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Future Internet, Volume 15, Issue 12 (December 2023) – 36 articles

Cover Story (view full-size image): The interconnected nature of IoT systems enables the collection and exchange of data via sensors, actuators, and software from various components such as physical devices, vehicles, etc. These systems have achieved seamless connectivity requirements using the next-generation wireless network infrastructures. Open RAN (O-RAN) promotes flexibility and intelligence in the next-generation RAN. Our work reviews the applications of O-RAN in supporting the next-generation smart IoT systems by conducting a thorough survey. We propose a generic problem space, which consists of IoT systems (transportation, industry, etc.), targets (reliable communication, real-time analytics), and artificial intelligence and machine learning (AI/ML). We also outline future research directions concerning robust and scalable solutions, interoperability and standardization, privacy, and security. View this paper
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28 pages, 505 KiB  
Article
HeFUN: Homomorphic Encryption for Unconstrained Secure Neural Network Inference
Future Internet 2023, 15(12), 407; https://doi.org/10.3390/fi15120407 - 18 Dec 2023
Viewed by 1274
Abstract
Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by three inherent limitations. Firstly, they cannot evaluate non-linear functions such [...] Read more.
Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by three inherent limitations. Firstly, they cannot evaluate non-linear functions such as ReLU, the most widely adopted activation function in neural networks. Secondly, the permitted number of homomorphic operations on ciphertexts is bounded, consequently limiting the depth of neural networks that can be evaluated. Thirdly, the computational overhead associated with HE is prohibitively high, particularly for deep neural networks. In this paper, we introduce a novel paradigm designed to address the three limitations of HE-based SNNI. Our approach is an interactive approach that is solely based on HE, called iLHE. Utilizing the idea of iLHE, we present two protocols: ReLU, which facilitates the direct evaluation of the ReLU function on encrypted data, tackling the first limitation, and HeRefresh, which extends the feasible depth of neural network computations and mitigates the computational overhead, thereby addressing the second and third limitations. Based on HeReLU and HeRefresh protocols, we build a new framework for SNNI, named HeFUN. We prove that our protocols and the HeFUN framework are secure in the semi-honest security model. Empirical evaluations demonstrate that HeFUN surpasses current HE-based SNNI frameworks in multiple aspects, including security, accuracy, the number of communication rounds, and inference latency. Specifically, for a convolutional neural network with four layers on the MNIST dataset, HeFUN achieves 99.16% accuracy with an inference latency of 1.501 s, surpassing the popular HE-based framework CryptoNets proposed by Gilad-Bachrach, which achieves 98.52% accuracy with an inference latency of 3.479 s. Full article
(This article belongs to the Section Cybersecurity)
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20 pages, 1518 KiB  
Article
Decentralized Storage with Access Control and Data Persistence for e-Book Stores
Future Internet 2023, 15(12), 406; https://doi.org/10.3390/fi15120406 - 18 Dec 2023
Viewed by 1141
Abstract
The e-book services we use today have a serious drawback in that we will no longer be able to read the books we have purchased when the service is terminated. One way to solve this problem is to build a decentralized system that [...] Read more.
The e-book services we use today have a serious drawback in that we will no longer be able to read the books we have purchased when the service is terminated. One way to solve this problem is to build a decentralized system that does not depend on a specific company or organization by combining smart contracts running on the Ethereum blockchain and distributed storage such as an IPFS. However, a simple combination of existing technologies does not make the stored e-book data persistent, so the risk of purchased e-books becoming unreadable remains. In this paper, we propose a decentralized distributed storage called d-book-repository, which has both access management function and data durability for purchased e-books. This system uses NFTs as access rights to realize strict access control by preventing clients who do not have NFTs from downloading e-book data. In addition, e-book data stored on storage nodes in the distributed storage is divided into shards using Reed–Solomon codes, and each storage node stores only a single shard, thereby preventing the creation of nodes that can restore the entire content from locally stored data. The storage of each shard is not handled by a single node but by a group of nodes, and the shard is propagated to all nodes in the group using the gossip protocol, where erasure codes are utilized to increase the resilience against node departure. Furthermore, an incentive mechanism to encourage participation as a storage node is implemented using smart contracts. We built a prototype of the proposed system on AWS and evaluated its performance. The results showed that both downloading and uploading 100 MB of e-book data (equivalent to one comic book) were completed within 10 s using an instance type of m5.xlarge. This value is only 1.3 s longer for downloading and 2.2 s longer for uploading than the time required for a simple download/upload without access control, confirming that the overhead associated with the proposed method is sufficiently small. Full article
(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)
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22 pages, 864 KiB  
Article
Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
Future Internet 2023, 15(12), 405; https://doi.org/10.3390/fi15120405 - 16 Dec 2023
Viewed by 1406
Abstract
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but [...] Read more.
