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Virtual reality (VR) and head-mounted displays (HMDs) are gaining popularity in rehabilitation. However, to be effectively integrated into clinical settings, these systems must offer advanced features such as telerehabilitation support, flexible rehabilitator-patient ratios, and real-time monitoring and interaction with virtual scenarios. To meet these requirements, this paper proposes a new system for the remote configuration and monitoring of VR-HMD rehabilitation exercises, enabling experts to interact with and manage patient-specific virtual scenarios. The system comprises two executables: one on the VR-HMD and another on an external monitoring device controlled by the expert, both coordinated by a shared Network Manager library. The paper evaluates the system by assessing time overhead (the duration when the patient is not actively engaged in rehabilitation) and system performance (including frames per second, network bandwidth usage, and monitoring effectiveness). This evaluation is compared with traditional HMD rehabilitation setups that lack rehabilitator interaction. Additionally, the system is reviewed in terms of development effort (steps and time required to adapt exercises for each monitoring method) and developers’ feedback on usability, functionality, ease of use, and future interest. Evaluation results indicate that the proposed system significantly reduces time overhead—by up to 85%—by minimizing the need to transfer the HMD between patient and rehabilitator. The system maintains performance with only a 2-5% decrease and limits network usage to under 350 kilobytes per second, ensuring fast and accurate monitoring. Developers have rated the system highly for usability and functionality, though ease of use needs improvement, and future interest depends on more streamlined development features and better documentation. Overall, the system enhances VR rehabilitation by facilitating seamless intervention, efficient monitoring, and device compatibility through low-bandwidth solutions and adjustable visual quality, thus improving control, interaction, and effectiveness.

Internet of Things (IoT) devices introduce new vulnerabilities to the network. These devices are relatively cheap, have simple design yet they can collect private user data, and be employed as botnets to conduct large-scale attacks. In general, IoT devices have a limited set of functionalities. Thus, the network administrator can formulate the expected traffic patterns of the devices and employ the network traffic to detect malicious activities. Existing systems to detect anomaly in IoT traffic mainly use machine learning. Thus, they require tuning the parameters of models and selecting/extracting a representative set of features from the network traffic data. In this paper, we introduce a novel approach Locality Sensitive Anomaly Detection and Identification (LSADI) to detect anomaly in IoT network traffic based on the locality-sensitive hash of the traffic flow. The proposed approach does not require feature selection/extraction from the data and does not have complex set of parameters that need to be tuned. Evaluation with three datasets containing 25 attacks shows that LSADI can detect and identify the type of anomalous flows with an accuracy above 90% on average and performs equally well compared to the state-of-the-art machine learning-based methods.

In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.

The increasing growth in complexity of vehicles’ functionalities is driving a technological shift in the design of software architectures in the automotive industry. Traditional signal-oriented networking is being replaced by service-oriented communications enabled by a new generation of Electronic Control Units (ECUs). The growing interest for full-fledged middlewares can be supported by these powerful ECUs. However, the new capabilities come at a non-negligible cost, which conflicts with the need to design a cost-effective solution that allows for meeting aggressive budget goals in a high volume market like automotive. In this paper, we illustrate how a significant part of the functionalities of a powerful middleware like Data Distribution Service (DDS) can be effectively implemented through hardware accelerators. We show that our approach can guarantee high performance while minimizing system complexity at the software level (e.g. AUTOSAR) by shifting painful or inefficient software implementations of QoS policies directly to hardware. This, in turn, allows to build cost-effective solutions suitable for next-generation automotive systems.

This work exploits the concept of one-class classifier applied to the problem of anomaly detection in communication networks. The article presents the design of an innovative anomaly detection algorithm based on polynomial interpolation technique and statistical analysis. The innovative method is applied to datasets largely used in the scientific community for bench-marking such as KDD99, UNSW-NB15 and CSE-CIC-IDS-2018, and further evaluated with application to a novel available dataset EDGE-IIOTSET 2022. The paper also reports experimental results showing that the proposed methodology outperforms classic one-class classifiers, such as Extreme Learning Machine and Support Vector Machine models, and rule-based intrusion detection system like SNORT. With respect to binary classifiers, this work has the advantage of not requiring any a-priori knowledge about attacks and is based on the collection of only normal data traffic.

