method to establish the shared secret and re-verified its security. The results show that the overall security of the protocol has been improved by 16.7% while effectively preventing current replay attack. Full article

(This article belongs to the Section Internet of Things)

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19 pages, 8158 KiB

Open AccessArticle

A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams

by Barbara Cardone, Ferdinando Di Martino and Vittorio Miraglia

Future Internet 2023, 15(1), 23; https://doi.org/10.3390/fi15010023 - 30 Dec 2022

Cited by 5 | Viewed by 2510

Abstract

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied [...] Read more.

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents’ postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens’ discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density. Full article

(This article belongs to the Collection Innovative People-Centered Solutions Applied to Industries, Cities and Societies)

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20 pages, 9080 KiB

Open AccessArticle

A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting

by Songtao Huang, Jun Shen, Qingquan Lv, Qingguo Zhou and Binbin Yong

Future Internet 2023, 15(1), 22; https://doi.org/10.3390/fi15010022 - 30 Dec 2022

Cited by 9 | Viewed by 2819

Abstract

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional [...] Read more.

Electricity load forecasting has seen increasing importance recently, especially with the effectiveness of deep learning methods growing. Improving the accuracy of electricity load forecasting is vital for public resources management departments. Traditional neural network methods such as long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) have been widely used in electricity load forecasting. However, LSTM and its variants are not sensitive to the dynamic change of inputs and miss the internal nonperiodic rules of series, due to their discrete observation interval. In this paper, a novel neural ordinary differential equation (NODE) method, which can be seen as a continuous version of residual network (ResNet), is applied to electricity load forecasting to learn dynamics of time series. We design three groups of models based on LSTM and BiLSTM and compare the accuracy between models using NODE and without NODE. The experimental results show that NODE can improve the prediction accuracy of LSTM and BiLSTM. It indicates that NODE is an effective approach to improving the accuracy of electricity load forecasting. Full article

(This article belongs to the Special Issue Internet of Things and Internet of Everything: Current Trends, Challenges, and New Perspectives)

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21 pages, 2088 KiB

Open AccessReview

The Emerging Technologies of Digital Payments and Associated Challenges: A Systematic Literature Review

by Khando Khando, M. Sirajul Islam and Shang Gao

Future Internet 2023, 15(1), 21; https://doi.org/10.3390/fi15010021 - 30 Dec 2022

Cited by 32 | Viewed by 61253

Abstract

The interplay between finance and technology with the use of the internet triggered the emergence of digital payment technologies. Such technological innovation in the payment industry is the foundation for financial inclusion. However, despite the continuous progress and potential of moving the payment [...] Read more.

The interplay between finance and technology with the use of the internet triggered the emergence of digital payment technologies. Such technological innovation in the payment industry is the foundation for financial inclusion. However, despite the continuous progress and potential of moving the payment landscape towards digital payments and connecting the population to the ubiquitous digital environment, some critical issues need to be addressed to achieve a more harmonious inclusive and sustainable cashless society. The study aims to provide a comprehensive literature review on the emerging digital payment technologies and associated challenges. By systematically reviewing existing empirical studies, this study puts forward the state-of-the-art classification of digital payment technologies and presents four categories of digital payment technologies: card payment, e-payment,mobile payment and cryptocurrencies. Subsequently, the paper presents the key challenges in digital payment technologies categorized into broad themes: social, economic, technical, awareness and legal. The classification and categorization of payment technologies and associated challenges can be useful to both researchers and practitioners to understand, elucidate and develop a coherent digital payment strategy. Full article

(This article belongs to the Collection Featured Reviews of Future Internet Research)

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18 pages, 326 KiB

Open AccessArticle

Teachers’ Views on Integrating Augmented Reality in Education: Needs, Opportunities, Challenges and Recommendations

by Maria Perifanou, Anastasios A. Economides and Stavros A. Nikou

Future Internet 2023, 15(1), 20; https://doi.org/10.3390/fi15010020 - 29 Dec 2022

Cited by 29 | Viewed by 7038

Abstract

The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in [...] Read more.

The integration of augmented reality (AR) in education is promising since it enhances teaching and offers more engaging and appealing learning experiences. Teachers can have a catalytic role towards the adoption of AR in education; therefore, their perspectives with regard to AR in teaching and learning are very important. The current study explores teachers’ views on the integration of AR in education through an open-ended questionnaire that has been answered by 93 educators worldwide. A set of digital skills that can support student-centered pedagogies in an appropriate infrastructure are the main requirement for effective teaching with AR. Among the perceived benefits and opportunities are interactive teaching and learning, increased interest and engagement, better understanding of complex concepts. As barriers, participants reported the lack of AR educational applications, the cost of buying and maintaining AR equipment and resources, the lack of teachers’ and students’ digital skills, classroom management issues, and security and ethical issues. Moreover, survey participants highlighted the need for raising teachers’ awareness for the added value of AR in education and the need for teachers’ continuous professional development. Implications and future research recommendations on the integration of AR in education are discussed. Full article

