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BPMNE4IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes
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Quantum Computing for Healthcare: A Review
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RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework
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Human–Machine Interaction through Advanced Haptic Sensors: A Piezoelectric Sensory Glove with Edge Machine Learning for Gesture and Object Recognition
Journal Description
Future Internet
Future Internet
is a scholarly, peer-reviewed, open access journal on Internet technologies and the information society, published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, Inspec, and other databases.
- Journal Rank: CiteScore - Q2 (Computer Networks and Communications)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 15.2 days after submission; acceptance to publication is undertaken in 3.5 days (median values for papers published in this journal in the second half of 2022).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
Future Internet 2023, 15(4), 118; https://doi.org/10.3390/fi15040118 - 24 Mar 2023
Abstract
Nonorthogonal multiple access (NOMA), one of the favorable candidates of next-generation wireless networks combined with group device-to-device (D2D) networks, can sufficiently increase a system’s spectral efficiency. In fact, in a cooperative scenario, successive interference cancellation (SIC) is used in NOMA receivers to reduce
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Nonorthogonal multiple access (NOMA), one of the favorable candidates of next-generation wireless networks combined with group device-to-device (D2D) networks, can sufficiently increase a system’s spectral efficiency. In fact, in a cooperative scenario, successive interference cancellation (SIC) is used in NOMA receivers to reduce the complexity of relaying, as each user has to decode high-order user data. This work presents a quality of service (QoS)-based cooperative NOMA-aided group D2D system (Q-CNOMA). The Q-CNOMA system not only reduces the burden on the group transmitter by relaying the signal to a receiver in neighboring cells but also improves the overall system performance. In order to model the major components in a D2D scenario such as receivers clustering around a transmitter, the spatial distribution of D2D transmitters is modeled using a Gaussian–Poisson process (GPP). A closed-form expression of outage probability is calculated and benchmarked against conventional systems to prove the superiority of the proposed Q-CNOMA system.
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(This article belongs to the Section Internet of Things)
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Artificial-Intelligence-Based Charger Deployment in Wireless Rechargeable Sensor Networks
Future Internet 2023, 15(3), 117; https://doi.org/10.3390/fi15030117 - 22 Mar 2023
Abstract
To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment,
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To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio.
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(This article belongs to the Special Issue Distributing Computing in the Internet of Things: Cloud, Fog and Edge Computing)
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Open AccessReview
Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review
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Future Internet 2023, 15(3), 116; https://doi.org/10.3390/fi15030116 - 22 Mar 2023
Abstract
In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human
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In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.
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(This article belongs to the Special Issue Artificial Intelligence for Smart Cities)
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A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands
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Future Internet 2023, 15(3), 115; https://doi.org/10.3390/fi15030115 - 21 Mar 2023
Abstract
Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in
[...] Read more.
Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure.
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(This article belongs to the Special Issue Future Communication Networks for the Internet of Things (IoT))
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Research on Spaceborne Target Detection Based on Yolov5 and Image Compression
Future Internet 2023, 15(3), 114; https://doi.org/10.3390/fi15030114 - 19 Mar 2023
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Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground
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Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images.
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CvAMoS—Event Abstraction Using Contextual Information
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Future Internet 2023, 15(3), 113; https://doi.org/10.3390/fi15030113 - 18 Mar 2023
Abstract
Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented
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Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented is reflected in the granularity of the event log. When a process is captured by the usage of sensor systems, process activities are recorded at the sensor-level in the form of sensor readings, and are therefore too fine-grained and non-explanatory. To increase the understandability of the process model, events need to be abstracted into higher-level activities that provide a more meaningful representation of the process. The abstraction becomes more relevant and challenging when the process involves human behavior, as the flexible nature of human actions can make it harder to identify and abstract meaningful activities. This paper proposes CvAMoS, a trace-based approach for event abstraction, which focuses on identifying motifs while taking context into account. A motif is a recurring sequence of events that represents an activity that took place under specific circumstances depicted by the context. Context information is logged in the event log in the form of environmental sensor readings (e.g., the temperature and light sensors). The presented algorithm uses a distance function to deal with the variability in the execution of activities. The result is a set of meaningful and interpretable motifs. The algorithm has been tested on both synthetic and real datasets, and compared to the state of the art. CvAMoS is implemented as a Java application and the code is freely available.