Machine learning (ML) models are essential to securing communication networks. However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output. Adversarial training is an effective defense method against such attacks but relies on access to a substantial number of AEs, a prerequisite that entails significant computational resources and the inherent limitation of poor performance on clean data. To address these problems, this study proposes a novel approach to improve the robustness of ML-based network traffic classification models by integrating derived variables (DVars) into training. Unlike adversarial training, our approach focuses on enhancing training using DVars, introducing randomness into the input data. DVars are generated from the baseline dataset and significantly improve the resilience of the model to AEs. To evaluate the effectiveness of DVars, experiments were conducted using the CSE-CIC-IDS2018 dataset and three state-of-the-art ML-based models: decision tree (DT), random forest (RF), and k-neighbors (KNN). The results show that DVars can improve the accuracy of KNN under attack from 0.45% to 0.84% for low-intensity attacks and from 0.32% to 0.66% for high-intensity attacks. Furthermore, both DT and RF achieve a significant increase in accuracy when subjected to attack of different intensity. Moreover, DVars are computationally efficient, scalable, and do not require access to AEs. Full article
(This article belongs to the Special Issue Privacy and Authentication for Communication Networks)
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28 pages, 3646 KiB  
Article
Blockchain in Agriculture to Ensure Trust, Effectiveness, and Traceability from Farm Fields to Groceries
Future Internet 2023, 15(12), 404; https://doi.org/10.3390/fi15120404 - 16 Dec 2023
Cited by 1 | Viewed by 1400
Abstract
Despite its status as one of the most ancient sectors worldwide, agriculture continues to be a fundamental cornerstone of the global economy. Nevertheless, it faces obstacles such as a lack of trust, difficulties in tracking, and inefficiencies in managing the supply chain. This [...] Read more.
Despite its status as one of the most ancient sectors worldwide, agriculture continues to be a fundamental cornerstone of the global economy. Nevertheless, it faces obstacles such as a lack of trust, difficulties in tracking, and inefficiencies in managing the supply chain. This article examines the potential of blockchain technology (BCT) to alter the agricultural industry by providing a decentralized, transparent, and unchangeable solution to meet the difficulties it faces. The initial discussion provides an overview of the challenges encountered by the agricultural industry, followed by a thorough analysis of BCT, highlighting its potential advantages. Following that, the article explores other agricultural uses for blockchain technology, such as managing supply chains, verifying products, and processing payments. In addition, this paper examines the constraints and challenges related to the use of blockchain technology in agriculture, including issues such as scalability, legal frameworks, and interoperability. This paper highlights the potential of BCT to transform the agricultural industry by offering a transparent and secure platform for managing the supply chain. Nevertheless, it emphasizes the need for involving stakeholders, having clear legislation, and possessing technical skills in order to achieve effective implementation. This work utilizes a systematic literature review using the PRISMA technique and applies meta-analysis as the research methodology, enabling a thorough investigation of the present information available. The results emphasize the significant and positive effect of BCT on agriculture, emphasizing the need for cooperative endeavors among governments, industry pioneers, and technology specialists to encourage its extensive implementation and contribute to the advancement of a sustainable and resilient food system. Full article
(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)
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54 pages, 1251 KiB  
Review
Federated Learning for Intrusion Detection Systems in Internet of Vehicles: A General Taxonomy, Applications, and Future Directions
Future Internet 2023, 15(12), 403; https://doi.org/10.3390/fi15120403 - 14 Dec 2023
Viewed by 4693
Abstract
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across [...] Read more.
In recent years, the Internet of Vehicles (IoV) has garnered significant attention from researchers and automotive industry professionals due to its expanding range of applications and services aimed at enhancing road safety and driver/passenger comfort. However, the massive amount of data spread across this network makes securing it challenging. The IoV network generates, collects, and processes vast amounts of valuable and sensitive data that intruders can manipulate. An intrusion detection system (IDS) is the most typical method to protect such networks. An IDS monitors activity on the road to detect any sign of a security threat and generates an alert if a security anomaly is detected. Applying machine learning methods to large datasets helps detect anomalies, which can be utilized to discover potential intrusions. However, traditional centralized learning algorithms require gathering data from end devices and centralizing it for training on a single device. Vehicle makers and owners may not readily share the sensitive data necessary for training the models. Granting a single device access to enormous volumes of personal information raises significant privacy concerns, as any system-related problems could result in massive data leaks. To alleviate these problems, more secure options, such as Federated Learning (FL), must be explored. A decentralized machine learning technique, FL allows model training on client devices while maintaining user data privacy. Although FL for IDS has made significant progress, to our knowledge, there has been no comprehensive survey specifically dedicated to exploring the applications of FL for IDS in the IoV environment, similar to successful systems research in deep learning. To address this gap, we undertake a well-organized literature review on IDSs based on FL in an IoV environment. We introduce a general taxonomy to describe the FL systems to ensure a coherent structure and guide future research. Additionally, we identify the relevant state of the art in FL-based intrusion detection within the IoV domain, covering the years from FL’s inception in 2016 through 2023. Finally, we identify challenges and future research directions based on the existing literature. Full article
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22 pages, 2427 KiB  
Article
Energy Consumption Reduction in Wireless Sensor Network-Based Water Pipeline Monitoring Systems via Energy Conservation Techniques
Future Internet 2023, 15(12), 402; https://doi.org/10.3390/fi15120402 - 14 Dec 2023
Viewed by 1093
Abstract
In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for [...] Read more.
In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for frequent battery replacement. Given that sensor nodes in such applications are typically battery-powered and often physically inaccessible, maximizing energy efficiency by minimizing unnecessary energy consumption is of vital importance. This paper presents an experimental study that investigates the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling, on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. A custom sensor node is designed using the ESP32 MCU and nRF24L01+ transceiver. Hierarchical sensing is implemented through the use of LSM9DS1 and ADXL344 accelerometers, distributed computing through the implementation of a distributed Kalman filter, and duty cycling through the implementation of interrupt-enabled sleep/wakeup functionality. The experimental results reveal that combining distributed computing, hierarchical sensing and duty cycling reduces energy consumption by a factor of eight compared to the lone implementation of distributed computing. Full article
(This article belongs to the Section Internet of Things)
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13 pages, 290 KiB  
Article
Distributed Denial of Service Classification for Software-Defined Networking Using Grammatical Evolution
Future Internet 2023, 15(12), 401; https://doi.org/10.3390/fi15120401 - 13 Dec 2023
Viewed by 989
Abstract
Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, [...] Read more.
Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2GEN exhibit average class errors of 12.51% and 15.86%, respectively. Full article
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22 pages, 1538 KiB  
Review
A Survey on Blockchain-Based Federated Learning
Future Internet 2023, 15(12), 400; https://doi.org/10.3390/fi15120400 - 12 Dec 2023
Viewed by 1342
Abstract
Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data [...] Read more.
Federated learning (FL) and blockchains exhibit significant commonality, complementarity, and alignment in various aspects, such as application domains, architectural features, and privacy protection mechanisms. In recent years, there have been notable advancements in combining these two technologies, particularly in data privacy protection, data sharing incentives, and computational performance. Although there are some surveys on blockchain-based federated learning (BFL), these surveys predominantly focus on the BFL framework and its classifications, yet lack in-depth analyses of the pivotal issues addressed by BFL. This work aims to assist researchers in understanding the latest research achievements and development directions in the integration of FL with blockchains. Firstly, we introduced the relevant research in FL and blockchain technology and highlighted the existing shortcomings of FL. Next, we conducted a comparative analysis of existing BFL frameworks, delving into the significant problems in the realm of FL that the combination of blockchain and FL addresses. Finally, we summarized the application prospects of BFL technology in various domains such as the Internet of Things, Industrial Internet of Things, Internet of Vehicles, and healthcare services, as well as the challenges that need to be addressed and future research directions. Full article
(This article belongs to the Special Issue Blockchain and Web 3.0: Applications, Challenges and Future Trends)
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17 pages, 888 KiB  
Article
Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
Future Internet 2023, 15(12), 399; https://doi.org/10.3390/fi15120399 - 11 Dec 2023
Cited by 1 | Viewed by 940
Abstract
Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the [...] Read more.
Three-dimensional object detection involves estimating the dimensions, orientations, and locations of 3D bounding boxes. Intersection of Union (IoU) loss measures the overlap between predicted 3D box and ground truth 3D bounding boxes. The localization task uses smooth-L1 loss with IoU to estimate the object’s location, and the classification task identifies the object/class category inside each 3D bounding box. Localization suffers a performance gap in cases where the predicted and ground truth boxes overlap significantly less or do not overlap, indicating the boxes are far away, and in scenarios where the boxes are inclusive. Existing axis-aligned IoU losses suffer performance drop in cases of rotated 3D bounding boxes. This research addresses the shortcomings in bounding box regression problems of 3D object detection by introducing an Improved Intersection Over Union (IIoU) loss. The proposed loss function’s performance is experimented on LiDAR-based and Camera-LiDAR-based fusion methods using the KITTI dataset. Full article
(This article belongs to the Special Issue State-of-the-Art Future Internet Technology in USA 2022–2023)
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43 pages, 3225 KiB  
Review
Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions
Future Internet 2023, 15(12), 398; https://doi.org/10.3390/fi15120398 - 09 Dec 2023
Cited by 2 | Viewed by 4920
Abstract
The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling [...] Read more.
The concept of smart cities, which aim to enhance the quality of urban life through innovative technologies and policies, has gained significant momentum in recent years. As we approach the era of next-generation smart cities, it becomes crucial to explore the key enabling technologies that will shape their development. This work reviews the leading technologies driving the future of smart cities. The work begins by introducing the main requirements of different smart city applications; then, the enabling technologies are presented. This work highlights the transformative potential of the Internet of things (IoT) to facilitate data collection and analysis to improve urban infrastructure and services. As a complementary technology, distributed edge computing brings computational power closer to devices, reducing the reliance on centralized data centers. Another key technology is virtualization, which optimizes resource utilization, enabling multiple virtual environments to run efficiently on shared hardware. Software-defined networking (SDN) emerges as a pivotal technology that brings flexibility and scalability to smart city networks, allowing for dynamic network management and resource allocation. Artificial intelligence (AI) is another approach for managing smart cities by enabling predictive analytics, automation, and smart decision making based on vast amounts of data. Lastly, the blockchain is introduced as a promising approach for smart cities to achieve the required security. The review concludes by identifying potential research directions to address the challenges and complexities brought about by integrating these key enabling technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Blockchain Technology for Smart Cities)
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31 pages, 5689 KiB  
Article
Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks
Future Internet 2023, 15(12), 397; https://doi.org/10.3390/fi15120397 - 09 Dec 2023
Viewed by 1293
Abstract
The rapid evolution of the Internet of Everything (IoE) has significantly enhanced global connectivity and multimedia content sharing, simultaneously escalating the unauthorized distribution of multimedia content, posing risks to intellectual property rights. In 2022 alone, about 130 billion accesses to potentially non-compliant websites [...] Read more.
