Future Internet, Volume 13, Issue 8 (August 2021) – 34 articles

In this work, we present an innovative approach for damage detection of infrastructures on-edge devices, exploiting a brain-inspired algorithm. The proposed solution exploits recurrent spiking neural networks (LSNNs), which are emerging for their theoretical energy efficiency and compactness, to recognise damage conditions by processing data from low-cost accelerometers (MEMS) directly on the sensor node. We focus on designing an efficient coding of MEMS data to optimise SNN execution on a low-power microcontroller. We characterised and profiled LSNN performance and energy consumption on a hardware prototype sensor node equipped with an STM32 embedded microcontroller and a digital MEMS accelerometer. We used a hardware-in-the-loop environment with virtual sensors generating data on an SPI interface connected to the physical microcontroller to evaluate the system with a data stream from a real viaduct. We exploited this environment also to study the impact of different on-sensor encoding techniques, mimicking a bio-inspired sensor able to generate events instead of accelerations. Obtained results show that the proposed optimised embedded LSNN (eLSNN), when using a spike-based input encoding technique, achieves 54% lower execution time with respect to a naive LSNN algorithm implementation present in the state-of-the-art. The optimised eLSNN requires around 47 kCycles, which is comparable with the data transfer cost from the SPI interface. However, the spike-based encoding technique requires considerably larger input vectors to get the same classification accuracy, resulting in a longer pre-processing and sensor access time. Overall the event-based encoding techniques leads to a longer execution time (1.49×) but similar energy consumption. Moving this coding on the sensor can remove this limitation leading to an overall more energy-efficient monitoring system. Full article

Smart city is a collective term for technologies and concepts that are directed toward making cities efficient, technologically more advanced, greener and more socially inclusive. These concepts include technical, economic and social innovations. This term has been tossed around by various actors in politics, business, administration and urban planning since the 2000s to establish tech-based changes and innovations in urban areas. The idea of the smart city is used in conjunction with the utilization of digital technologies and at the same time represents a reaction to the economic, social and political challenges that post-industrial societies are confronted with at the start of the new millennium. The key focus is on dealing with challenges faced by urban society, such as environmental pollution, demographic change, population growth, healthcare, the financial crisis or scarcity of resources. In a broader sense, the term also includes non-technical innovations that make urban life more sustainable. So far, the idea of using IoT-based sensor networks for healthcare applications is a promising one with the potential of minimizing inefficiencies in the existing infrastructure. A machine learning approach is key to successful implementation of the IoT-powered wireless sensor networks for this purpose since there is large amount of data to be handled intelligently. Throughout this paper, it will be discussed in detail how AI-powered IoT and WSNs are applied in the healthcare sector. This research will be a baseline study for understanding the role of the IoT in smart cities, in particular in the healthcare sector, for future research works. Full article

After the General Data Protection Regulation (GDPR) was introduced, some organizations and big data companies shared data without conducting any privacy protection and compliance authentication, which endangered user data security, and were punished financially for this reason. This study proposes a blockchain-based GDPR compliance data sharing scheme, aiming to promote compliance with regulations and provide a tool for interaction between users and service providers to achieve data security sharing. The zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK) algorithm is adopted for protecting data and ensure that the user’s private data can satisfy the individual requirements of the service provider without exposing user data. The proposed scheme ensures mutual authentication through the Proof of Authority consensus based on the Committee Endorsement Mechanism (CEM-PoA), and prevents nodes from doing evil using the reputation incentive mechanism. Theoretical analysis and performance comparison indicate that the scheme meets the confidentiality, availability, and other indicators. It has superiority in efficiency and privacy protection compared with other schemes. Full article

