Computers, Volume 9, Issue 3 (September 2020) – 22 articles

Cloud Computing provides on-demand computing services like software, networking, storage, analytics, and intelligence over the Internet (“the cloud”). But it is facing challenges because of the explosion of the Internet of Things (IoT) devices and the volume, variety, veracity and velocity of the data generated by these devices. There is a need for ultra-low latency, reliable service along with security and privacy. Fog Computing is a promising solution to overcome these challenges. The originality, scope and novelty of this paper is the definition and formulation of the problem of smart neighborhoods in context of smart grids. This is achieved through an extensive literature study, firstly on Fog Computing and its foundation technologies, its applications and the literature review of Fog Computing research in various application domains. Thereafter, we introduce smart grid and community MicroGrid concepts and, their challenges to give the in depth background of the problem and hence, formalize the problem. The smart grid, which ensures reliable, secure, and cost-effective power supply to the smart neighborhoods, effectively needs Fog Computing architecture to achieve its purpose. This paper also identifies, without rigorous analysis, potential solutions to address the problem of smart neighborhoods. The challenges in the integration of Fog Computing and smart grids are also discussed. Full article

Currently, unmanned aerial vehicles, such as drones, are becoming a part of our lives and extend to many areas of society, including the industrialized world. A common alternative for controlling the movements and actions of the drone is through unwired tactile interfaces, for which different remote control devices are used. However, control through such devices is not a natural, human-like communication interface, which sometimes is difficult to master for some users. In this research, we experimented with a domain-based speech recognition architecture to effectively control an unmanned aerial vehicle such as a drone. The drone control was performed in a more natural, human-like way to communicate the instructions. Moreover, we implemented an algorithm for command interpretation using both Spanish and English languages, as well as to control the movements of the drone in a simulated domestic environment. We conducted experiments involving participants giving voice commands to the drone in both languages in order to compare the effectiveness of each, considering the mother tongue of the participants in the experiment. Additionally, different levels of distortion were applied to the voice commands to test the proposed approach when it encountered noisy input signals. The results obtained showed that the unmanned aerial vehicle was capable of interpreting user voice instructions. Speech-to-action recognition improved for both languages with phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions. Using raw audio inputs, the cloud-based approach achieves 74.81% and 97.04% accuracy for English and Spanish instructions, respectively. However, with our phoneme matching approach the results are improved, yielding 93.33% accuracy for English and 100.00% accuracy for Spanish. Full article

Cybersecurity issues constitute a key concern of today’s technology-based economies. Cybersecurity has become a core need for providing a sustainable and safe society to online users in cyberspace. Considering the rapid increase of technological implementations, it has turned into a global necessity in the attempt to adapt security countermeasures, whether direct or indirect, and prevent systems from cyberthreats. Identifying, characterizing, and classifying such threats and their sources is required for a sustainable cyber-ecosystem. This paper focuses on the cybersecurity of smart grids and the emerging trends such as using blockchain in the Internet of Things (IoT). The cybersecurity of emerging technologies such as smart cities is also discussed. In addition, associated solutions based on artificial intelligence and machine learning frameworks to prevent cyber-risks are also discussed. Our review will serve as a reference for policy-makers from the industry, government, and the cybersecurity research community. Full article

A general problem in large cities is mobility. Every day, there are incidents (accidents, construction, or meteorological events) that increase the duration of the journeys in a city and exert negative effects on the lives of citizens. A particular case of this situation is communications with airports. Shuttles are a type of private transport service that operates in airports. The number of passengers carried by shuttles is small. Moreover, shuttles transport passengers to their final destinations and their prices are more competitive than those other private services. However, shuttle services have a limitation with respect to their routes due to the many different destinations of passengers. For this reason, it is important to be able to calculate the best route to optimize the journey. This work presents an Android application that implements a value-added service to plan the route of a shuttle and manage its service (passengers, journeys, routes, etc.). This application combines information from different sources, such as geolocation information from the shuttle driver’s mobile phone, information on the passengers, and results obtained from the service offered by MapBox to calculate the optimized route between several destinations. Full article