The rapid evolution of the Internet of Everything (IoE) has significantly enhanced global connectivity and multimedia content sharing, simultaneously escalating the unauthorized distribution of multimedia content, posing risks to intellectual property rights. In 2022 alone, about 130 billion accesses to potentially non-compliant websites were recorded, underscoring the challenges for industries reliant on copyright-protected assets. Amidst prevailing uncertainties and the need for technical and AI-integrated solutions, this study introduces two pivotal contributions. First, it establishes a novel taxonomy aimed at safeguarding and identifying IoE-based content infringements. Second, it proposes an innovative architecture combining IoE components with automated sensors to compile a dataset reflective of potential copyright breaches. This dataset is analyzed using a Bidirectional Encoder Representations from Transformers-based advanced Natural Language Processing (NLP) algorithm, further fine-tuned by a dense neural network (DNN), achieving a remarkable 98.71% accuracy in pinpointing websites that violate copyright. Full article
(This article belongs to the Section Big Data and Augmented Intelligence)
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22 pages, 511 KiB  
Article
PROFEE: A Probabilistic-Feedback Based Speed Rate Adaption for IEEE 802.11bc
Future Internet 2023, 15(12), 396; https://doi.org/10.3390/fi15120396 - 09 Dec 2023
Viewed by 913
Abstract
WiFi is a widely used wireless technology for data transmission. WiFi can also play a crucial role in simultaneously broadcasting content to multiple devices in multimedia transmission for venues such as classrooms, theaters, and stadiums, etc. Broadcasting allows for the efficient dissemination of [...] Read more.
WiFi is a widely used wireless technology for data transmission. WiFi can also play a crucial role in simultaneously broadcasting content to multiple devices in multimedia transmission for venues such as classrooms, theaters, and stadiums, etc. Broadcasting allows for the efficient dissemination of information to all devices connected to the network, and it becomes crucial to ensure that the WiFi network has sufficient capacity to transmit broadcast multimedia content without interruptions or delays. However, using WiFi for broadcasting presents challenges that can impact user experience, specifically the difficulty of obtaining real-time feedback from potentially hundreds or thousands of users due to potential collisions of feedback messages. This work focuses on providing accurate feedback to the Access Point about the percentage of users not receiving broadcast traffic correctly so it can adjust its Modulation and Coding Scheme (MCS) while transmitting broadcast multimedia content to many users. The proposed method is comprised of two sequential algorithms. In order to reduce the probability of a collision after transmitting each message, an algorithm searches for the best probability value for users to transmit ACK/NACK messages, depending on whether messages are received correctly or not. This feedback allows the Access Point to estimate the number of STAs correctly/incorrectly receiving the messages being transmitted. A second algorithm uses this estimation so the Access Point can select the best MCS while maintaining the percentage of users not receiving broadcast content correctly within acceptable margins, thus providing users with the best possible content quality. We implemented the proposed method in the ns-3 simulator, and the results show it yields quick, reliable feedback to the Access Point that was then able to adjust to the best possible MCS in only a few seconds, regardless of the user density and dimensions of the scenario. Full article
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21 pages, 5475 KiB  
Article
A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
Future Internet 2023, 15(12), 395; https://doi.org/10.3390/fi15120395 - 07 Dec 2023
Viewed by 1081
Abstract
The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers [...] Read more.
The existing research on dependent task offloading and resource allocation assumes that edge servers can provide computational and communication resources free of charge. This paper proposes a two-stage resource allocation method to address this issue. In the first stage, users incentivize edge servers to provide resources. We formulate the incentive problem in this stage as a multivariate Stackelberg game, which takes into account both computational and communication resources. In addition, we also analyze the uniqueness of the Stackelberg equilibrium under information sharing conditions. Considering the privacy issues of the participants, the research is extended to scenarios without information sharing, where the multivariable game problem is modeled as a partially observable Markov decision process (POMDP). In order to obtain the optimal incentive decision in this scenario, a reinforcement learning algorithm based on the learning game is designed. In the second stage, we propose a greedy-based deep reinforcement learning algorithm that is aimed at minimizing task execution time by optimizing resource and task allocation strategies. Finally, the simulation results demonstrate that the algorithm designed for non-information sharing scenarios can effectively approximate the theoretical Stackelberg equilibrium, and its performance is found to be better than that of the other three benchmark methods. After the allocation of resources and sub-tasks by the greedy-based deep reinforcement learning algorithm, the execution delay of the dependent task is significantly lower than that in local processing. Full article
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19 pages, 869 KiB  
Article
Envisioning Digital Practices in the Metaverse: A Methodological Perspective
Future Internet 2023, 15(12), 394; https://doi.org/10.3390/fi15120394 - 06 Dec 2023
Viewed by 1140
Abstract
Researchers are exploring methods that exploit digital twins as all-purpose abstractions for sophisticated modelling and simulation, bringing elements of the real world into the virtual realm. Digital twins are essential elements of the digital transformation of society, which mostly benefit manufacturing, smart cities, [...] Read more.
Researchers are exploring methods that exploit digital twins as all-purpose abstractions for sophisticated modelling and simulation, bringing elements of the real world into the virtual realm. Digital twins are essential elements of the digital transformation of society, which mostly benefit manufacturing, smart cities, healthcare contexts, and in general systems that include humans in the loop. As the metaverse concept continues to evolve, the line separating the virtual and the real will progressively fade away. Considering the metaverse’s goal to emulate our social reality, it becomes essential to examine the aspects that characterise real-world interaction practices and explicitly model both physical and social contexts. While the unfolding metaverse may reshape these practices in distinct ways from their real-world counterparts, our position is that it is essential to incorporate social theories into the modelling processes of digital twins within the metaverse. In this work, we discuss our perspective by introducing a digital practice model inspired by the theory of social practice. We illustrate this model by exploiting the scenario of a virtual grocery shop designed to help older adults reduce their social isolation. Full article
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36 pages, 3018 KiB  
Review
Information Security Applications in Smart Cities: A Bibliometric Analysis of Emerging Research
Future Internet 2023, 15(12), 393; https://doi.org/10.3390/fi15120393 - 01 Dec 2023
Viewed by 5388
Abstract
This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on information [...] Read more.
