Future Internet

This paper summarizes the work of many different authors, industries, and countries by introducing important and influential factors that will help in the development, successful adoption, and sustainable use of the Web3/metaverse and its applications. We introduce a few important factors derived from the current state-of-the-art literature, including four essential elements including (1) appropriate decentralization, (2) good user experience, (3) appropriate translation and synchronization to the real world, and (4) a viable economy, which are required for appropriate implementation of a metaverse and its applications. The future of Web3 is all about decentralization, and blockchain can play a significant part in the development of the Metaverse. This paper also sheds light on some of the most relevant open issues and challenges currently facing the Web3/metaverse and its applications, with the hope that this discourse will help to encourage the development of appropriate solutions. Full article

This study, focusing on identifying rare attacks in imbalanced network intrusion datasets, explored the effect of using different ratios of oversampled to undersampled data for binary classification. Two designs were compared: random undersampling before splitting the training and testing data and random undersampling after splitting the training and testing data. This study also examines how oversampling/undersampling ratios affect random forest classification rates in datasets with minority dataor rare attacks. The results suggest that random undersampling before splitting gives better classification rates; however, random undersampling after oversampling with BSMOTE allows for the use of lower ratios of oversampled data. Full article

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs’ performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services’ management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value. Full article

For the current stage of complex and changing network environments and correlated and synchronized vulnerability attacks, this study first fuses attack graph technology and Bayesian networks and constructs Bayesian attack graphs toportray the correlation relationships between vulnerabilities and discovering attackers’ intentions. Meanwhile, improving the Bayesian attack graph is difficult because it is difficult to achieve active updates and adapt to the changing network environment and other problems. The study proposed a detection method that integrated the Bayesian attack graph and the XGBoost incremental learning (IL) approach. Experiments showed that the IL model had an accuracy of 0.951, an accuracy of 0.999, a recall of 0.815, an F1 value of 0.898, and an Area Under Curve (AUC) value of 0.907. The prediction ability of this method was better than that of the base model. Bayesian attack graphs fused with IL can detect attacks in the network more efficiently and accurately, so the probability of each node in the network system being attacked can be updated in real time. Full article

In this paper, considering the problem that the common defensive means in the current cyber confrontation often fall into disadvantage, honeypot technology is adopted to turn reactive into proactive to deal with the increasingly serious cyberspace security problem. We address the issue of common defensive measures in current cyber confrontations that frequently lead to disadvantages. To tackle the progressively severe cyberspace security problem, we propose the adoption of honeypot technology to shift from a reactive to a proactive approach. This system uses honeypot technology for active defense, tempting attackers into a predetermined sandbox to observe the attacker’s behavior and attack methods to better protect equipment and information security. During the research, it was found that due to the singularity of traditional honeypots and the limitations of low-interactivity honeypots, the application of honeypot technology has difficulty in achieving the desired protective effect. Therefore, the system adopts a highly interactive honeypot and a modular design idea to distinguish the honeypot environment from the central node of data processing, so that the honeypot can obtain more sufficient information and the honeypot technology can be used more easily. By managing honeypots at the central node, i.e., adding, deleting, and modifying honeypots and other operations, it is easy to maintain and upgrade the system, while reducing the difficulty of using honeypots. The high-interactivity honeypot technology not only attracts attackers into pre-set sandboxes to observe their behavior and attack methods, but also performs a variety of advanced functions, such as network threat analysis, virtualization, vulnerability perception, tracing reinforcement, and camouflage detection. We have conducted a large number of experimental comparisons and proven that our method has significant advantages compared to traditional honeypot technology and provides detailed data support. Our research provides new ideas and effective methods for network security protection. Full article

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with features from the images containing seasonal changes. Our design features a unique scheme to ensure that the synthesized images contain the important features from both reference and patch images, covering seasonable features and minimizing the gap for the image-based localization tasks. The performance evaluation shows that the proposed framework can synthesize the views in various weather and lighting conditions. Full article