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(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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A Descriptive Study of Webpage Designs for Posting Privacy Policies for Different-Sized US Hospitals to Create an Assessment Framework
Future Internet 2023, 15(3), 112; https://doi.org/10.3390/fi15030112 - 17 Mar 2023
Abstract
In the United States, there are laws and standards guiding how people should be informed about the use of their private data. However, the challenge of communicating these guidelines to the naïve user is still at its peak. Research has shown that the
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In the United States, there are laws and standards guiding how people should be informed about the use of their private data. However, the challenge of communicating these guidelines to the naïve user is still at its peak. Research has shown that the willingness to read privacy statements is influenced by attitudes toward privacy risks and privacy benefits. Many websites publish privacy policies somewhere on their web pages, and it can be difficult to navigate to them. In the healthcare field, research has found that health information websites’ key information is presented poorly and inconsistently. For the policies to be legally binding, a person must be able to find them. In the healthcare industry, where sensitive data are being collected, research on how a user navigates to privacy policies for different size hospital websites is limited. Studies exist about privacy policies or website design and not both. This descriptive study involved ascertaining commonalities and differences among different-sized hospitals’ website designs for supporting privacy policies. A foundation framework was created using Web Content Accessibility Guidelines (WGAC) principles and the literature review findings for evaluating practices for website publishing of privacy policies. The results demonstrated a very low variance in the website design concepts employed by hospitals to publish their privacy policy.
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(This article belongs to the Section Techno-Social Smart Systems)
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Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study
Future Internet 2023, 15(3), 111; https://doi.org/10.3390/fi15030111 - 17 Mar 2023
Abstract
Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether
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Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.
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(This article belongs to the Special Issue Challenges and Opportunities in Electronic Medical Record (EMR))
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Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues
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, , , , and
Future Internet 2023, 15(3), 110; https://doi.org/10.3390/fi15030110 - 14 Mar 2023
Abstract
Research on the analysis of counselling conversations through natural language processing methods has seen remarkable growth in recent years. However, the potential of this field is still greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert
[...] Read more.
Research on the analysis of counselling conversations through natural language processing methods has seen remarkable growth in recent years. However, the potential of this field is still greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert annotations, but it has been alleviated thanks to the recent release of AnnoMI, the first publicly and freely available conversation dataset of 133 faithfully transcribed and expert-annotated demonstrations of high- and low-quality motivational interviewing (MI)—an effective therapy strategy that evokes client motivation for positive change. In this work, we introduce new expert-annotated utterance attributes to AnnoMI and describe the entire data collection process in more detail, including dialogue source selection, transcription, annotation, and post-processing. Based on the expert annotations on key MI aspects, we carry out thorough analyses of AnnoMI with respect to counselling-related properties on the utterance, conversation, and corpus levels. Furthermore, we introduce utterance-level prediction tasks with potential real-world impacts and build baseline models. Finally, we examine the performance of the models on dialogues of different topics and probe the generalisability of the models to unseen topics.
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(This article belongs to the Special Issue Deep Learning and Natural Language Processing II)
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DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
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, , , , , , , and
Future Internet 2023, 15(3), 109; https://doi.org/10.3390/fi15030109 - 14 Mar 2023
Abstract
The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and
[...] Read more.
The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.