This paper aims to analyze the intellectual structure and research fronts in application information security in smart cities to identify research boundaries, trends, and new opportunities in the area. It applies bibliometric analyses to identify the main authors and their influences on information security and the smart city area. Moreover, this analysis focuses on journals indexed in Scopus databases. The results indicate that there is an opportunity for further advances in the adoption of information security policies in government institutions. Moreover, the production indicators presented herein are useful for the planning and implementation of information security policies and the knowledge of the scientific community about smart cities. The bibliometric analysis provides support for the visualization of the leading research technical collaboration networks among authors, co-authors, countries, and research areas. The methodology offers a broader view of the application information security in smart city areas and makes it possible to assist new research that may contribute to further advances. The smart cities topic has been receiving much attention in recent years, but to the best of our knowledge, there is no research on reporting new possibilities for advances. Therefore, this article may contribute to an emerging body of literature that explores the nature of application information security and smart cities research productivity to assist researchers in better understanding the current emerging of the area. Full article
(This article belongs to the Section Cybersecurity)
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5 pages, 212 KiB  
Editorial
Recent Advances in Information-Centric Networks (ICNs)
Future Internet 2023, 15(12), 392; https://doi.org/10.3390/fi15120392 - 01 Dec 2023
Viewed by 1174
Abstract
The great success of the Internet has been essentially based on the simplicity and versatility of its TCP/IP architecture, which imposes almost no restrictions on either the underlying network technology or on the data being transmitted [...] Full article
(This article belongs to the Special Issue Recent Advances in Information-Centric Networks (ICNs))
45 pages, 6018 KiB  
Review
A Comprehensive Survey Exploring the Multifaceted Interplay between Mobile Edge Computing and Vehicular Networks
Future Internet 2023, 15(12), 391; https://doi.org/10.3390/fi15120391 - 30 Nov 2023
Viewed by 4789
Abstract
In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing [...] Read more.
In recent years, the need for computation-intensive applications in mobile networks requiring more storage, powerful processors, and real-time responses has risen substantially. Vehicular networks play an important role in this ecosystem, as they must support multiple services, such as traffic monitoring or sharing of data involving different aspects of the vehicular traffic. Moreover, new resource-hungry applications have been envisaged, such as autonomous driving or in-cruise entertainment, hence making the demand for computation and storage resources one of the most important challenges in vehicular networks. In this context, Mobile Edge Computing (MEC) has become the key technology to handle these problems by providing cloud-like capabilities at the edge of mobile networks to support delay-sensitive and computation-intensive tasks. In the meantime, researchers have envisaged use of onboard vehicle resources to extend the computing capabilities of MEC systems. This paper presents a comprehensive review of the most recent works related to MEC-assisted vehicular networks, as well as vehicle-assisted MEC systems. We illustrate the MEC system architecture and discuss its deployment in vehicular environments, as well as the key technologies to realize this integration. After that, we review the recent literature by identifying three different areas, i.e.: (i) MEC providing additional resources to vehicles (e.g., for task offloading); (ii) MEC enabling innovative vehicular applications (e.g., platooning), and (iii) vehicular networks providing additional resources to MEC systems. Finally, we discuss open challenges and future research directions, addressing the possible interplays between MEC systems and vehicular networks. Full article
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22 pages, 2246 KiB  
Article
ICN-Based Enhanced Content Delivery for CDN
Future Internet 2023, 15(12), 390; https://doi.org/10.3390/fi15120390 - 30 Nov 2023
Viewed by 1076
Abstract
With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most [...] Read more.
With the rapid growth of internet traffic, the traditional host-to-host TCP/IP architecture is subject to many service limitations faced with content-oriented applications. Various novel network architectures have been proposed to solve these limitations, among which Information-Centric Networking (ICN) is one of the most prominent. ICN features the decoupling of content (service) from the physical devices storing (providing) it through location-independent naming, and offers inherent enhancement to network performance, such as multicast and in-network caching. ICN in-network caching has been extensively studied, and we believe that it may also be the main incentive for ISPs to deploy ICN. A CDN (content delivery network) is a typical content-oriented network paradigm that aims to provide the fast delivery of content. In this paper, we leverage the advantages of the in-network caching of ICN to enhance the content delivery efficiency of CDN by integrating ICN as a service. First, we present our design of a content delivery network enhanced with ICN, called IECDN. Additionally, we formulate a mathematical model to optimize the performance of our proposed design and conduct a series of evaluations. The results indicate that our proposed design provides significant performance gains while reducing bandwidth consumption and shows better resilience to traffic surge. Full article
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15 pages, 4892 KiB  
Article
Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
Future Internet 2023, 15(12), 389; https://doi.org/10.3390/fi15120389 - 30 Nov 2023
Viewed by 1082
Abstract
As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber–physical systems (CPS). However, GPS [...] Read more.
As a core component of small unmanned aerial vehicles (UAVs), GPS is playing a critical role in providing localization for UAV navigation. UAVs are an important factor in the large-scale deployment of the Internet of Things (IoT) and cyber–physical systems (CPS). However, GPS is vulnerable to spoofing attacks that can mislead a UAV to fly into a sensitive area and threaten public safety and private security. The conventional spoofing detection methods need too much overhead, which stops efficient detection from working in a computation-constrained UAV and provides an efficient response to attacks. In this paper, we propose a novel approach to obtain a lightweight detection model in the UAV system so that GPS spoofing attacks can be detected from a long distance. With long-short term memory (LSTM), we propose a lightweight detection model on the ground control stations, and then we distill it into a compact size that is able to run in the control system of the UAV with knowledge distillation. The experimental results show that our lightweight detection algorithm runs in UAV systems reliably and can achieve good performance in GPS spoofing detection. Full article
(This article belongs to the Special Issue Securing Big Data Analytics for Cyber-Physical Systems)
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26 pages, 2138 KiB  
Article
Protecting Hybrid ITS Networks: A Comprehensive Security Approach
Future Internet 2023, 15(12), 388; https://doi.org/10.3390/fi15120388 - 30 Nov 2023
Viewed by 1362
Abstract
Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures [...] Read more.