We introduce a new perspective for musical interaction tailored to a specific class of sonic resources: impact sounds. Our work is informed by the field of ubiquitous music (ubimus) and engages with the demands of artistic practices. Through a series of deployments of a low-cost and highly flexible network-based prototype, the Dynamic Drum Collective, we exemplify the limitations and specific contributions of banging interaction. Three components of this new design strategy—adaptive interaction, mid-air techniques and timbre-led design—target the development of creative-action metaphors that make use of resources available in everyday settings. The techniques involving the use of sonic gridworks yielded positive outcomes. The subjects tended to choose sonic materials that—when combined with their actions on the prototype—approached a full rendition of the proposed soundtrack. The results of the study highlighted the subjects’ reliance on visual feedback as a non-exclusive strategy to handle both temporal organization and collaboration. The results show a methodological shift from device-centric and instrumental-centric methods to designs that target the dynamic relational properties of ubimus ecosystems. Full article

The concept of a metaverse, a virtual world that offers immersive experiences, has gained widespread interest in recent years. Despite the hype, there is still a gap in its practical application, especially in the realm of education. This study presents the design and implementation of a metaverse tailored to the needs of education. The goal of this paper is to demonstrate the feasibility of such a system and evaluate its effectiveness. It is crucial to understand the architecture and implementation of a metaverse to effectively customise it for educational purposes. To assess user experience, a field study was conducted, collecting data through questionnaires and qualitative feedback. The results show that users were pleased with the features, player experience, and ease of use. Full article

Social media usage is a direct result of Internet connectivity and is gaining increased prominence in business-to-consumer (B2C), business-to-business (B2B), and consumer-to-business (C2B) relationship building, which is allowing marketers to devise and implement digital marketing strategies that are perceived as enhancing a customer’s well-being. Through the process of utilizing social media (SM) to share information with consumers, marketers are affording themselves with the concept of value co-creation and ensuring that the development of knowledge is given priority. To explain how this happens in an emerging economy, in-depth personal interviews were undertaken with the owners of five retail fashion companies in Vietnam. The findings indicate that fashion retail companies in Vietnam are deploying digital marketing strategies that deliver perceived enhanced value to consumers through the process of value co-creation. This highlights the advantage of using SM in relation to increasing retail staff’s ability to convert information into usable resources such as intelligence and knowledge. By achieving knowledge conversion, retail staff fulfill the role of ‘knowledge broker’ and ‘knowledge connector’, and identify how organizational intervention, such as new operating structures, can help to deliver perceived enhanced value to customers. Full article

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer’s experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model’s generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. Full article

Privacy and verifiability are crucial security requirements in e-voting systems and combining them is considered to be a challenge given that they seem to be contradictory. On one hand, privacy means that cast votes cannot be traced to the corresponding voters. On the other hand, linkability of voters and their votes is a requirement of verifiability which has the consequence that a voter is able to check their vote in the election result. These two contradictory features can be addressed by adopting privacy-preserving cryptographic primitives, which at the same time as achieving privacy, achieve verifiability. Many end-to-end schemes that support verifiability and privacy have the need for some voter action. This makes ballot casting more complex for voters. We propose the PVPBC voting system, which is an e-voting system that preserves privacy and verifiability without affecting voter usability. The PVPBC voting system uses an effective and distributed method of authorization, which is based on revocable anonymity, by making use of a permissioned distributed ledger and smart contract. In addition, the underlying PVPBC voting system satisfies election verifiability using the Selene voting scheme. The Selene protocol is a verifiable e-voting protocol. It publishes votes in plaintext accompanied by tracking numbers. This enables voters to confirm that their votes have been captured correctly by the system. Numerical experiments support the claim that PVPBC scales well as a function of the number of voters and candidates. In particular, PVPBC’s authorization time increases linearly as a function of the population size. The average latency associated with accessing the system also increases linearly with the voter population size. The latency incurred when a valid authentication transaction is created and sent on the DLT network is 6.275 ms. Empirical results suggest that the cost in GBP for casting and storing an encrypted ballot alongside a tracker commitment is a linear function of the number of candidates, which is an attractive aspect of PVPBC. Full article

As the Internet communication model changes from host-centric to content-centric, information-centric networking (ICN) as a new network architecture has received increasing attention. There are often multiple replicas of content in ICN, and how to reasonably utilize the characteristics of multiple replicas to further improve user experience is an important issue. In this paper, we propose a replica-selection algorithm, called the transmission completion time estimation (TCTE) algorithm. TCTE maintains the state of replica nodes in the domain with passive measurements in a limited domain of an enhanced name resolution system (ENRS), then estimates the transmission completion time of different replica nodes and selects the smallest one. When no replica is found in the ENRS domain, the nearest-replica algorithm will be used, so TCTE will not increase the traffic in the core network. Experiments show that TCTE not only effectively improves the user’s download rate and edge node throughput, reduces download rate fluctuations, reduces user download delay, and improves fairness, but also has universal applicability. Full article

