Future Internet, Volume 13, Issue 7 (July 2021) – 24 articles

The 5G New Radio (NR) technology operating in millimeter wave (mmWave) frequency band is designed for support bandwidth-greedy applications requiring extraordinary rates at the access interface. However, the use of directional antenna radiation patterns, as well as extremely large path losses and blockage phenomenon, requires efficient algorithms to support these services. In this study, we consider the multi-layer virtual reality (VR) service that utilizes multicast capabilities for baseline layer and unicast transmissions for delivering an enhanced experience. By utilizing the tools of stochastic geometry and queuing theory we develop a simple algorithm allowing to estimate the deployment density of mmWave NR base stations (BS) supporting prescribed delivery guarantees. Our numerical results show that the highest gains of utilizing multicast service for distributing base layer is observed for high UE densities. Despite of its simplicity, the proposed multicast group formation scheme operates close to the state-of-the-art algorithms utilizing the widest beams with longest coverage distance in approximately 50–70% of cases when UE density is $\lambda \ge 0.3$. Among other parameters, QoS profile and UE density have a profound impact on the required density of NR BSs while the effect of blockers density is non-linear having the greatest impact on strict QoS profiles. Depending on the system and service parameters the required density of NR BSs may vary in the range of 20–250 BS/km². Full article

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country, with more than 34.1 million total confirmed cases up to 1 June 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet searches and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with a lower infection rate. Lastly, we demonstrate that correlations between online social media and search trends are sensitive to time, mainly due to the attention shifting of the public. Full article

Blockchain is predicted to disrupt industries, economies, and societies. The properties of distributed ledgers allow the creation of immutable data structures that facilitate shared access in real time and enable a plethora of innovative applications. However, blockchain is not a uniform technology but rather a bundle of evolving components whose implications are notoriously hard to predict. At present, it is not clear how current trends will evolve, with technical evolution, legislation, and public policy being three contingency factors that make ongoing disruptive transformations particularly hard to predict. In light of blockchain’s potential disruptive impact, it is surprising that scenario analysis has hitherto been largely ignored in academic research. Therefore, in this paper, we introduce the technique, clarify several misconceptions, and provide examples illustrating how this method can help to overcome the limitations of existing technology impact research. We conclude that if applied correctly, scenario analysis represents the ideal tool to rigorously explore uncertain future developments and to create a comprehensive foundation for future research. Full article

Recently, automatic speech recognition (ASR) and visual speech recognition (VSR) have been widely researched owing to the development in deep learning. Most VSR research works focus only on frontal face images. However, assuming real scenes, it is obvious that a VSR system should correctly recognize spoken contents from not only frontal but also diagonal or profile faces. In this paper, we propose a novel VSR method that is applicable to faces taken at any angle. Firstly, view classification is carried out to estimate face angles. Based on the results, feature extraction is then conducted using the best combination of pre-trained feature extraction models. Next, lipreading is carried out using the features. We also developed audio-visual speech recognition (AVSR) using the VSR in addition to conventional ASR. Audio results were obtained from ASR, followed by incorporating audio and visual results in a decision fusion manner. We evaluated our methods using OuluVS2, a multi-angle audio-visual database. We then confirmed that our approach achieved the best performance among conventional VSR schemes in a phrase classification task. In addition, we found that our AVSR results are better than ASR and VSR results. Full article

“Hybrid Cloud Storage” (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of “Public Cloud Storage” as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This issue can become a major problem when one or more private storage nodes fail. In this paper, we propose a new framework for optimizing the data uploading process that is currently used with hybrid cloud storage systems. The optimization is concerned with spreading the data over the multiple storages in the HCS system according to some predefined objective functions. Furthermore, we also used Network Coding technics for minimizing data transfer latency between the receiver (private storages) and transmitter nodes. Full article