Cooperative intelligent transport systems (C-ITS) continue to be developed to enhance transportation safety and sustainability. However, the communication of vehicle-to-everything (V2X) systems is inherently open, leading to vulnerabilities that attackers can exploit. This represents a threat to all road users, as security failures can lead to privacy violations or even fatalities. Moreover, a high fatality rate is correlated with soft-mobility road users. Therefore, when developing C-ITS systems, it is important to broaden the focus beyond connected vehicles to include soft-mobility users and legacy vehicles. This work presents a new approach developed in the context of emerging hybrid networks, combining intelligent transport systems operating in 5.9 GHz (ITS-G5) and radio-mobile cellular technologies. Two protocols were implemented and evaluated to introduce security guarantees (such as privacy and integrity) in communications within the developed C-ITS hybrid environment. As a result, this work securely integrates G5-connected ITS stations and soft-mobility users through a smartphone application via cellular networks. Commercial equipment was used for this goal, including on-board and roadside units. Computational, transmission and end-to-end latency were used to assess the system’s performance. Implemented protocols introduce an additional 11% end-to-end latency in hybrid communications. Moreover, workflows employing hybrid communications impose, on average, an extra 28.29 ms of end-to-end latency. The proposal shows promise, as it reaches end-to-end times below the latency requirements imposed in most C-ITS use cases. Full article
(This article belongs to the Special Issue Inter-Vehicle Communication Protocols and Their Applications)
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16 pages, 1237 KiB  
Article
Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
Future Internet 2023, 15(12), 387; https://doi.org/10.3390/fi15120387 - 29 Nov 2023
Viewed by 1171
Abstract
The safeguarding of intellectual property and maintaining privacy for video content are closely linked to the effectiveness of security protocols employed in internet streaming platforms. The inadequate implementation of security measures by content providers has resulted in security breaches within entertainment applications, hence [...] Read more.
The safeguarding of intellectual property and maintaining privacy for video content are closely linked to the effectiveness of security protocols employed in internet streaming platforms. The inadequate implementation of security measures by content providers has resulted in security breaches within entertainment applications, hence causing a reduction in the client base. This research aimed to enhance the security measures employed for video content by implementing a multi-key approach for encryption and decryption processes. The aforementioned objective was successfully accomplished through the use of hybrid methodologies, the production of dynamic keys, and the implementation of user-attribute-based techniques. The main aim of the study was to improve the security measures associated with the process of generating video material. The proposed methodology integrates a system of mathematical equations and a pseudorandom key within its execution. This novel approach significantly augments the degree of security the encryption mechanism provides. The proposed methodology utilises a set of mathematical equations that are randomly employed to achieve encryption. Using a random selection procedure contributes to the overall enhancement of the system’s security. The suggested methodology entails the division of the video into smaller entities known as chunks. Following this, every segment is subjected to encryption using unique keys that are produced dynamically in real-time. The proposed methodology is executed via Android platforms. The transmitter application is tasked with the responsibility of facilitating the streaming of the video content, whereas the receiver application serves the purpose of presenting the video to the user. A careful study was conducted to compare and contrast the suggested method with other similar methods that were already in use. The results of the study strongly support the safety and dependability of the procedure that was made available. Full article
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16 pages, 3223 KiB  
Article
Lightweight Privacy-Preserving Remote User Authentication and Key Agreement Protocol for Next-Generation IoT-Based Smart Healthcare
Future Internet 2023, 15(12), 386; https://doi.org/10.3390/fi15120386 - 29 Nov 2023
Cited by 1 | Viewed by 1562
Abstract
The advancement and innovations in wireless communication technologies including the Internet of Things have massively changed the paradigms of health-based services. In particular, during the COVID-19 pandemic, the trends of working from home have been promoted. Wireless body area network technology frameworks help [...] Read more.
The advancement and innovations in wireless communication technologies including the Internet of Things have massively changed the paradigms of health-based services. In particular, during the COVID-19 pandemic, the trends of working from home have been promoted. Wireless body area network technology frameworks help sufferers in remotely obtaining scientific remedies from physicians through the Internet without paying a visit to the clinics. IoT sensor nodes are incorporated into the clinical device to allow health workers to consult the patients’ fitness conditions in real time. Insecure wireless communication channels make unauthorized access to fitness-related records and manipulation of IoT sensor nodes attached to the patient’s bodies possible, as a result of security flaws. As a result, IoT-enabled devices are threatened by a number of well-known attacks, including impersonation, replay, man-in-the-middle, and denial-of-service assaults. Modern authentication schemes do solve these issues, but they frequently involve challenging mathematical concepts that raise processing and transmission costs. In this paper, we propose a lightweight, secure, and efficient symmetric key exchange algorithm and remote user authentication scheme. Our research proposal presents a successful privacy-protecting method for remote users and provides protection against known attacks. When compared to conventional options, this technique significantly reduces calculation costs by up to 37.68% and transmission costs by up to 32.55%. Full article
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20 pages, 879 KiB  
Article
A Transmission Rate Control Method for Active Congestion Reduction Based on Network Node Bandwidth Allocation
Future Internet 2023, 15(12), 385; https://doi.org/10.3390/fi15120385 - 29 Nov 2023
Viewed by 1082
Abstract
The control of transmission rates is currently a major topic in network research, as it plays a significant role in determining network performance. Traditional network design principles suggest that network nodes should only be responsible for forwarding data, while the sending node should [...] Read more.