Brain–computer interfaces (BCIs) can help people with limited motor abilities to interact with their environment without external assistance. A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data. Due to the highly individualized nature of EEG signals, it has been difficult to develop a cross-subject classification method that achieves sufficiently high accuracy when predicting the subject’s intention. In this study, we propose a multi-branch 2D convolutional neural network (CNN) that utilizes different hyperparameter values for each branch and is more flexible to data from different subjects. Our model, EEGNet Fusion, achieves 84.1% and 83.8% accuracy when tested on the 103-subject eegmmidb dataset for executed and imagined motor actions, respectively. The model achieved statistically significantly higher results compared with three state-of-the-art CNN classifiers: EEGNet, ShallowConvNet, and DeepConvNet. However, the computational cost of the proposed model is up to four times higher than the model with the lowest computational cost used for comparison. Full article

The aerospace and defense industry is facing an end-of-life production issue with legacy embedded uniprocessor systems. Most, if not all, embedded processor manufacturers have already moved towards system-on-a-chip multicore architectures. Current scheduling arrangements do not consider schedules related to safety and security. The methods are also inefficient because they arbitrarily assign larger-than-necessary windows of execution. This research creates a hierarchical scheduling framework as a model for real-time multicore systems to integrate the scheduling for safe and secure systems. This provides a more efficient approach which automates the migration of embedded systems’ real-time software tasks to multicore architectures. A novel genetic algorithm with a unique objective function and encoding scheme was created and compared to classical bin-packing algorithms. The simulation results show the genetic algorithm had 1.8–2.5 times less error (a 56–71% difference), outperforming its counterparts in uniformity in utilization. This research provides an efficient, automated method for commercial, private and defense industries to use a genetic algorithm to create a feasible two-level hierarchical schedule for real-time embedded multicore systems that address safety and security constraints. Full article

The lack of high-performance RINA (Recursive InterNetwork Architecture) implementations to date makes it hard to experiment with RINA as an underlay networking fabric solution for different types of networks, and to assess RINA’s benefits in practice on scenarios with high traffic loads. High-performance router implementations typically require dedicated hardware support, such as FPGAs (Field Programmable Gate Arrays) or specialized ASICs (Application Specific Integrated Circuit). With the advance of hardware programmability in recent years, new possibilities unfold to prototype novel networking technologies. In particular, the use of the P4 programming language for programmable ASICs holds great promise for developing a RINA router. This paper details the design and part of the implementation of the first P4-based RINA interior router, which reuses the layer management components of the IRATI Linux-based RINA implementation and implements the data-transfer components using a P4 program. We also describe the configuration and testing of our initial deployment scenarios, using ancillary open-source tools such as the P4 reference test software switch (BMv2) or the P4Runtime API. Full article

In this work, we demonstrate how the blockchain and the off-chain storage interact via Oracle-based mechanisms, which build an effective connection between a distributed database and real assets. For demonstration purposes, smart contracts were drawn up to deal with two different applications. Due to the characteristics of the blockchain, we may still encounter severe privacy issues, since the data stored on the blockchain are exposed to the public. The proposed scheme provides a general solution for resolving the above-mentioned privacy issue; that is, we try to protect the on-chain privacy of the sensitive data by using homomorphic encryption techniques. Specifically, we constructed a secure comparison protocol that can check the correctness of a logic function directly in the encrypted domain. By using the proposed access control contract and the secure comparison protocol, one can carry out sensitive data-dependent smart contract operations without revealing the data themselves. Full article

This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications. Full article

The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions. Full article

E-commerce implies an electronic purchasing and marketing process online by using typical Web browsers. As e-commerce is quickly developing on the planet, particularly in recent years, many areas of life are affected, particularly the improvement in how individuals regulate themselves non-financially and financially in different transactions. In electronic payment or e-commerce payment, the gateway is a major component of the structure to assure that such exchanges occur without disputes, while maintaining the common security over such systems. Most Internet payment gateways in e-commerce provide monetary information to customers using trusted third parties directly to a payment gateway. Nonetheless, it is recognized that the cloud Web server is not considered a protected entity. This article aims to develop an efficient and secure electronic payment protocol for e-commerce where consumers can immediately connect with the merchant properly. Interestingly, the proposed system does not require the customer to input his/her identity in the merchant’s website even though the customer can hide his/her identity and make a temporary identity to perform the service. It has been found that our protocol has much improved security effectiveness in terms of confidentiality, integrity, non-repudiation, anonymity availability, authentication, and authorization. Full article