Traditional research methods in software defect prediction use part of the data in the same project to train the defect prediction model and predict the defect label of the remaining part of the data. However, in the practical realm of software development, the software project that needs to be predicted is generally a brand new software project, and there is not enough labeled data to build a defect prediction model; therefore, traditional methods are no longer applicable. Cross-project defect prediction uses the labeled data of the same type of project similar to the target project to build the defect prediction model, so as to solve the problem of data loss in traditional methods. However, the difference in data distribution between the same type of project and the target project reduces the performance of defect prediction. To solve this problem, this paper proposes a cross-project defect prediction method based on manifold feature transformation. This method transforms the original feature space of the project into a manifold space, then reduces the difference in data distribution of the transformed source project and the transformed target project in the manifold space, and finally uses the transformed source project to train a naive Bayes prediction model with better performance. A comparative experiment was carried out using the Relink dataset and the AEEEM dataset. The experimental results show that compared with the benchmark method and several cross-project defect prediction methods, the proposed method effectively reduces the difference in data distribution between the source project and the target project, and obtains a higher F1 value, which is an indicator commonly used to measure the performance of the two-class model. Full article

During transmission of digital images, secret messages can be embedded using data hiding techniques. Such techniques can transfer private secrets without drawing the attention of eavesdroppers. To reduce the amount of transmitted data, image compression methods are widely applied. Hiding secret data in compressed images is a hot issue recently. In this paper, we apply the de-clustering concept and the indicator-free search-order coding (IFSOC) technique to hide information into vector quantization (VQ) compressed images. Experimental results show that the proposed two-layer reversible data hiding scheme for IFSOC-encoded VQ index table can hide a large amount of secret data among state-of-the-art methods with a relatively lower bit rate and high security. Full article

The paper considers the possibilities, prospects, and drawbacks of the mixed reality (MR) technology application using mixed reality smartglasses Microsoft HoloLens 2. The main challenge was to find and develop an approach that would allow surgeons to conduct operations using mixed reality on a large scale, reducing the preparation time required for the procedure and without having to create custom solutions for each patient. Research was conducted in three clinical cases: two median neck and one branchial cyst excisions. In each case, we applied a unique approach of hologram positioning in space based on mixed reality markers. As a result, we listed a series of positive and negative aspects related to MR surgery, along with proposed solutions for using MR in surgery on a daily basis. Full article

With the rapid proliferation of wireless traffic and the surge of various data-intensive applications, the energy consumption of wireless networks has tremendously increased in the last decade, which not only leads to more CO² emission, but also results in higher operating expenditure. Consequently, energy efficiency (EE) has been regarded as an essential design criterion for future wireless networks. This paper investigates the problem of EE maximisation for a cooperative heterogeneous network (HetNet) powered by hybrid energy sources via joint base station (BS) switching (BS-Sw) and power allocation using combinatorial optimisation. The cooperation among the BSs is achieved through a coordinated multi-point (CoMP) technique. Next, to overcome the complexity of combinatorial optimisation, Lagrange dual decomposition is applied to solve the power allocation problem and a sub-optimal distance-based BS-Sw scheme is proposed. The main advantage of the distance-based BS-Sw is that the algorithm is tuning-free as it exploits two dynamic thresholds, which can automatically adapt to various user distributions and network deployment scenarios. The optimal binomial and random BS-Sw schemes are also studied to serve as benchmarks. Further, to solve the non-fractional programming component of the EE maximisation problem, a low-complexity and fast converging Dinkelbach’s method is proposed. Extensive simulations under various scenarios reveal that in terms of EE, the proposed joint distance-based BS-Sw and power allocation technique applied to the cooperative and harvesting BSs performs around 15–20% better than the non-cooperative and non-harvesting BSs and can achieve near-optimal performance compared to the optimal binomial method. Full article

While Europe was beginning to deal with the resurgence of COVID-19 due to the Delta variant, the European football championship took place from 11 June to 11 July 2021. We studied the inversion in the decreased/increased rate of new SARS-COV-2 infections in the countries of the tournament, investigating the hypothesis of an association. Using a Bayesian piecewise regression with a Poisson generalized linear model, we looked for a changepoint in the timeseries of the new SARS-COV-2 cases of each country, expecting it to appear not later than two to three weeks after the date of their first match. The two slopes, before and after the changepoint, were used to discuss the reversal from a decreasing to an increasing rate of the infections. For 17 out of 22 countries (77%) the changepoint came on average 14.97 days after their first match (95% CI 12.29–17.47). For all those 17 countries, the changepoint coincides with an inversion from a decreasing to an increasing rate of the infections. Before the changepoint, the new cases were decreasing, halving on average every 18.07 days (95% CI 11.81–29.42). After the changepoint, the cases begin to increase, doubling every 29.10 days (95% CI 14.12–9.78). This inversion in the SARS-COV-2 case rate, which happened during the tournament, provides evidence in favor of a relationship. Full article