The control of transmission rates is currently a major topic in network research, as it plays a significant role in determining network performance. Traditional network design principles suggest that network nodes should only be responsible for forwarding data, while the sending node should manage control. However, sending nodes often lack information about network resources and must use slow-start algorithms to increase the transmission rate, potentially leading to wasted bandwidth and network congestion. Furthermore, incorrect judgments about network congestion by sending nodes may further reduce network throughput. The emergence of new Internet architectures, such as information-centric networks (ICNn), has empowered network nodes with more capabilities, including computation and caching. This paper proposes a method for transmission rate control that actively avoids congestion through network node bandwidth allocation. The sending, network, and receiving nodes each calculate the available transmission rate, and the sending node negotiates with the other nodes through a rate negotiation message to obtain the maximum transmission rate possible given the current state of the network. The network nodes notify the sending node to adjust the transmission rate to adapt to changes in the network through a rate adjustment message. Simulation experiments show that the proposed method is better than traditional methods in reducing network congestion, providing a stable transmission rate, increasing the network throughput capacity, and improving performance in high-latency and high-bandwidth networks. Additionally, the proposed transmission rate control method is fairer than traditional methods. Full article
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15 pages, 441 KiB  
Article
Enhancements in BlenderBot 3: Expanding Beyond a Singular Model Governance and Boosting Generational Performance
Future Internet 2023, 15(12), 384; https://doi.org/10.3390/fi15120384 - 28 Nov 2023
Viewed by 1065
Abstract
This paper provides a pioneering examination and enhancement of generative chat models, with a specific focus on the BlenderBot 3 model. Through meticulous interaction with a diverse set of human participants, we dissected the fundamental components of these models, unveiling several deficiencies, including [...] Read more.
This paper provides a pioneering examination and enhancement of generative chat models, with a specific focus on the BlenderBot 3 model. Through meticulous interaction with a diverse set of human participants, we dissected the fundamental components of these models, unveiling several deficiencies, including long-term memory and entity recognition. Leveraging these insights, we engineered refined, streamlined iterations, culminating in a chatbot that transcends the capabilities of all existing models. Our work follows Occam’s razor principle and proves that, for tasks with relatively low complexity, using large overparameterized models instead of smaller ones does not bring significant benefits but increases latency, which may result in a lowered overall user experience. In upholding our commitment to transparency and the progression of shared knowledge, we have made our improved model universally accessible through open-source distribution. Full article
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27 pages, 4542 KiB  
Review
A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
Future Internet 2023, 15(12), 383; https://doi.org/10.3390/fi15120383 - 28 Nov 2023
Viewed by 1634
Abstract
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to [...] Read more.
The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective. Full article
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27 pages, 1362 KiB  
Systematic Review
Review of Blockchain Tokens Creation and Valuation
Future Internet 2023, 15(12), 382; https://doi.org/10.3390/fi15120382 - 27 Nov 2023
Viewed by 1514
Abstract
Blockchain and tokens are relatively new research areas insufficiently explored from both technical and economic perspectives. Even though tokens provide benefits such as easier market access, increased liquidity, lower transaction costs, and automated transactional process, their valuation and price determination are still challenging [...] Read more.
Blockchain and tokens are relatively new research areas insufficiently explored from both technical and economic perspectives. Even though tokens provide benefits such as easier market access, increased liquidity, lower transaction costs, and automated transactional process, their valuation and price determination are still challenging due to factors such as a lack of intrinsic value, volatility, and regulation making trading risky. In this paper, we address this knowledge gap by reviewing the existing literature on token creation and valuation to identify and document the factors affecting their valuation, investment, and founding, as well as the most promising domains of applicability. The study follows the PRISMA methodology and uses the Web of Science database, defining clear research questions and objective inclusion criteria for the articles. We discuss token technical development, including creating, issuing, and managing tokens on an Ethereum blockchain using smart contracts. The study revealed several key factors that significantly impact the field of tokenomics: demand and supply, social incentives, market conditions, macroeconomics, collective behavior, speculation, and inclusion in index funds. The most relevant use cases of blockchain and tokens are related to the digitization of virtual and physical assets, accountability, and traceability usual in smart grids or supply chains management, social governance, and art and gamification including metaverse. Full article
(This article belongs to the Section Techno-Social Smart Systems)
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19 pages, 1073 KiB  
Article
The Multiband over Spatial Division Multiplexing Sliceable Transceiver for Future Optical Networks
Future Internet 2023, 15(12), 381; https://doi.org/10.3390/fi15120381 - 27 Nov 2023
Viewed by 1051
Abstract
In the last 15 years, global data traffic has been doubling approximately every 2–3 years, and there is a strong indication that this pattern will persist. Hence, also driven by the emergence of new applications and services expected within the 6G era, new [...] Read more.