A QKD network provides an additional security layer for IT-secure cryptographic key distribution that is added to existing conventional networks. Thus, QKD network components must be resilient to security challenges from conventional network environments. This paper provided a novel solution for designing a Key Management System resistant to DoS attacks. Our solution allows applications to function securely in environments with fewer keys. In addition, we have provided approaches for allocating and managing QKD resources to avoid malicious key reservations. Simulation experiments verified the proposed solutions.

This work addresses an often-overlooked challenge in designing datacenter traffic control schemes. Specifically, most existing schemes were designed for network topologies with non-blocking network core. While this enabled the development of elegant solutions, core networks in practice are more likely to be over-subscribed by the access layer network due to cost considerations. Consequently, existing traffic control schemes may perform sub-optimally in real-world over-subscribed datacenter networks. This work proposes a new centralized flow-based scheduling scheme called CAPTON which schedules flow according to remaining flow size and link capacities to avoid congestion in over-subscribed networks. CAPTON employs all-path transmission to exploit all available paths and redundant links between source and destination hosts to maximize resource utilization and eliminate the need for routing. Unlike switch-based approaches, CAPTON does not require additional support from network switches and thus can be readily deployed in current datacenters equipped with commodity switches.

As networks grow larger in size and complexity, their monitoring is becoming an increasing challenge because of the required polling performance and also due to heterogeneity of devices. As it turns out, SNMP (Simple Network Management Protocol) is by far the most popular monitoring protocol. However, due to the increase in the number of network devices, it becomes necessary to employ multiple SNMP managers, which is not cost-effective due to the hardware requirements. Additionally, the different proprietary SNMP implementations require custom configuration very often, as new devices are being incorporated into the network. Therefore, current SNMP managers not only require capabilities for large-scale monitoring but also a high degree of flexibility and programmability. In response, we propose an SNMP manager architecture with a flexible multi-threaded architecture, which effectively reduces the hardware resources necessary to poll the increasing number of SNMP agents. In addition, it features a scripting component to deal with the different data representations caused by proprietary implementations. Our experience has shown that SNMP agents can have high variability in their response times. Actually, our findings show a strong correlation between high response times and CPU load. As a solution, we propose and analyze novel adaptive polling algorithms that decrease the load on agents' CPUs while keeping the desired polling rate for fast agents. Finally, we present several real-world use cases where we show the benefits of the polling algorithms and the scripting component, by means of extensive measurement campaigns.

LoRa-based networks exhibit good flexibility in terms of configurable parameters and adjustable modulation properties. Thanks to this, wireless nodes can be tuned to improve their communication behavior. In fact, optimal network-level transmission configurations (Copt) can be derived in such a way that the global network performance is maximized. To derive this Copt, one must know the radio-propagation behavior of each node beforehand. Traditionally, this has been pursued by using general, low-precision, propagation models due to the infeasibility (in terms of time and energy) of deriving each individual node propagation behavior. In this work we propose a straightforward bounding technique that reduces up to 73% the energy and time required to obtain the radio-propagation behavior of each individual node in the network, enabling the derivation of network-level optimal transmission configurations. Also, we provide mechanisms to keep this knowledge updated, swiftly reacting to changes in the environment and leading to network performance improvements of 15% when compared to traditional alternatives like LoRaWAN ADR. Furthermore, by means of a testbed we demonstrate that this mechanism can also provide resistance to Denial-of-service attacks. Finally, we incorporate the power consumption into the proposed Copt formulation and provide a generalizable power-consumption determination methodology. This way we can limit the set of eligible transmission configurations to help extending LoRa network lifetimes more than 40%.

Blockchain technology has been developed with the vision to enable trusted collaboration between untrusted parties, without the need for a central authority. Despite its many promising applications, the technology suffers from a scalability problem. In order to increase transaction throughput and decrease transaction confirmation latency, payment channel networks have been proposed. Payment channel networks introduce a layer on top of the main chain, in which transactions can happen in a safe manner between only the transacting parties without burdening the entire network. In this article, we present and highlight the many interesting research aspects this new type of network introduces. We first provide background on the mechanics of the operation of payment channel networks, and then proceed to present a plethora of research problems of networking and/or economics flavor arising in this context, including routing, scheduling, rebalancing, network design and topology analysis, and fee optimization. This work is within the scope of both the networking and network economics communities.

This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.

Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effect- in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

Attackers usually use a command and control (C2) server to manipulate the communication. In order to perform an attack, threat actors often employ a domain generation algorithm (DGA), which can allow malware to communicate with C2 by generating a variety of network locations. Traditional malware control methods, such as blacklisting, are insufficient to handle DGA threats. In this paper, we propose a machine learning framework for identifying and detecting DGA domains to alleviate the threat. We collect real-time threat data from the real-life traffic over a one-year period. We also propose a deep learning model to classify a large number of DGA domains. The proposed machine learning framework consists of a two-level model and a prediction model. In the two-level model, we first classify the DGA domains apart from normal domains and then use the clustering method to identify the algorithms that generate those DGA domains. In the prediction model, a time-series model is constructed to predict incoming domain features based on the hidden Markov model (HMM). Furthermore, we build a deep neural network (DNN) model to enhance the proposed machine learning framework by handling the huge dataset we gradually collected. Our extensive experimental results demonstrate the accuracy of the proposed framework and the DNN model. To be precise, we achieve an accuracy of 95.89% for the classification in the framework and 97.79% in the DNN model, 92.45% for the second-level clustering, and 95.21% for the HMM prediction in the framework.

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

Today's modern technology relies increasingly on real-time distributed systems. Such systems could gain a remarkable amount of both academic and industrial attention in several fields. Real-time synchronization of the distributed sub-systems is one key success that raises many challenges. In this paper, we first discuss the existing time-triggered protocol known as TTEthernet. Furthermore, an analytical study made on flying dunlin birds is used here to adopt the same mechanism to optimize the synchronization process. The natural phenomenon consists of avian flocks performing manoeuvres with high accuracy in a small window of time. Inspired by this phenomenon, a real-time synchronization algorithm is proposed. Results reflect the reliability of the new algorithm and provide a remarkable percentage of optimization compared with other leading protocols.

Cloud technology is an attractive infrastructure solution that provides customers with an almost unlimited on-demand computational capacity using a pay-per-use approach, and allows data centers to increase their energy and economic savings by adopting a virtualized resource sharing model. However, resources such as graphics processing units (GPUs), have not been fully adapted to this model. Although, general-purpose computing on graphics processing units (GPGPU) is becoming more and more popular, cloud providers lack of flexibility to manage accelerators, because of the extended use of peripheral component interconnect (PCI) passthrough techniques to attach GPUs to virtual machines (VMs). For this reason, we design, develop, and evaluate a service that provides a complete management of cloudified GPUs (cGPUs) in public cloud platforms. Our solution enables an effective, anonymous, and transparent access from VMs to cGPUs that are previously scheduled and assigned by a full resource manager, taking into account new GPU selection policies and new working modes based on the locality of the physical accelerators and the exclusivity when accessing them. This easy-to-adopt tool improves the resource availability through different cGPUs configurations for end-users, whilst cloud providers are able to achieve a better utilization of their infrastructures and offer more competitive services. Scalability results in a real cloud environment demonstrate that our solution introduces a virtually null overhead in the deployment of VMs. Besides, performance experiments reveal that GPU-enabled clusters based on cloud infrastructures can benefit from our proposal not only exploiting better the accelerators, but also serving more jobs requests per unit of time.

The vision of Industry 4.0, otherwise known as the fourth industrial revolution, is the integration of massively deployed smart computing and network technologies in industrial production and manufacturing settings for the purposes of automation, reliability, and control, implicating the development of an Industrial Internet of Things (I-IoT). Specifically, I-IoT is devoted to adopting the IoT to enable the interconnection of anything, anywhere, and at any time in the manufacturing system context to improve the productivity, efficiency, safety, and intelligence. As an emerging technology, I-IoT has distinct properties and requirements that distinguish it from consumer IoT, including the unique types of smart devices incorporated, network technologies and quality-of-service requirements, and strict needs of command and control. To more clearly understand the complexities of I-IoT and its distinct needs and to present a unified assessment of the technology from a systems' perspective, in this paper, we comprehensively survey the body of existing research on I-IoT. Particularly, we first present the I-IoT architecture, I-IoT applications (i.e., factory automation and process automation), and their characteristics. We then consider existing research efforts from the three key system aspects of control, networking, and computing. Regarding control, we first categorize industrial control systems and then present recent and relevant research efforts. Next, considering networking, we propose a three-dimensional framework to explore the existing research space and investigate the adoption of some representative networking technologies, including 5G, machine-to-machine communication, and software-defined networking. Similarly, concerning computing, we again propose a second three-dimensional framework that explores the problem space of computing in I-IoT and investigate the cloud, edge, and hybrid cloud and edge computing platforms. Finally, we outline particular challenges and future research needs in control, networking, and computing systems, as well as for the adoption of machine learning in an I-IoT context.