Evaluation of AI-Supported Input Methods in Augmented Reality Environment

Augmented Reality (AR) solutions are providing tools that could improve applications in the medical and industrial fields. Augmentation can provide additional information in training, visualization, and work scenarios, to increase efficiency, reliability, and safety, while improving communication with other devices and systems on the network. Unfortunately, tasks in these fields often require both hands to execute, reducing the variety of input methods suitable to control AR applications. People with certain physical disabilities, where they are not able to use their hands, are also negatively impacted when using these devices. The goal of this work is to provide novel hand-free interfacing methods, using AR technology, in association with AI support approaches to produce an improved Human-Computer interaction solution

An analytical framework in LEO mobile satellite systems servicing batched Poisson traffic

The authors consider a low earth orbit (LEO) mobile satellite system (MSS) that accepts new and handover calls of multirate service-classes. New calls arrive in the system as batches, following the batched Poisson process. A batch has a generally distributed number of calls. Each call is treated separately from the others and its acceptance is decided according to the availability of the requested number of channels. Handover calls follow also a batched Poisson process. All calls compete for the available channels under the complete sharing policy. By considering the LEO-MSS as a multirate loss system with ‘satellite-fixed’ cells, it can be analysed via a multi-dimensional Markov chain, which yields to a product form solution (PFS) for the steady-state distribution. Based on the PFS, they propose a recursive and yet efficient formula for the determination of the channel occupancy distribution, and consequently, for the calculation of various performance measures including call blocking and handover failure probabilities. The latter are much higher compared to the corresponding probabilities in the case of the classical (and less bursty) Poisson process. Simulation results verify the accuracy of the proposed formulas. Furthermore, they discuss the applicability of the proposed model in software-defined LEO-MSS

Quality management of surveillance multimedia streams via federated SDN controllers in Fiwi-iot integrated deployment environments

Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: The primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness

Editorial for the special issue on Energy‐efficient Networking

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136372/1/dac3311_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136372/2/dac3311.pd

Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions

The current study focuses on systematically analyzing the recent advances in the field of Multimodal eXplainable Artificial Intelligence (MXAI). In particular, the relevant primary prediction tasks and publicly available datasets are initially described. Subsequently, a structured presentation of the MXAI methods of the literature is provided, taking into account the following criteria: a) The number of the involved modalities, b) The stage at which explanations are produced, and c) The type of the adopted methodology (i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are discussed. Finally, a comprehensive analysis of current challenges and future research directions is provided.Comment: 26 pages, 11 figure

ARIES: a novel multivariate intrusion detection system for smart grid

The advent of the Smart Grid (SG) raises severe cybersecurity risks that can lead to devastating consequences. In this paper, we present a novel anomaly-based Intrusion Detection System (IDS), called ARIES (smArt gRid Intrusion dEtection System), which is capable of protecting efficiently SG communications. ARIES combines three detection layers that are devoted to recognising possible cyberattacks and anomalies against (a) network flows, (b) Modbus/Transmission Control Protocol (TCP) packets and (c) operational data. Each detection layer relies on a Machine Learning (ML) model trained using data originating from a power plant. In particular, the first layer (network flow-based detection) performs a supervised multiclass classification, recognising Denial of Service (DoS), brute force attacks, port scanning attacks and bots. The second layer (packet-based detection) detects possible anomalies related to the Modbus packets, while the third layer (operational data based detection) monitors and identifies anomalies upon operational data (i.e., time series electricity measurements). By emphasising on the third layer, the ARIES Generative Adversarial Network (ARIES GAN) with novel error minimisation functions was developed, considering mainly the reconstruction difference. Moreover, a novel reformed conditional input was suggested, consisting of random noise and the signal features at any given time instance. Based on the evaluation analysis, the proposed GAN network overcomes the efficacy of conventional ML methods in terms of Accuracy and the F1 score

Data Protection and Cybersecurity Certification Activities and Schemes in the Energy Sector

Cybersecurity concerns have been at the forefront of regulatory reform in the European Union (EU) recently. One of the outcomes of these reforms is the introduction of certification schemes for information and communication technology (ICT) products, services and processes, as well as for data processing operations concerning personal data. These schemes aim to provide an avenue for consumers to assess the compliance posture of organisations concerning the privacy and security of ICT products, services and processes. They also present manufacturers, providers and data controllers with the opportunity to demonstrate compliance with regulatory requirements through a verifiable third-party assessment. As these certification schemes are being developed, various sectors, including the electrical power and energy sector, will need to access the impact on their operations and plan towards successful implementation. Relying on a doctrinal method, this paper identifies relevant EU legal instruments on data protection and cybersecurity certification and their interpretation in order to examine their potential impact when applying certification schemes within the Electrical Power and Energy System (EPES) domain. The result suggests that the EPES domain employs different technologies and services from diverse areas, which can result in the application of several certification schemes within its environment, including horizontal, technological and sector-specific schemes. This has the potential for creating a complex constellation of implementation models and would require careful design to avoid proliferation and disincentivising of stakeholders. © 2022 by the authors. Licensee MDPI, Basel, Switzerland