The Third Generation Partnership Project (3GPP) Release 16 defines the sensing-based semi-persistent scheduling (SPS) as the resource allocation scheme for Sidelink Mode 2 in New Radio (NR)-based vehicle-to-everything (V2X) communication. A well-known issue in Mode 2 is the persistent packet collision that results from two or more vehicles repeatedly using the same resource for transmission. It may create serious safety problems when the vehicles are in a situation where only the broadcast safety beacons can assist in driving. To resolve this issue, a solution that relies on the feedback from neighboring vehicles is proposed, through which the vehicles suffering from persistent packet collisions can quickly part and select other resources. Extensive simulations show that the proposed broadcast feedback scheme reduces persistent packet collisions by an order of magnitude compared to SPS, and it is achieved without sacrificing the average packet reception ratio (PRR). Namely, it is the quality aspect (i.e., burstiness) of the packet collisions that the proposed scheme addresses rather than the quantity (i.e., total number of collision losses). By preventing extended packet loss events, the proposed scheme is expected to serve NR V2X better, which requires stringent QoS in terms of the information update delay thereby helping to reduce the chances of vehicle crashes. Full article

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered. Full article

Quality of experience (QoE) is an important measure of users’ satisfaction regarding their network-based services, and it is widely employed today to provide a real assessment of the service quality as perceived by the end users. QoE measures can be used to improve application performance, as well as to optimize network resources and reallocate them as needed when the service quality degrades. While quantitative QoE assessments based on network parameters may provide insights into users’ experience, subjective assessments through direct feedback from the users have also gathered interest recently due to their accuracy and interactive nature. In this paper, we propose a framework that can be used to collect real-time QoE feedback through crowdsourcing and forward it to network controllers to enhance streaming routes. We analyze how QoE can be affected by different network conditions, and how different streaming protocols compare against each other when the network parameters change dynamically. We also compare the real-time user feedback to predefined network changes to measure if participants will be able to identify all degradation events, as well as to examine which combination of degradation events are noticeable to the participants. Our aim is to demonstrate that real-time QoE feedback can enhance cloud-based services and can adjust service quality on the basis of real-time, active participants’ interactions. Full article

The data market concept has gained a lot of momentum in recent years, fuelled by initiatives to set up such markets, e.g., on the European level. Still, the typical data market concept aims at providing a centralised platform with all of its positive and negative side effects. Internal data markets, also called local or on-premise data markets, on the other hand, are set up to allow data trade inside an institution (e.g., between divisions of a large company) or between members of a small, well-defined consortium, thus allowing the remuneration of providing data inside these structures. Still, while research on securing global data markets has garnered some attention throughout recent years, the internal data markets have been treated as being more or less similar in this respect. In this paper, we outline the major differences between global and internal data markets with respect to security and why further research is required. Furthermore, we provide a fundamental model for a secure internal data market that can be used as a starting point for the generation of concrete internal data market models. Finally, we provide an overview on the research questions we deem most pressing in order to make the internal data market concept work securely, thus allowing for more widespread adoption. Full article

IoT group communication allows users to control multiple IoT devices simultaneously. A convenient method for implementing this communication paradigm is by leveraging software-defined networking (SDN) and allowing IoT endpoints to “advertise” the resources that can be accessed through group communication. In this paper, we propose a solution for securing this process by preventing IoT endpoints from advertising “fake” resources. We consider group communication using the constrained application protocol (CoAP), and we leverage Web of Things (WoT) Thing Description (TD) to enable resources’ advertisement. In order to achieve our goal, we are using linked-data proofs. Additionally, we evaluate the application of zero-knowledge proofs (ZKPs) for hiding certain properties of a WoT-TD file. Full article