(This article belongs to the Special Issue Artificial Neural Networks for Educational Data Mining in Higher Education)

21 pages, 1445 KiB

Open AccessArticle

Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks

by Sarantis Kalafatidis, Sotiris Skaperas, Vassilis Demiroglou, Lefteris Mamatas and Vassilis Tsaoussidis

Future Internet 2023, 15(1), 19; https://doi.org/10.3390/fi15010019 - 29 Dec 2022

Cited by 7 | Viewed by 3232

Abstract

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. [...] Read more.

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed. Full article

(This article belongs to the Special Issue Software-Defined Networking for the Internet of Things)

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3 pages, 162 KiB

Open AccessEditorial

VR, AR, and 3-D User Interfaces for Measurement and Control

by Annalisa Liccardo and Francesco Bonavolontà

Future Internet 2023, 15(1), 18; https://doi.org/10.3390/fi15010018 - 29 Dec 2022

Cited by 1 | Viewed by 1923

Abstract

The topics of virtual, mixed, and extended reality have now become key areas in various fields of scientific and industrial applications, and the interest in them is made tangible by the numerous papers available in the scientific literature. In this regard, the Special [...] Read more.

The topics of virtual, mixed, and extended reality have now become key areas in various fields of scientific and industrial applications, and the interest in them is made tangible by the numerous papers available in the scientific literature. In this regard, the Special Issue “VR, AR, and 3-D User Interfaces for Measurement and Control” received a fair number of varied contributions that analyzed different aspects of the implementation of virtual, mixed, and extended reality systems and approaches in the real world. They range from investigating the requirements of new potential technologies to the prediction verification of the effectiveness and benefits of their use, the analysis of the difficulties of interaction with graphical interfaces to the possibility of performing complex and risky tasks (such as surgical operations) using mixed reality viewers. All contributions were of a high standard and mainly highlight that measurement and control applications based on the new models of interaction with reality are by now increasingly ready to leave laboratory spaces and become objects and features of common life. The significant benefits of this technology will radically change the way we live and interact with information and the reality around us, and it will surely be worthy of further exploration, maybe even in a new Special Issue of Future Internet. Full article

(This article belongs to the Special Issue VR, AR, and 3-D User Interfaces for Measurement and Control)

19 pages, 892 KiB

Open AccessArticle

Drifting Streaming Peaks-Over-Threshold-Enhanced Self-Evolving Neural Networks for Short-Term Wind Farm Generation Forecast

by Yunchuan Liu, Amir Ghasemkhani and Lei Yang

Future Internet 2023, 15(1), 17; https://doi.org/10.3390/fi15010017 - 28 Dec 2022

Cited by 1 | Viewed by 2204

Abstract

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of [...] Read more.

This paper investigates the short-term wind farm generation forecast. It is observed from the real wind farm generation measurements that wind farm generation exhibits distinct features, such as the non-stationarity and the heterogeneous dynamics of ramp and non-ramp events across different classes of wind turbines. To account for the distinct features of wind farm generation, we propose a Drifting Streaming Peaks-over-Threshold (DSPOT)-enhanced self-evolving neural networks-based short-term wind farm generation forecast. Using DSPOT, the proposed method first classifies the wind farm generation data into ramp and non-ramp datasets, where time-varying dynamics are taken into account by utilizing dynamic ramp thresholds to separate the ramp and non-ramp events. We then train different neural networks based on each dataset to learn the different dynamics of wind farm generation by the NeuroEvolution of Augmenting Topologies (NEAT), which can obtain the best network topology and weighting parameters. As the efficacy of the neural networks relies on the quality of the training datasets (i.e., the classification accuracy of the ramp and non-ramp events), a Bayesian optimization-based approach is developed to optimize the parameters of DSPOT to enhance the quality of the training datasets and the corresponding performance of the neural networks. Based on the developed self-evolving neural networks, both distributional and point forecasts are developed. The experimental results show that compared with other forecast approaches, the proposed forecast approach can substantially improve the forecast accuracy, especially for ramp events. The experiment results indicate that the accuracy improvement in a 60 min horizon forecast in terms of the mean absolute error (MAE) is at least

33.6 %

for the whole year data and at least

37 %

for the ramp events. Moreover, the distributional forecast in terms of the continuous rank probability score (CRPS) is improved by at least

35.8 %

for the whole year data and at least

35.2 %

for the ramp events. Full article

(This article belongs to the Special Issue Multi-Agent Deep Reinforcement Learning for Distributed Operation and Control of Microgrids)

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12 pages, 3571 KiB

Open AccessArticle

Narrowband Internet-of-Things to Enhance the Vehicular Communications Performance

by Qadri Hamarsheh, Omar Daoud, Mohammed Baniyounis and Ahlam Damati

Future Internet 2023, 15(1), 16; https://doi.org/10.3390/fi15010016 - 28 Dec 2022

Cited by 7 | Viewed by 2232

Abstract

The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband [...] Read more.