In the last 15 years, global data traffic has been doubling approximately every 2–3 years, and there is a strong indication that this pattern will persist. Hence, also driven by the emergence of new applications and services expected within the 6G era, new transmission systems and technologies should be investigated to enhance network capacity and achieve increased bandwidth, improved spectral efficiency, and greater flexibility to effectively accommodate all the expected data traffic. In this paper, an innovative transmission solution based on multiband (MB) over spatial division multiplexing (SDM) sliceable bandwidth/bitrate variable transceiver (S-BVT) is implemented and assessed in relation to the provision of sustainable capacity scaling. MB transmission (S+C+L) over 25.4 km of 19-cores multicore fibre (MCF) is experimentally assessed and demonstrated achieving an aggregated capacity of 119.1 Gb/s at 4.62×103 bit error rate (BER). The proposed modular sliceable transceiver architecture arises as a suitable option towards achieving 500 Tb/s per fibre transmission, by further enabling more slices covering all the available S+C+L spectra and the 19 cores of the MCF. Full article
(This article belongs to the Special Issue Key Enabling Technologies for Beyond 5G Networks)
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25 pages, 2498 KiB  
Review
Open Radio Access Networks for Smart IoT Systems: State of Art and Future Directions
Future Internet 2023, 15(12), 380; https://doi.org/10.3390/fi15120380 - 27 Nov 2023
Viewed by 1369
Abstract
The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT [...] Read more.
The Internet of Things (IoT) constitutes a vast network comprising various components such as physical devices, vehicles, buildings, and other items equipped with sensors, actuators, and software. These components are interconnected, facilitating the collection and exchange of copious data across networked communications. IoT empowers extensive monitoring and control over a myriad of objects, enabling them to gather and disseminate data that bolster applications, thereby enhancing the system’s capacity for informed decision making, environmental surveillance, and autonomous inter-object interaction, all without the need for direct human involvement. These systems have achieved seamless connectivity requirements using the next-generation wireless network infrastructures (5G, 6G, etc.), while their diverse reliability and quality of service (QoS) requirements across various domains require more efficient solutions. Open RAN (O-RAN), i.e., open radio open access network (RAN), promotes flexibility and intelligence in the next-generation RAN. This article reviews the applications of O-RAN in supporting the next-generation smart world IoT systems by conducting a thorough survey. We propose a generic problem space, which consists of (i) IoT Systems: transportation, industry, healthcare, and energy; (ii) targets: reliable communication, real-time analytics, fault tolerance, interoperability, and integration; and (iii) artificial intelligence and machine learning (AI/ML): reinforcement learning (RL), deep neural networks (DNNs), etc. Furthermore, we outline future research directions concerning robust and scalable solutions, interoperability and standardization, privacy, and security. We present a taxonomy to unveil the security threats to emerge from the O-RAN-assisted IoT systems and the feasible directions to move this research forward. Full article
(This article belongs to the Special Issue Joint Design and Integration in Smart IoT Systems)
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20 pages, 1695 KiB  
Review
Extended Reality (XR) Engines for Developing Gamified Apps and Serious Games: A Scoping Review
Future Internet 2023, 15(12), 379; https://doi.org/10.3390/fi15120379 - 27 Nov 2023
Viewed by 1423
Abstract
Extended Reality (XR) is an emerging technology that enables enhanced interaction between the real world and virtual environments. In this study, we conduct a scoping review of XR engines for developing gamified apps and serious games. Our study revolves around four aspects: (1) [...] Read more.
Extended Reality (XR) is an emerging technology that enables enhanced interaction between the real world and virtual environments. In this study, we conduct a scoping review of XR engines for developing gamified apps and serious games. Our study revolves around four aspects: (1) existing XR game engines, (2) their primary features, (3) supported serious game attributes, and (4) supported learning activities. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) model to conduct the scoping review, which included 40 primary studies published between 2019 and 2023. Our findings help us understand how current XR engines support the development of XR-enriched serious games and gamified apps for specific learning activities. Additionally, based on our findings, we suggest a set of pre-established game attributes that could be commonly supported by all XR game engines across the different game categories proposed by Lameras. Hence, this scoping review can help developers (1) select important game attributes for their new games and (2) choose the game engine that provides the most support to these attributes. Full article
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21 pages, 649 KiB  
Article
Statistical Model Checking in Process Mining: A Comprehensive Approach to Analyse Stochastic Processes
Future Internet 2023, 15(12), 378; https://doi.org/10.3390/fi15120378 - 26 Nov 2023
Viewed by 1282
Abstract
The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is [...] Read more.
The study of business process analysis and optimisation has attracted significant scholarly interest in the recent past, due to its integral role in boosting organisational performance. A specific area of focus within this broader research field is process mining (PM). Its purpose is to extract knowledge and insights from event logs maintained by information systems, thereby discovering process models and identifying process-related issues. On the other hand, statistical model checking (SMC) is a verification technique used to analyse and validate properties of stochastic systems that employs statistical methods and random sampling to estimate the likelihood of a property being satisfied. In a seamless business setting, it is essential to validate and verify process models. The objective of this paper is to apply the SMC technique in process mining for the verification and validation of process models with stochastic behaviour and large state space, where probabilistic model checking is not feasible. We propose a novel methodology in this research direction that integrates SMC and PM by formally modelling discovered and replayed process models and apply statistical methods to estimate the results. The methodology facilitates an automated and proficient evaluation of the extent to which a process model aligns with user requirements and assists in selecting the optimal model. We demonstrate the effectiveness of our methodology with a case study of a loan application process performed in a financial institution that deals with loan applications submitted by customers. The case study highlights our methodology’s capability to identify the performance constraints of various process models and aid enhancement efforts. Full article
(This article belongs to the Section Big Data and Augmented Intelligence)
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