The evolution of 5th generation (5G) cellular technology has introduced several enhancements and provides better performance compared to previous generations. To understand the real capabilities, the importance of the empirical studies is significant to also understand the possible limitations. This is very important especially from the service and use case point of view. Several test sites exist around the globe for introducing, testing, and evaluating new features, use cases, and performance in restricted and secure environments alongside the commercial operators. Test sites equipped with the standard technology are the perfect places for performing deep analysis of the latest wireless and cellular technologies in real operating environments. The testing sites provide valuable information with sophisticated quality of service (QoS) indicators when the 5G vertical use cases are evaluated using the actual devices in the carrier grade network. In addition, the Wi-Fi standards are constantly evolving toward higher bit rates and reduced latency, and their usage in 5G dedicated verticals can even improve performance, especially when lower coverage is sufficient. This work presents the detailed comparative measurements between Wi-Fi 6 and 5G New Radio (NR) performance in indoor facilities and extensive results carried out in 5G and beyond test site located in Finland. The results gathered from the extensive test sets indicate that the Wi-Fi 6 can outperform the 5G in the indoor environment in terms of throughput and latency when distance and coverage do not increase enormously. In addition, the usage of wireless technologies allows improved uplink performance, which is usually more limited in cellular networks. The gained results of our measurements provide valuable information for designing, developing, and implementing the requirements for the next-generation wireless applications. Full article

Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible $1\times 1$ convolution. However, the $1\times 1$ convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible $n\times n$ convolution approach that overcomes the limitations of the invertible $1\times 1$ convolution. In addition, our proposed network is not only tractable and invertible but also uses fewer parameters than standard convolutions. The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible $n\times n$ convolution helps to improve the performance of generative models significantly. Full article

Nowadays, Data Science (DS) is gaining a relevant impact on the community. The most recent developments in Computer Science, such as advances in Machine and Deep Learning, Big Data, Knowledge Discovery, and Data Analytics, have triggered the development of several innovative solutions (e.g., approaches, methods, models, or paradigms). It is a trending topic with many application possibilities and motivates the researcher to conduct experiments in these most diverse areas. This issue created an opportunity to expose some of the most relevant achievements in the Knowledge Discovery and Data Science field and contribute to such subjects as Health, Smart Homes, Social Humanities, Government, among others. The relevance of this field can be easily observed by its current achieved numbers: thirteen research articles, one technical note, and forty-six authors from fifteen nationalities. Full article

The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document. Full article

Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that involves three subtasks: one-class instance segmentation (OCIS), multiobject tracking (MOT), and zero-shot fine-grained recognition of 3D objects (ZSFGR3D). A new level set map network (LSMNet) and a multiview redundancy-free feature network (MVRFFNet) are proposed for the first and third subtasks, respectively. The level set map (LSM) is used to annotate instances instead of the traditional multichannel binary mask, and each peak of the LSM represents one instance. Based on the LSM, LSMNet can adopt a pix2pix architecture to segment instances. MVRFFNet is a generalized zero-shot learning (GZSL) framework based on the Wasserstein generative adversarial network for 3D object recognition. Multi-view features of an object are combined into a compact registered feature. By treating the registered features as the category attribution in the GZSL setting, MVRFFNet learns a mapping function that maps original retrieve features into a new redundancy-free feature space. To validate the performance of the proposed methods, a segmentation dataset and a fine-grained classification dataset about objects on a conveyor are established. Experimental results on these datasets show that LSMNet can achieve a recalling accuracy close to the light instance segmentation framework You Only Look At CoefficienTs (YOLACT), while its computing speed on an NVIDIA GTX1660TI GPU is 80 fps, which is much faster than YOLACT’s 25 fps. Redundancy-free features generated by MVRFFNet perform much better than original features in the retrieval task. Full article

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company. Full article

As a typical cyber-physical system, networked unmanned aerial vehicles (UAVs) have received much attention in recent years. Emerging communication technologies and high-performance control methods enable networked UAVs to operate as aerial sensor networks to collect more complete and consistent information with significantly improved mobility and flexibility than traditional sensing platforms. One of the main applications of networked UAVs is surveillance and monitoring, which constitute essential components of a well-functioning public safety system and many industrial applications. Although the existing literature on surveillance and monitoring UAVs is extensive, a comprehensive survey on this topic is lacking. This article classifies publications on networked UAVs for surveillance and monitoring using the targets of interest and analyzes several typical problems on this topic, including the control, navigation, and deployment optimization of UAVs. The related research gaps and future directions are also presented. Full article