The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband Internet-of-Things to enhance the robustness of the vehicular communication system. Accordingly, the system’s QoS is enhanced. This enhancement is based on proposing two parts to cover the latency and the harmonics issues, in addition to proposing a distributed antenna configuration for the moving vehicles under a machine learning benchmark, which uses the across-entropy algorithm. The proposed environment has been simulated and compared to the state-of-the-art work performance. The simulation results verify the proposed work performance based on three different parameters; namely the latency, the mean squared error rate, and the transmitted signal block error rate. From these results, the proposed work outperforms the literature; at the probability of 10⁻³, the proposed work reduces the peak power deficiency by almost 49%, an extra 23.5% enhancement has been attained from the self-interference cancellation side, and a bit error rate enhancement by a ratio of 31%. Full article

(This article belongs to the Section Internet of Things)

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13 pages, 408 KiB

Open AccessArticle

BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization

by Moreno La Quatra and Luca Cagliero

Future Internet 2023, 15(1), 15; https://doi.org/10.3390/fi15010015 - 27 Dec 2022

Cited by 44 | Viewed by 5121

Abstract

The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of [...] Read more.

The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of room for improvement. In this paper, we present BART-IT, a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a large corpus of Italian-written pieces of text to learn language-specific features and then fine-tuned on several benchmark datasets established for abstractive summarization. The experimental results show that BART-IT outperforms other state-of-the-art models in terms of ROUGE scores in spite of a significantly smaller number of parameters. The use of BART-IT can foster the development of interesting NLP applications for the Italian language. Beyond releasing the model to the research community to foster further research and applications, we also discuss the ethical implications behind the use of abstractive summarization models. Full article

(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in Italy 2022–2023)

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42 pages, 15670 KiB

Open AccessArticle

Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition

by Roberto De Fazio, Vincenzo Mariano Mastronardi, Matteo Petruzzi, Massimo De Vittorio and Paolo Visconti

Future Internet 2023, 15(1), 14; https://doi.org/10.3390/fi15010014 - 27 Dec 2022

Cited by 22 | Viewed by 12964

Abstract

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, [...] Read more.

Human–machine interaction (HMI) refers to systems enabling communication between machines and humans. Systems for human–machine interfaces have advanced significantly in terms of materials, device design, and production methods. Energy supply units, logic circuits, sensors, and data storage units must be flexible, stretchable, undetectable, biocompatible, and self-healing to act as human–machine interfaces. This paper discusses the technologies for providing different haptic feedback of different natures. Notably, the physiological mechanisms behind touch perception are reported, along with a classification of the main haptic interfaces. Afterward, a comprehensive overview of wearable haptic interfaces is presented, comparing them in terms of cost, the number of integrated actuators and sensors, their main haptic feedback typology, and their future application. Additionally, a review of sensing systems that use haptic feedback technologies—specifically, smart gloves—is given by going through their fundamental technological specifications and key design requirements. Furthermore, useful insights related to the design of the next-generation HMI devices are reported. Lastly, a novel smart glove based on thin and conformable AlN (aluminum nitride) piezoelectric sensors is demonstrated. Specifically, the device acquires and processes the signal from the piezo sensors to classify performed gestures through an onboard machine learning (ML) algorithm. Then, the design and testing of the electronic conditioning section of AlN-based sensors integrated into the smart glove are shown. Finally, the architecture of a wearable visual-tactile recognition system is presented, combining visual data acquired by a micro-camera mounted on the user’s glass with the haptic ones provided by the piezoelectric sensors. Full article

(This article belongs to the Section Big Data and Augmented Intelligence)

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20 pages, 5182 KiB

Open AccessArticle

A Cross-Platform Personalized Recommender System for Connecting E-Commerce and Social Network

by Jiaxu Zhao, Binting Su, Xuli Rao and Zhide Chen

Future Internet 2023, 15(1), 13; https://doi.org/10.3390/fi15010013 - 27 Dec 2022

Cited by 4 | Viewed by 3011

Abstract

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information [...] Read more.