With the objective of assessing the farmers’ situation regarding the use of the ICT and their relations with the electronic government, a case study consisting in the realization of 34 face-to-face surveys was conducted between February and March 2020 in dairy farms in the region of Galicia (Spain). The sample was selected according to one of the most important online journals in the farming sector at a national level. From the census, we chose those farms considered most representative taking into account the main criteria: the level of PAC (Common Agrarian Politics) subsidies and milk production (litres/cow and year). The results show that the majority of the farmers used the internet, but on many an occasion, they were discontented in relation to the poor connection quality in their farms. In regard to the use of the electronic government for procedures related to their farms, many of them were able to perform them through the government website; however, there were procedures which the users defined as “complex” and which had to be outsourced to authorised entities. The results also show that the farmers do want to employ the e-government, mainly because of the time and cost saving; however, the current web pages do not meet the users’ expectations. Finally, this situation, applied to a region placed among the 10 most productive regions of milk, is comparable to what happens in other regions. Full article

In this paper, the physical-layer security for a three-node wiretap system model is studied. Under the threat of multiple eavesdroppers, it is presumed that a transmitter is communicating with a legitimate receiver. The channels are assumed to be following cascaded $\kappa$-$\mu$ fading distributions. In addition, two scenarios for eavesdroppers’ interception and information-processing capabilities are investigated: colluding and non-colluding eavesdroppers. The positions of these eavesdroppers are assumed to be random in the non-colluding eavesdropping scenario, based on a homogeneous Poisson point process (HPPP). The security is examined in terms of the secrecy outage probability, the probability of non-zero secrecy capacity, and the intercept probability. The exact and asymptotic expressions for the secrecy outage probability and the probability of non-zero secrecy capacity are derived. The results demonstrate the effect of the cascade level on security. Additionally, the results indicate that as the number of eavesdroppers rises, the privacy of signals exchanged between legitimate ends deteriorates. Furthermore, in this paper, regarding the capabilities of tapping and processing the information, we provide a comparison between colluding and non-colluding eavesdropping. Full article

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the top-k documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation. Full article

A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability. Full article

Education 4.0 is looking to prepare future scientists and engineers not only by granting them with knowledge and skills but also by giving them the ability to apply them to solve real life problems through the implementation of disruptive technologies. As a consequence, there is a growing demand for educational material that introduces science and engineering students to technologies, such as Artificial Intelligence (AI) and Brain–Computer Interfaces (BCI). Thus, our contribution towards the development of this material is to create a test bench for BCI given the basis and analysis on how they can be discriminated against. This is shown using different AI methods: Fisher Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Restricted Boltzmann Machines (RBM) and Self-Organizing Maps (SOM), allowing students to see how input changes alter their performance. These tests were done against a two-class Motor Image database. First, using a large frequency band and no filtering eye movement. Secondly, the band was reduced and the eye movement was filtered. The accuracy was analyzed obtaining values around 70∼80% for all methods, excluding SVM and SOM mapping. Accuracy and mapping differentiability increased for some subjects for the second scenario 70∼85%, meaning either their band with the most significant information is on that limited space or the contamination because of eye movement was better mitigated by the regression method. This can be translated to saying that these methods work better under limited spaces. The outcome of this work is useful to show future scientists and engineers how BCI experiments are conducted while teaching them the basics of some AI techniques that can be used in this and other several experiments that can be carried on the framework of Education 4.0. Full article

This study aims to analyze the application of good enough governance in considering the citizens’ value propositions that shape smart city societies. This paper applied a quantitative method with cross-country comparisons. Survey data were derived from the World Values Survey. Malaysia was chosen as the main study area, and compared with Indonesia and other countries worldwide. The findings revealed that politics is the value of least concern across all samples. In terms of qualities for children to develop, respondents in both Malaysia and Indonesia were less concerned about imagination and unselfishness. As for materialist versus post-materialist, the ratios of Malaysia and Indonesia were slightly higher than the average; the post-materialist value of free speech was the lowest value chosen. In the long term, all countries are experiencing the trend of moving toward post-materialist societies. To be sustained under the Collective and Adaptive System of smart city societies, good enough governance in Malaysia and Indonesia should consider the cultural context of the Muslim majority, prioritize governance content that allows more space for political participation and free speech, and cultivate the imagination and unselfishness of children. The generated insights underline the critical role that smart societies play in establishing smart cities. Full article

Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest. Full article

Network behavior analysis is an effective method to outline user requirements, and can extract user characteristics by constructing machine learning models. To protect the privacy of data, the shared information in the model is limited to non-directional network behavior information, such as online duration, traffic, etc., which also hides users’ unconscious needs and habits. However, the value density of this type of information is low, and it is still unclear how much student performance is affected by online behavior; in addition there is a lack of methods for analyzing the correlation between non-directed online behavior and academic performance. In this article, we propose a model for analyzing the correlation between non-directed surfing behavior and academic performance based on user portraits. Different from the existing research, we mainly focus on the public student behavior information in the campus network system and conduct in-depth research on it. The experimental results show that online time and online traffic are negatively correlated with academic performance, respectively, and student’s academic performance can be predicted through the study of non-directional online behavior. Full article

Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research. Full article

The spread of crypto-currencies globally has led to blockchain technology receiving greater attention in recent times. This paper focuses more broadly on the uses of ledger databases as a traditional database manager. Ledger databases will be examined within the parameters of two categories. The first of these are Centralized Ledger Databases (CLD)-based Centralised Ledger Technology (CLT), of which LedgerDB will be discussed. The second of these are Permissioned Blockchain Technology-based Decentralised Ledger Technology (DLT) where Hyperledger Fabric, FalconDB, BlockchainDB, ChainifyDB, BigchainDB, and Blockchain Relational Database will be examined. The strengths and weaknesses of the reviewed technologies will be discussed, alongside a comparison of the mentioned technologies. Full article

The creation of the future quantum Internet requires the development of new systems, architectures, and communications protocols. As a matter of fact, the optical fiber technology is affected by extremely high losses; thus, the deployment of a quantum satellite network (QSN) composed of quantum satellite repeaters (QSRs) in low Earth orbit would make it possible to overcome these attenuation problems. For these reasons, we consider the design of an ad hoc quantum satellite backbone based on the Software-Defined Networking (SDN) paradigm with a modular two-tier Control Plane (CP). The first tier of the CP is embedded into a Master Control Station (MCS) on the ground, which coordinates the entire constellation and performs the management of the CP integrated into the constellation itself. This second tier is responsible for entanglement generation and management on the selected path. In addition to defining the SDN architecture in all its components, we present a possible protocol to generate entanglement on the end-to-end (E2E) path. Furthermore, we evaluate the performance of the developed protocol in terms of the latency required to establish entanglement between two ground stations connected via the quantum satellite backbone. Full article

Hip fracture incidence is life-threatening and has an impact on the person’s physical functionality and their ability to live independently. Proper rehabilitation with a set program can play a significant role in recovering the person’s physical mobility, boosting their quality of life, reducing adverse clinical outcomes, and shortening hospital stays. The Internet of Things (IoT), with advancements in digital health, could be leveraged to enhance the backup intelligence used in the rehabilitation process and provide transparent coordination and information about movement during activities among relevant parties. This paper presents a post-operative hip fracture rehabilitation model that clarifies the involved rehabilitation process, its associated events, and the main physical movements of interest across all stages of care. To support this model, the paper proposes an IoT-enabled movement monitoring system architecture. The architecture reflects the key operational functionalities required to monitor patients in real time and throughout the rehabilitation process. The approach was tested incrementally on ten healthy subjects, particularly for factors relevant to the recognition and tracking of movements of interest. The analysis reflects the significance of personalization and the significance of a one-minute history of data in monitoring the real-time behavior. This paper also looks at the impact of edge computing at the gateway and a wearable sensor edge on system performance. The approach provides a solution for an architecture that balances system performance with remote monitoring functional requirements. Full article

Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders. However, using optical motion sensors and wearable sensors is expensive and often requires highly trained professionals to identify specific impairments. In this work, we proposed a graph convolutional neural network that mimics the intuition of physical therapists to identify patient-specific impairments based on video of a patient. In addition, two modeling approaches are compared: a graph convolutional network applied solely on skeleton input data and a graph convolutional network accompanied with a 1-dimensional convolutional neural network (1D-CNN). Experiments on the dataset showed that the proposed method not only improves the correlation of the predicted gait measure with the ground truth value (speed = 0.791, gait deviation index (GDI) = 0.792) but also enables faster training with fewer parameters. In conclusion, the proposed method shows that the possibility of using video-based data to treat neurological and musculoskeletal disorders with acceptable accuracy instead of depending on the expensive and labor-intensive optical motion capture systems. Full article