This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In the analysis, the non-responsiveness of individual users is taken into account; a user probabilistically spreads the received information, where not spreading (not responding) is equivalent to that the received information is not noticed. In most practical applications, an exact analytic solution is not available for the SIR model, so previous studies have largely been based on the assumption that the probability of an SNS user having the target information is independent of whether or not its neighbors have that information. In contrast, we propose a different approach based on a “strong correlation assumption”, in which the probability of an SNS user having the target information is strongly correlated with whether its neighboring users have that information. To account for the non-responsiveness of individual users, we also propose the “representative-response-based analysis”, in which some information spreading patterns are first obtained assuming representative response patterns of each user and then the results are averaged. Through simulation experiments, we show that the combination of this strong correlation assumption and the representative-response-based analysis makes it possible to analyze the spread of information with far greater accuracy than the traditional approach. Full article

The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has exposed the global DNS infrastructure to malicious actors. The passive DNS data collection process creates a database containing various DNS data elements, some of which are personal and need to be protected to preserve the privacy of the end users. To this end, we propose the use of distributed ledger technology. We use Hyperledger Fabric to create a permissioned blockchain, which only authorized entities can access. The proposed solution supports queries for storing and retrieving data from the blockchain ledger, allowing the use of the passive DNS database for further analysis, e.g., for the identification of malicious domain names. Additionally, it effectively protects the DNS personal data from unauthorized entities, including the administrators that can act as potential malicious insiders, and allows only the data owners to perform queries over these data. We evaluated our proposed solution by creating a proof-of-concept experimental setup that passively collects DNS data from a network and then uses the distributed ledger technology to store the data in an immutable ledger, thus providing a full historical overview of all the records. Full article

Effective capacity allocation is essential for a network to operate properly, providing predictable quality of service guarantees and avoiding bottlenecks. Achieving capacity allocation fairness is a long-standing problem extensively researched in the frame of transport and network layer protocols such as TCP/IP. The Recursive InterNetwork Architecture offers programmable policies that enable more flexible control on the mechanics of network flow allocation. In this paper, we present our version of one of these policies, which provides flow allocation according to the bandwidth requirements of requesting applications. We implement the bandwidth-aware flow allocation policy by extending rlite, an open source RINA implementation. Our evaluation shows how the policy can prevent links from becoming oversaturated and use alternate paths to achieve high total link data-rate use. Full article

A protection of information against electromagnetic penetration is very often considered in the aspect of the possibility of obtaining data contained in printed documents or displayed on screen monitors. However, many printing devices are equipped with screens based on LED technology or liquid crystal displays. Options enabling the selection of parameters of the printed document, technical settings of the device (e.g., screen activity time) are the most frequently displayed information. For more extensive displays, more detailed information appears, which may contain data that are not always irrelevant to third parties. Such data can be: names of printed documents (or documents registered and available on the internal media), service password access, user names or regular printer user activity. The printer display can be treated as a source of revealing emissions, like a typical screen monitor. The emissions correlated with the displayed data may allow us to obtain the abovementioned information. The article includes analyses of various types of computer printer displays. The tests results of the existing threat are presented in the form of reconstructed images that show the possibility of reading the text data contained in them. Full article

Today, Knowledge Management (KM) is becoming a popular approach for improving organizational innovation, but whether encouraging knowledge sharing will lead to a better innovative working behaviour of employees is still a question. This study aims to identify the factors of KM affecting the innovative working behaviour of Information Technology (IT) employees in Vietnam. The research model involves three elements: attitude, subjective norm and perceived behavioural control affecting knowledge sharing, and then, on innovative working behaviour. The research method is the quantitative method. The survey was conducted with 202 samples via the five-scale questionnaire. The analysis results show that knowledge sharing has a positive impact on the innovative working behaviour of IT employees in Vietnam. Besides, attitude and perceived behavioural control are confirmed to have a strong positive effect on knowledge sharing, but the subjective norm has no significant impact on knowledge sharing. Based on this result, recommendations to promote knowledge sharing and the innovative work behaviour of IT employees in Vietnam are made. Full article