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(This article belongs to the Special Issue IoT-Based BPM for Smart Environments)
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A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
Future Internet 2023, 15(3), 108; https://doi.org/10.3390/fi15030108 - 10 Mar 2023
Abstract
In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels
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In digital image processing and steganography, images are often described using edges and local binary pattern (LBP) codes. By combining these two properties, a novel hybrid image steganography method of secret embedding is proposed in this paper. This method only employs edge pixels that influence how well the novel approach embeds data. To increase the quantity of computed edge pixels, several edge detectors are applied and hybridized using a logical OR operation. A morphological dilation procedure in the hybridized edge image is employed to this purpose. The least significant bits (LSB) and all LBP codes are calculated for edge pixels. Afterward, these LBP codes, LSBs, and secret bits using an exclusive-OR operation are merged. These resulting implanted bits are delivered to edge pixels’ LSBs. The experimental results show that the suggested approach outperforms current strategies in terms of measuring perceptual transparency, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI). The embedding capacity per tempered pixel in the proposed approach is also substantial. Its embedding guidelines protect the privacy of implanted data. The entropy, correlation coefficient, cosine similarity, and pixel difference histogram data show that our proposed method is more resistant to various types of cyber-attacks.
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(This article belongs to the Collection Information Systems Security)
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Dealing with Deadlocks in Industrial Multi Agent Systems
Future Internet 2023, 15(3), 107; https://doi.org/10.3390/fi15030107 - 09 Mar 2023
Abstract
Automated Manufacturing Systems (AMS) consisting of many cooperating devices incorporated into multiple cooperating production lines, sharing common resources, represent industrial Multi-Agent Systems (MAS). Deadlocks may occur during operation of such MAS. It is necessary to deal with deadlocks (more precisely said, to prevent
[...] Read more.
Automated Manufacturing Systems (AMS) consisting of many cooperating devices incorporated into multiple cooperating production lines, sharing common resources, represent industrial Multi-Agent Systems (MAS). Deadlocks may occur during operation of such MAS. It is necessary to deal with deadlocks (more precisely said, to prevent them) to ensure the correct behavior of AMS. For this purpose, among other methods, methods based on Petri nets (PN) are used too. Because AMS are very often described by PN models, two PN-based methods will be presented here, namely based on (i) PN place invariants (P-invariants); and (ii) PN siphons and traps. Intended final results of usage these methods is finding a supervisor allowing a deadlock-free activity of the global MAS. While the former method yields results in analytical terms, latter one need computation of siphons and traps.
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(This article belongs to the Special Issue Modern Trends in Multi-Agent Systems)
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Complex Queries for Querying Linked Data
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Future Internet 2023, 15(3), 106; https://doi.org/10.3390/fi15030106 - 09 Mar 2023
Abstract
Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages,
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Querying Linked Data is one of the most important issues for the semantic web community today because it requires the user to understand the structure and vocabularies used in various data sources. Furthermore, users must be familiar with the syntax of query languages, such as SPARQL. However, because users are accustomed to natural language-based search, novice users may find it challenging to use these features. As a result, new approaches for querying Linked Data sources on the web with NL queries must be defined. In this paper, we propose a novel system for converting natural language queries into SPARQL queries to query linked and heterogeneous semantic data on the web. While most existing methods have focused on simple queries and have ignored complex queries, the method described in this work aims to handle various types of NL queries, particularly complex queries containing negation, numbers, superlatives, and comparative adjectives. Three complementary strategies are used in this context: (1) identifying the semantic relations between query terms in order to understand the user’s needs; (2) mapping the NL terms to semantic entities; and (3) constructing the query’s valid triples based on the different links used to describe the identified entities in order to generate correct SPARQL queries. The empirical evaluations show that the proposed system is effective.