The increasing cost of healthcare services is accelerating the development of the telehealth system to fulfill the necessity of delivering an efficient and cost-effective remote healthcare services. Moreover, the ageing of the global population and the disruption of the COVID-19 pandemic are creating a rapid rise of demand for healthcare services. This includes those who are in need of remote monitoring for chronic conditions through rehabilitation exercises. Therefore, this paper presents a telehealth system using PoseNet for in-home rehabilitation, with built-in statistical computation for doctors to analyze the patient’s recovery status. This system enables patients to perform rehabilitation exercises at home using an ordinary webcam. The PoseNet skeleton-tracking method is applied to detect and track the patients’ angular movements for both elbows and knees. By using this system, the measurement of the elbow and knee joint angles can be calculated and recorded while patients are performing rehabilitation exercises in front of the laptop webcam. After the patients complete their rehabilitation exercises, the skeleton results of four body parts will be generated. Based on the same actions performed by patients on selected days, the doctors can examine and evaluate the deviation rate of patients’ angular movements between different days to determine the recovery rate. Full article

Capsicum is a genus of flowering plants in the Solanaceae family in which the members are well known to have a high economic value. The Capsicum fruits, which are popularly known as peppers or chili, have been widely used by people worldwide. It serves as a spice and raw material for many products such as sauce, food coloring, and medicine. For many years, scientists have studied this plant to optimize its production. A tremendous amount of knowledge has been obtained and shared, as reflected in multiple knowledge-based systems, databases, or information systems. An approach to knowledge-sharing is through the adoption of a common ontology to eliminate knowledge understanding discrepancy. Unfortunately, most of the knowledge-sharing solutions are intended for scientists who are familiar with the subject. On the other hand, there are groups of potential users that could benefit from such systems but have minimal knowledge of the subject. For these non-expert users, finding relevant information from a less familiar knowledge base would be daunting. More than that, users have various degrees of understanding of the available content in the knowledge base. This understanding discrepancy raises a personalization problem. In this paper, we introduce a solution to overcome this challenge. First, we developed an ontology to facilitate knowledge-sharing about Capsicum to non-expert users. Second, we developed a personalized faceted search algorithm that provides multiple structured ways to explore the knowledge base. The algorithm addresses the personalization problem by identifying the degree of understanding about the subject from each user. In this way, non-expert users could explore a knowledge base of Capsicum efficiently. Our solution characterized users into four groups. As a result, our faceted search algorithm defines four types of matching mechanisms, including three ranking mechanisms as the core of our solution. In order to evaluate the proposed method, we measured the predictability degree of produced list of facets. Our findings indicated that the proposed matching mechanisms could tolerate various query types, and a high degree of predictability can be achieved by combining multiple ranking mechanisms. Furthermore, it demonstrates that our approach has a high potential contribution to biodiversity science in general, where many knowledge-based systems have been developed with limited access to users outside of the domain. Full article

In global networks, Border Gateway Protocol (BGP) is widely used in exchanging routing information. While the original design of BGP did not focus on security protection against deliberate or accidental errors regarding to routing disruption, one of fundamental vulnerabilities in BGP is a lack of insurance in validating authority for announcing network layer reachability. Therefore, a distributed repository system known as Resource Public Key Infrastructure (RPKI) has been utilized to mitigate this issue. However, such a validation requires further deployment steps for Autonomous System (AS), and it might cause performance and compatibility problems in legacy network infrastructure. Nevertheless, with recent advancements in network innovation, some traditional networks are planning to be restructured with Software-Defined Networking (SDN) technology for gaining more benefits. By using SDN, Internet eXchange Point (IXP) is able to enhance its capability of management by applying softwarized control methods, acting as a Software-Defined eXchange (SDX) center to handle numerous advertisement adaptively. To use the SDN method to strengthen routing security of IXP, this paper proposed an alternative SDX development, SD-BROV, an SDX-based BGP Route Origin Validation mechanism that establishes a flexible route exchange scenario with RPKI validation. The validating application built in the SDN controller is capable of investigating received routing information. It aims to support hybrid SDN environments and help non-SDN BGP neighbors to get trusted routes and drop suspicious ones in transition. To verify proposed idea with emulated environment, the proof-of-concept development is deployed on an SDN testbed running over Research and Education Networks (RENs). During BGP hijacking experiment, the results show that developed SD-BROV is able to detect and stop legitimate traffic to be redirected by attacker, making approach to secure traffic forwarding on BGP routers. Full article