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation. In this work, we discuss how machine learning approaches can be used to improve network performance (and if and how they can help). To this aim, we describe a methodology to process LoRaWAN packets and apply a machine learning pipeline to: (i) perform device profiling, and (ii) predict the inter-arrival of IoT packets. This latter analysis is very related to the channel and network usage and can be leveraged in the future for system performance enhancements. Our analysis mainly focuses on the use of k-means, Long Short-Term Memory Neural Networks and Decision Trees. We test these approaches on a real large-scale LoRaWAN network where the overall captured traffic is stored in a proprietary database. Our study shows how profiling techniques enable a machine learning prediction algorithm even when training is not possible because of high error rates perceived by some devices. In this challenging case, the prediction of the inter-arrival time of packets has an error of about 3.5% for 77% of real sequence cases. Full article

European funded research into the Recursive Inter-Network Architecture (RINA) started with IRATI, which developed an initial prototype implementation for OS/Linux. IRATI was quickly succeeded by the PRISTINE project, which developed different policies, each tailored to specific use cases. Both projects were development-driven, where most experimentation was limited to unit testing and smaller scale integration testing. In order to assess the viability of RINA as an alternative to current network technologies, larger scale experimental deployments are needed. The opportunity arose for a project that shifted focus from development towards experimentation, leveraging Europe’s investment in Future Internet Research and Experimentation (FIRE+) infrastructures. The ARCFIRE project took this next step, developing a user-friendly framework for automating RINA experiments. This paper reports and discusses the implications of the experimental results achieved by the ARCFIRE project, using open source RINA implementations deployed on FIRE+ Testbeds. Experiments analyze the properties of RINA relevant to fast network recovery, network renumbering, Quality of Service, distributed mobility management, and network management. Results highlight RINA properties that can greatly simplify the deployment and management of real-world networks; hence, the next steps should be focused on addressing very specific use cases with complete network RINA-based networking solutions that can be transferred to the market. Full article

In today’s digital world, the information systems are revolutionizing the way we connect. As the people are trying to adopt and integrate intelligent systems into daily lives, the risks around cyberattacks on user-specific information have significantly grown. To ensure safe communication, the Intrusion Detection Systems (IDS) were developed often by using machine learning (ML) algorithms that have the unique ability to detect malware against network security violations. Recently, it was reported that the IDS are prone to carefully crafted perturbations known as adversaries. With the aim to understand the impact of such attacks, in this paper, we have proposed a novel random neural network-based adversarial intrusion detection system (RNN-ADV). The NSL-KDD dataset is utilized for training. For adversarial attack crafting, the Jacobian Saliency Map Attack (JSMA) algorithm is used, which identifies the feature which can cause maximum change to the benign samples with minimum added perturbation. To check the effectiveness of the proposed adversarial scheme, the results are compared with a deep neural network which indicates that RNN-ADV performs better in terms of accuracy, precision, recall, F1 score and training epochs. Full article

Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information. Full article

As Web applications become more and more complex, the development costs are increasing as well. A Model Driven Architecture (MDA) approach is proposed in this paper since it simplifies modeling, design, implementation, and integration of applications by defining software mainly at the model level. We adopt the The Unified Modeling Language (UML), as modeling language. UML provides a set of diagrams to model structural and behavioral aspects of the Web applications. Automatic translation of UML diagrams to the Object-Oriented code is highly desirable because it eliminates the chances of introducing human errors. Moreover, automatic code generation helps the software designers delivering of the software on time. In our approach, the automatic transformations across the MDA’s levels are based on meta-models for two of the most important constructs of UML, namely Use Cases and classes. A proprietary tool (called xGenerator) performs the transformations up to the Java source code. The architecture of the generated Web applications respects a variant of the well-known Model-View-Controller (MVC) pattern. Full article

Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results. Full article