One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets. Full article

Digitalization is boosting the manufacturing industry’s shift to smart manufacturing systems, which will efficiently utilize the potential of new technologies for their business outcomes and value. However, the literature shows that manufacturing companies have implemented very little digital technology due to a lack of the required knowledge and competences. Increasingly, interconnected, digitalized, and complex processes lead to new skill requirements in companies and thereafter also of their workforce’s training needs to respond to the smart manufacturing’s new great expectations. The article provides concrete examples of tackling challenges in education arising from digital manufacturing. The case study introduced in this article concerns the additive manufacturing (AM) method, which is expected to give rise to significant changes in various industrial fields, including digital manufacturing. Advances in digital manufacturing requires skilled professionals who are aware of the possibilities and potential of the latest technology. Education therefore needs to be developed. This article points out that the built learning and development environment, SmartLab, supports multidisciplinary approaches and close collaboration between several stakeholders like companies, engineering education courses, students, and RDI actors. The SmartLab concept is thus also expected to provide a remarkable competitive advantage for business in the region. Full article

Global navigation satellite systems (GNSS) are widely used for the navigation of land vehicles. However, the positioning accuracy of GNSS, such as the global positioning system (GPS), deteriorates in urban areas due to signal blockage and multipath effects. GNSS can be integrated with a micro-electro-mechanical system (MEMS)–based inertial navigation system (INS), such as a reduced inertial sensor system (RISS) using a Kalman filter (KF) to enhance the performance of the integrated navigation solution in GNSS challenging environments. The linearized KF cannot model the low-cost and small-size sensors due to relatively high noise levels and compound error characteristics. This paper reviews two approaches to employing parallel cascade identification (PCI), a non-linear system identification technique, augmented with KF to enhance the navigational solution. First, PCI models azimuth errors for a loosely coupled 2D RISS integrated system with GNSS to obtain a navigation solution. The experimental results demonstrated that PCI improved the integrated 2D RISS/GNSS performance by modeling linear, non-linear, and other residual azimuth errors. For the second scenario, PCI is utilized for modeling residual pseudorange correlated errors of a KF-based tightly coupled RISS/GNSS navigation solution. Experimental results have shown that PCI enhances the performance of the tightly coupled KF by modeling the non-linear pseudorange errors to provide an enhanced and more reliable solution. For the first algorithm, the results demonstrated that PCI can enhance the performance by 77% as compared to the KF solution during the GNSS outages. For the second algorithm, the performance improvement for the proposed PCI technique during the availability of three satellites was 39% compared to the KF solution. Full article

IEC 61499 is a reference architecture for constructing Industrial Cyber-Physical Systems (ICPS). However, current function block development environments only provide limited fault-finding capabilities. There is a need for comprehensive diagnostic tools that help engineers identify faults, both during development and after deployment. This article presents the software architecture for an agent-based fault diagnostic engine that equips agents with domain-knowledge of IEC 61499. The engine encourages a Model-Driven Development with Diagnostics methodology where agents work alongside engineers during iterative cycles of design, development, diagnosis and refinement. Attribute-Driven Design (ADD) was used to propose the architecture to capture fault telemetry directly from the ICPS. A Views and Beyond Software Architecture Document presents the architecture. The Architecturally-Significant Requirement (ASRs) were used to design the views while an Architectural Trade-off Analysis Method (ATAM) evaluated critical parts of the architecture. The agents locate faults during both early-stage development and later provide long-term fault management. The architecture introduces dynamic, low-latency software-in-loop Diagnostic Points (DPs) that operate under the control of an agent to capture fault telemetry. Using sound architectural design approaches and documentation methods, coupled with rigorous evaluation and prototyping, the article demonstrates how quality attributes, risks and architectural trade-offs were identified and mitigated early before the construction of the engine commenced. Full article