Cloud-Radio Access Network (RAN) is one of the architectural solutions for those mobile networks that aim to provide an infrastructure that satisfies the communication needs of a wide range of services and deployments. In Cloud-RAN, functions can be flexibly split between central and distributed units, which enables the use of different types of transport network. Ethernet-based fronthaul can be an attractive solution for Cloud-RAN. On the one hand, the deployment of Ethernet-based fronthaul enables Cloud-RAN to provide more diverse, flexible and cost-efficient solutions. On the other hand, Ethernet-based fronthaul requires packetized communication, which imposes challenges to delivering stringent latency requirements between RAN functionalities. In this paper, we set up a hardware experiment based on Cloud-RAN with a low layer split, particularly between medium access control and the physical layer. The aim is to demonstrate how multi-path and channel coding over the fronthaul can improve fronthaul reliability while ensuring that: (i) latency results meet the standard requirements; and (ii) the overall system operates properly. Our results show that the proposed solution can improve fronthaul reliability while latency remains below a strict latency bound required by the 3rd Generation Partnership Project for this functional split. Full article

Reliability and security when distributing safety messages among vehicles in an extremely mobile environment are prominent issues in Vehicular Ad-Hoc Networks (VANETs). In VANET, data transfer becomes challenging because of inherent features such as excessive speed, geographically constrained topologies, unsteady communication links, diversity in the capacity of the channel, etc. A major challenge in the multi-hop framework is maintaining and building a path under such a rigid environment. With VANET, potency in the traffic safety applications has performed well because of the proper design of medium access control (MAC) protocols. In this article, a protocol is proposed pertaining to the distribution of safety messages named mobility-aware multi-hop clustering-based MAC (MAMC-MAC) to accomplish minimum communication overhead, high reliability, and delivery of safety messages in real-time environments. MAMC-MAC has the ability to establish clustering-based multi-hop sequence using the time-division multiple access (TDMA) technique. The protocol was specially developed for highway outlines to achieve network enhancement and efficient channel usage and guarantees integrity among the vehicles. The performance of the proposed protocol is evaluated using Network Simulator (NS-2), and it demonstrates its superiority over various standard protocols in terms of a number of quality-of-service (QoS)-based parameters. The criteria to select and assess these parameters are their sensitivity and importance to the safety-based applications they provide. Full article

With data transparency and immutability, the blockchain can provide trustless and decentralized services for Internet of Things (IoT) applications. However, most blockchain-IoT networks, especially those with a private blockchain, are built on top of an infrastructure-based wireless network (i.e., using Wi-Fi access points or cellular base stations). Hence, they are still under the risk of Single-Point-of-Failure (SPoF) on the network layer, hindering the decentralization merit, for example, when the access points or base stations get failures. This paper presents an Optimized Link State Routing (OLSR) protocol-based solution for that issue in a private blockchain-IoT application. By decentralizing the underlying network with OLSR, the private blockchain network can avoid SPoF and automatically recover after a failure. Single blockchain connections can be extended to multiple ad hoc hops. Services over blockchain become flexible to fit various IoT scenarios. We show the effectiveness of our solution by constructing a private Ethereum blockchain network running on IoT devices (i.e., Raspberry Pi model 4) with environmental data sensing (i.e., Particular Matter (PM)). The IoT devices use OLSR to form an ad hoc network. The environment data are collected and propagated in transactions to a pre-loaded smart contract periodically. We then evaluate the IoT blockchain network’s recovery time when facing a link error. The evaluation results show that OLSR can automatically recover after the failure. We also evaluate the transaction-oriented latency and block-oriented latency, which indicates the blocks have a high transmission quality, while transactions are transferred individually. Full article

Broadcasters are building studio architectures based on commercial off-the-shelf (COTS) IT hardware because of advantages such as cost reduction, ease of management, and upgradation. Media function virtualization (MFV) leverages IP networking to transport media streams between virtual media functions (VMFs), where they are processed. Media service deployment in an MFV environment entails solving the VMF-FG scheduling problem to ensure that the required broadcast quality guarantees are fulfilled. In this paper, we formulate the VMF-FG scheduling problem and propose a greedy-based algorithm to solve it. The evaluation of the algorithm is carried in terms of the end-to-end delay and VMF queuing delay. Moreover, the importance of VMF-FG decomposition in upgradation to higher-quality formats is also highlighted. Full article