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(This article belongs to the Special Issue Information Retrieval on the Semantic Web)
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Beamforming Based on a SSS Angle Estimation Algorithm for 5G NR Networks
Future Internet 2023, 15(3), 105; https://doi.org/10.3390/fi15030105 - 09 Mar 2023
Abstract
The current 5G-NR standard includes the transmission of multiple synchronization signal blocks (SSBs) in different directions to be exploited in beamforming techniques. However, choosing a pair of these beams leads to performance degradation, mainly for the cases where the transmit and receive beams
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The current 5G-NR standard includes the transmission of multiple synchronization signal blocks (SSBs) in different directions to be exploited in beamforming techniques. However, choosing a pair of these beams leads to performance degradation, mainly for the cases where the transmit and receive beams are not aligned, because it considers that only few fixed directions among wide beams are established. Therefore, in this article, we design a new 3GPP-standard- compliant beam pair selection algorithm based on secondary synchronization signal (SSS) angle estimation (BSAE) that makes use of multiple synchronization signal blocks (SSBs) to maximize the reference signal received power (RSRP) value at the receiver. This optimization is performed using the SSSs present in each SSB to perform channel estimation in the digital domain. Afterwards, the combination of those estimations is used to perform equivalent channel propagation matrix estimation without the analog processing effects. Finally, through the estimated channel propagation matrix, the angle that maximizes the RSRP is determined to compute the most suitable beam. The proposed algorithm was evaluated and compared with a conventional beam pair selection algorithm. Ours has better performance results. Furthermore, the proposed algorithm achieved performance close to the optimal performance, where all channel state information (CSI) is available, emphasizing the interest of the proposed approach for practical 5G mmWave mMIMO implementations.
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(This article belongs to the Special Issue 5G Wireless Communication Networks)
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Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling
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, , , , , , and
Future Internet 2023, 15(3), 104; https://doi.org/10.3390/fi15030104 - 08 Mar 2023
Abstract
Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts
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Task scheduling algorithms are crucial for optimizing the utilization of computing resources. This work proposes a unique approach for improving task execution in real-time systems using an enhanced Round Robin scheduling algorithm variant incorporating dynamic time quantum and priority. The proposed algorithm adjusts the time slice allocated to each task based on execution time and priority, resulting in more efficient resource utilization. We also prioritize higher-priority tasks and execute them as soon as they arrive in the ready queue, ensuring the timely completion of critical tasks. We evaluate the performance of our algorithm using a set of real-world tasks and compare it with traditional Round Robin scheduling. The results show that our proposed approach significantly improves task execution time and resource utilization compared to conventional Round Robin scheduling. Our approach offers a promising solution for optimizing task execution in real-time systems. The combination of dynamic time quantum and priorities adds a unique element to the existing literature in this field.
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(This article belongs to the Special Issue Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II)
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Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags
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Future Internet 2023, 15(3), 103; https://doi.org/10.3390/fi15030103 - 08 Mar 2023
Abstract
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction
[...] Read more.
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the tag. This study investigates the performance of machine learning (ML) algorithms to detect the movement and direction of passive RFID tags. The dataset utilized in this study was created by considering a variety of conceivable tag motions and directions that may occur in actual warehouse settings, such as going inside and out of the gate, moving close to the gate, turning around, and static tags. The statistical features are derived from the received signal strength (RSS) and the timestamp of tags. Our proposed model combined Isolation Forest (iForest) outlier detection, Synthetic Minority Over Sampling Technique (SMOTE) and Random Forest (RF) has shown the highest accuracy up to 94.251% as compared to other ML models in detecting the movement and direction of RFID tags. In addition, we demonstrated the proposed classification model could be applied to a web-based monitoring system, so that tagged products that move in or out through a gate can be correctly identified. This study is expected to improve the RFID gate on detecting the status of products (being received or delivered) automatically.
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(This article belongs to the Special Issue Collaborative and Intelligent Networks and Decision Systems and Services for Supporting Engineering and Production Management II)
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IoT-Portrait: Automatically Identifying IoT Devices via Transformer with Incremental Learning
Future Internet 2023, 15(3), 102; https://doi.org/10.3390/fi15030102 - 07 Mar 2023
Abstract
With the development of IoT, IoT devices have proliferated. With the increasing demands of network management and security evaluation, automatic identification of IoT devices becomes necessary. However, existing works require a lot of manual effort and face the challenge of catastrophic forgetting. In
[...] Read more.