Structural health monitoring of civil infrastructure, such as bridges and buildings, has become a trending topic in the last few years. The key factor is the technological push given by new technologies that permit the acquisition, storage, processing and visualisation of data in real time, thus assessing a structure’s health condition. However, data related to anomaly conditions are difficult to retrieve, and, by the time those conditions are met, in general, it is too late. For this reason, the problem becomes unsupervised, since no labelled data are available, and anomaly detection algorithms are usually adopted in this context. This research proposes a novel algorithm that transforms the intrinsically unsupervised problem into a supervised one for condition monitoring purposes. Considering a bridge equipped with N sensors, which measure static structural quantities (rotations of the piers) and environmental parameters, exploiting the relationships between different physical variables and determining how these relationships change over time can indicate the bridge’s health status. In particular, this algorithm involves the training of N models, each of them able to estimate the quantity measured via a sensor by using the others’ $N-1$ measurements. Hence, the system can be represented by the ensemble of the N models. In this way, for each sensor, it is possible to compare the real measurement with the predicted one and evaluate the residual between the two; this difference can be addressed as a symptom of changes in the structure with respect to the condition regarded as nominal. This approach is applied to a real test case, i.e., Candia Bridge in Italy, and it is compared with a state-of-the-art anomaly detector (namely an autoencoder) in order to validate its robustness. Full article

Nonorthogonal multiple access (NOMA), one of the favorable candidates of next-generation wireless networks combined with group device-to-device (D2D) networks, can sufficiently increase a system’s spectral efficiency. In fact, in a cooperative scenario, successive interference cancellation (SIC) is used in NOMA receivers to reduce the complexity of relaying, as each user has to decode high-order user data. This work presents a quality of service (QoS)-based cooperative NOMA-aided group D2D system (Q-CNOMA). The Q-CNOMA system not only reduces the burden on the group transmitter by relaying the signal to a receiver in neighboring cells but also improves the overall system performance. In order to model the major components in a D2D scenario such as receivers clustering around a transmitter, the spatial distribution of D2D transmitters is modeled using a Gaussian–Poisson process (GPP). A closed-form expression of outage probability is calculated and benchmarked against conventional systems to prove the superiority of the proposed Q-CNOMA system. Full article

To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio. Full article

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps. Full article

Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure. Full article

Satellite image compression technology plays an important role in the development of space science. As optical sensors on satellites become more sophisticated, high-resolution and high-fidelity satellite images will occupy more storage. This raises the required transmission bandwidth and transmission rate in the satellite–ground data transmission system. In order to reduce the pressure from image transmission on the data transmission system, a spaceborne target detection system based on Yolov5 and a satellite image compression transmission system is proposed in this paper. It can reduce the pressure on the data transmission system by detecting the object of interest and deciding whether to transmit. An improved Yolov5 network is proposed to detect the small target on the high-resolution satellite image. Simulation results show that the improved Yolov5 network proposed in this paper can detect specific targets in real satellite images, including aircraft, ships, etc. At the same time, image compression has little effect on target detection, so detection complexity can be effectively reduced and detection speed can be improved by detecting the compressed images. Full article

Process mining analyzes events that are logged during the execution of a process, with the aim of gathering useful information and knowledge. Process discovery algorithms derive process models that represent these processes. The level of abstraction at which the process model is represented is reflected in the granularity of the event log. When a process is captured by the usage of sensor systems, process activities are recorded at the sensor-level in the form of sensor readings, and are therefore too fine-grained and non-explanatory. To increase the understandability of the process model, events need to be abstracted into higher-level activities that provide a more meaningful representation of the process. The abstraction becomes more relevant and challenging when the process involves human behavior, as the flexible nature of human actions can make it harder to identify and abstract meaningful activities. This paper proposes CvAMoS, a trace-based approach for event abstraction, which focuses on identifying motifs while taking context into account. A motif is a recurring sequence of events that represents an activity that took place under specific circumstances depicted by the context. Context information is logged in the event log in the form of environmental sensor readings (e.g., the temperature and light sensors). The presented algorithm uses a distance function to deal with the variability in the execution of activities. The result is a set of meaningful and interpretable motifs. The algorithm has been tested on both synthetic and real datasets, and compared to the state of the art. CvAMoS is implemented as a Java application and the code is freely available. Full article