Mobile ad hoc networks (MANETs) play a highly significant role in the Internet of Things (IoT) for managing node mobility. MANET opens the pathway for different IoT-based communication systems with effective abilities for a variety of applications in several domains. In IoT-based systems, it provides the self-formation and self-connection of networks. A key advantage of MANETs is that any device or node can freely join or leave the network; however, this makes the networks and applications vulnerable to security attacks. Thus, authentication plays an essential role in protecting the network or system from several security attacks. Consequently, secure communication is an important prerequisite for nodes in MANETs. The main problem is that the node moving from one group to another may be attacked on the way by misleading the device to join the neighboring group. To address this, in this paper, we present an authentication mechanism based on image hashing where the network administrator allows the crosschecking of the identity image of a soldier (i.e., a node) in the joining group. We propose the node joining and node migration algorithms where authentication is involved to ensure secure identification. The simulation tool NS-2 is employed to conduct extensive simulations for extracting the results from the trace files. The results demonstrate the effectiveness of the proposed scheme based on the memory storage communication overhead and computational cost. In our scheme, the attack can be detected effectively and also provides a highly robust assurance. Full article

We analyze the path from cryptocurrencies to official Central Bank Digital Currencies (CBDCs), to shed some light on the ultimate dematerialization of money. To that end, we made an extensive search that resulted in a review of more than 100 academic and grey literature references, including official positions from central banks. We present and discuss the characteristics of the different CBDC variants being considered—namely, wholesale, retail, and, for the latter, the account-based, and token-based—as well as ongoing pilots, scenarios of interoperability, and open issues. Our contribution enables decision-makers and society at large to understand the potential advantages and risks of introducing CBDCs, and how these vary according to many technical and economic design choices. The practical implication is that a debate becomes possible about the trade-offs that the stakeholders are willing to accept. Full article

In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics. Full article

The massive amount of data generated by social media present a unique opportunity for disaster analysis. As a leading social platform, Twitter generates over 500 million Tweets each day. Due to its real-time characteristic, more agencies employ Twitter to track disaster events to make a speedy rescue plan. However, it is challenging to build an accurate predictive model to identify disaster Tweets, which may lack sufficient context due to the length limit. In addition, disaster Tweets and regular ones can be hard to distinguish because of word ambiguity. In this paper, we propose a sentiment-aware contextual model named SentiBERT-BiLSTM-CNN for disaster detection using Tweets. The proposed learning pipeline consists of SentiBERT that can generate sentimental contextual embeddings from a Tweet, a Bidirectional long short-term memory (BiLSTM) layer with attention, and a 1D convolutional layer for local feature extraction. We conduct extensive experiments to validate certain design choices of the model and compare our model with its peers. Results show that the proposed SentiBERT-BiLSTM-CNN demonstrates superior performance in the F1 score, making it a competitive model in Tweets-based disaster prediction. Full article

Since Internet-based resources provides various and practical forms of English learning materials, Internet-based resources English learning is a common way for the younger generation. However, not like adult learning, university students need stronger motivation to learn English from Internet-based resources. This study surveyed Chinese college students in Central China to reveal the relationship between cultural intelligence, hedonic motivation, English self-efficacy, online experience quality, and willingness to continue learning online English. Using online media platforms and convenient sampling methods, a total of 385 questionnaires were collected. The data analysis was divided into three phases, descriptive analysis, measurement model evaluation, and structural equation model examination. The results showed Internet quality of experience significantly impacted English continuous learning intention. Cultural intelligence, English self-efficacy, and hedonic motivation all influenced significantly on Internet quality of experience and hedonic motivation had the strongest impact. In addition, the mediation effects of Internet quality of experience to these three factors and Internet-based resources English continuous learning intention all existed. Finally, the research results show cultural intelligence, English self-efficacy, and hedonic motivation were all examined significantly impacting Internet quality of experience statistically. English learning hedonic motivation is the most influencing factor. Therefore, English learning material should be attractive, fun, and enjoyable. This is what the teachers should think of and emphasize when to recommend learning material for students. Full article