With the development of IoT, IoT devices have proliferated. With the increasing demands of network management and security evaluation, automatic identification of IoT devices becomes necessary. However, existing works require a lot of manual effort and face the challenge of catastrophic forgetting. In this paper, we propose IoT-Portrait, an automatic IoT device identification framework based on a transformer network. IoT-Portrait automatically acquires information about IoT devices as labels and learns the traffic behavior characteristics of devices through a transformer neural network. Furthermore, for privacy protection and overhead reasons, it is not easy to save all past samples to retrain the classification model when new devices join the network. Therefore, we use a class incremental learning method to train the new model to preserve old classes’ features while learning new devices’ features. We implement a prototype of IoT-Portrait based on our lab environment and open-source database. Experimental results show that IoT-Portrait achieves a high identification rate of up to 99% and is well resistant to catastrophic forgetting with a negligible added cost both in memory and time. It indicates that IoT-Portrait can classify IoT devices effectively and continuously.
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(This article belongs to the Section Internet of Things)
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Relational Action Bank with Semantic–Visual Attention for Few-Shot Action Recognition
Future Internet 2023, 15(3), 101; https://doi.org/10.3390/fi15030101 - 03 Mar 2023
Abstract
Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performance by exploring additional or auxiliary information for video action recognition
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Recently, few-shot learning has attracted significant attention in the field of video action recognition, owing to its data-efficient learning paradigm. Despite the encouraging progress, identifying ways to further improve the few-shot learning performance by exploring additional or auxiliary information for video action recognition remains an ongoing challenge. To address this problem, in this paper we make the first attempt to propose a relational action bank with semantic–visual attention for few-shot action recognition. Specifically, we introduce a relational action bank as the auxiliary library to assist the network in understanding the actions in novel classes. Meanwhile, the semantic–visual attention is devised to adaptively capture the connections to the foregone actions via both semantic correlation and visual similarity. We extensively evaluate our approach via two backbone models (ResNet-50 and C3D) on HMDB and Kinetics datasets, and demonstrate that the proposed model can obtain significantly better performance compared against state-of-the-art methods. Notably, our results demonstrate an average improvement of about 6.2% when compared to the second-best method on the Kinetics dataset.
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(This article belongs to the Section Techno-Social Smart Systems)
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A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks
Future Internet 2023, 15(3), 100; https://doi.org/10.3390/fi15030100 - 28 Feb 2023
Abstract
Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The
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Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.
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(This article belongs to the Special Issue Security of IoT-Enabled Infrastructures in Smart Cities and Critical Systems)
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Securing Critical User Information over the Internet of Medical Things Platforms Using a Hybrid Cryptography Scheme
by
, , , , and
Future Internet 2023, 15(3), 99; https://doi.org/10.3390/fi15030099 - 28 Feb 2023
Abstract
The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully
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The application of the Internet of Medical Things (IoMT) in medical systems has brought much ease in discharging healthcare services by medical practitioners. However, the security and privacy preservation of critical user data remain the reason the technology has not yet been fully maximized. Undoubtedly, a secure IoMT model that preserves individual users’ privacy will enhance the wide acceptability of IoMT technology. However, existing works that have attempted to solve these privacy and insecurity problems are not space-conservative, computationally intensive, and also vulnerable to security attacks. In this paper, an IoMT-based model that conserves the privacy of the data, is less computationally intensive, and is resistant to various cryptanalysis attacks is proposed. Specifically, an efficient privacy-preserving technique where an efficient searching algorithm through encrypted data was used and a hybrid cryptography algorithm that combines the modification of the Caesar cipher with the Elliptic Curve Diffie Hellman (ECDH) and Digital Signature Algorithm (DSA) were projected to achieve user data security and privacy preservation of the patient. Furthermore, the modified algorithm can secure messages during transmission, perform key exchanges between clients and healthcare centres, and guarantee user authentication by authorized healthcare centres. The proposed IoMT model, leveraging the hybrid cryptography algorithm, was analysed and compared against different security attacks. The analysis results revealed that the model is secure, preserves the privacy of critical user information, and shows robust resistance against different cryptanalysis attacks.
Full article
(This article belongs to the Special Issue Machine Learning for Blockchain and IoT Systems in Smart City)
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