In this paper, we build a recommender system for a new study area: social commerce, which combines rich information about social network users and products on an e-commerce platform. The idea behind this recommender system is that a social network contains abundant information about its users which could be exploited to create profiles of the users. For social commerce, the quality of the profiles of potential consumers determines whether the recommender system is a success or a failure. In our work, not only the user’s textual information but also the tags and the relationships between users have been considered in the process of building user profiling model. A topic model has been adopted in our system, and a feedback mechanism also been design in this paper. Then, we apply a collative filtering method and a clustering algorithm in order to obtain a high recommendation accuracy. We do an empirical analysis based on real data collected on a social network and an e-commerce platform. We find that the social network has an impact on e-commerce, so social commerce could be realized. Simulations show that our topic model has a better performance in topic finding, meaning that our profile-building model is suitable for a social commerce recommender system. Full article

(This article belongs to the Section Techno-Social Smart Systems)

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25 pages, 2657 KiB

Open AccessArticle

Pedestrian Simulation with Reinforcement Learning: A Curriculum-Based Approach

by Giuseppe Vizzari and Thomas Cecconello

Future Internet 2023, 15(1), 12; https://doi.org/10.3390/fi15010012 - 27 Dec 2022

Cited by 8 | Viewed by 4605

Abstract

Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to [...] Read more.

Pedestrian simulation is a consolidated but still lively area of research. State of the art models mostly take an agent-based perspective, in which pedestrian decisions are made according to a manually defined model. Reinforcement learning (RL), on the other hand, is used to train an agent situated in an environment how to act so as to maximize an accumulated numerical reward signal (a feedback provided by the environment to every chosen action). We explored the possibility of applying RL to pedestrian simulation. We carefully defined a reward function combining elements related to goal orientation, basic proxemics, and basic way-finding considerations. The proposed approach employs a particular training curriculum, a set of scenarios growing in difficulty supporting an incremental acquisition of general movement competences such as orientation, walking, and pedestrian interaction. The learned pedestrian behavioral model is applicable to situations not presented to the agents in the training phase, and seems therefore reasonably general. This paper describes the basic elements of the approach, the training procedure, and an experimentation within a software framework employing Unity and ML-Agents. Full article

(This article belongs to the Special Issue Modern Trends in Multi-Agent Systems)

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20 pages, 1031 KiB

Open AccessArticle

HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security

by Duc-Minh Ngo, Dominic Lightbody, Andriy Temko, Cuong Pham-Quoc, Ngoc-Thinh Tran, Colin C. Murphy and Emanuel Popovici

Future Internet 2023, 15(1), 9; https://doi.org/10.3390/fi15010009 - 26 Dec 2022

Cited by 14 | Viewed by 3689

Abstract

This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have [...] Read more.

This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks’ security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation’s ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT. Full article

(This article belongs to the Special Issue Anomaly Detection in Modern Networks)

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24 pages, 1214 KiB

Open AccessArticle

Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study

by Tihomir Orehovački, Luka Blašković and Matej Kurevija

Future Internet 2023, 15(1), 8; https://doi.org/10.3390/fi15010008 - 26 Dec 2022

Cited by 6 | Viewed by 6824

Abstract

Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time [...] Read more.

Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time in the blink of an eye. To be consumed by a respective amount of bank clients regularly, mobile banking applications are required to be continuously improved and updated, be in line with recent security standards, and meet quality requirements. This paper tackles the perceived quality of mobile banking applications that are most commonly used in Croatia and has three objectives in that respect. The first one is to identify the extent to which pragmatic and hedonic dimensions of quality contribute to customers’ satisfaction and their behavioral intentions related to the continuous use of mobile banking applications. The second one is to determine if there are significant differences in the perceived quality between users of diverse mobile banking applications as well as between users who belong to different age groups. The last one is to uncover the advantages and disadvantages of evaluated mobile banking applications. For this purpose, an empirical study was carried out, during which data were collected with an online questionnaire. The sample was composed of 130 participants who are representative and regular users of mobile banking applications. The psychometric features of the proposed research model, which represents an interplay of perceived quality attributes, were tested using the partial least squares structural equation modeling (PLS-SEM) method. Differences in the perceived quality among different mobile banking applications and customers of various age groups were explored with Kruskal–Wallis tests. Pros and cons of mobile banking applications were identified with the help of descriptive statistics. Study findings indicate that, in the context of mobile banking applications used in Croatia, feedback quality and responsiveness contribute to the ease of use, usefulness is affected by both ease of use and efficiency, responsiveness has a significant impact on efficiency while ease of use, usefulness, and security of personal data are predictors of customers’ satisfaction which in turn influences their behavioral intentions. While no significant difference exists in the perceived quality of four examined mobile banking applications, we found a significant difference in the perceived quality among three age groups of users of mobile banking applications. The most commonly reported advantages of mobile banking applications were related to facets of their efficiency and usefulness, whereas their main drawback appeared to be the lack of features dealing with the personalization of offered services. The reported and discussed results of an empirical study can be used as a set of guidelines for future advances in the evaluation and design of mobile banking applications. Full article

(This article belongs to the Special Issue Information Networks with Human-Centric AI)

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Future Internet, Volume 15, Issue 1 (January 2023) – 39 articles

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