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INTRODUCTION: This study introduces a groundbreaking approach that integrates Artificial Intelligence (AI) with macroeconomic analysis to address a critical gap in existing economic forecasting methodologies. By leveraging diverse economic data sources, the study aims to transcend traditional analytical boundaries and provide a more comprehensive understanding of macroeconomic trends. OBJECTIVE: The primary objective is to pioneer a scalable framework for economic data analysis by combining AI with macroeconomic analysis. The study aims to utilize advanced machine learning algorithms to analyze and synthesize macroeconomic indicators, offering enhanced accuracy and predictive power. A key focus is on dynamically incorporating real-time data to adapt to evolving economic landscapes. METHODS: The research employs advanced machine learning algorithms to analyze and synthesize macroeconomic indicators. The integration of AI allows for a more nuanced understanding of complex economic dynamics. The methodology uniquely adapts to real-time data, providing a scalable framework for economic data analysis. RESULTS: The findings demonstrate the model's efficacy in predicting economic trends, surpassing conventional models in both precision and reliability. The study showcases the potential of AI-driven economic analysis to offer insights into economic dynamics with unprecedented accuracy. CONCLUSION: This study significantly contributes to the fields of AI and economics by proposing a transformative approach to macroeconomic analysis. The integration of technology and economics sets a new precedent, paving the way for future innovations in economic forecasting. The research also explores the implications of AI-driven economic analysis for policy-making, emphasizing its potential to inform more effective economic strategies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Quantum computing technology has become a center of attention in various scientific disciplines, including economic analysis. The adoption of quantum computing in economic analysis offers tremendous potential to improve the processing of complex economic data and provide deep insights. However, the use of quantum technology in the context of distributed information systems also raises several challenges, including data security and the limitations of quantum technology. OBJECTIVE: This research aims to investigate the implications of adopting quantum computing in economic analysis, with a focus on distributed information systems. METHODS: This research was carried out using a descriptive qualitative approach, with data derived from the results of relevant research and previous studies. The collected data will be processed and analyzed to gain a deeper understanding of the adoption of quantum computing in economic analysis in distributed information systems. RESULTS: This research then finds that the adoption of quantum computing in economic analysis has the potential to increase efficiency, accuracy, and depth of economic insight. However, limitations of current quantum technologies, including quantum errors, limited scale of operations, and data security issues, limit their applications. In the long term, research and development will be key to overcoming these obstacles and maximizing the potential of this technology in economic analysis. CONCLUSION: The long-term implications include increased economic competitiveness and significant changes in the way economic decision-making is carried out, assuming that ethical and regulatory issues are also carefully considered. [ABSTRACT FROM AUTHOR]

The blockchain paradigm and its application of Distributed Ledger Technology (DLT) are now enabling technologies for the worldwide development of trustworthy and decentralized solutions. DLT offers enormous potential to accommodate the intricate architectural patterns of individual Distributed Information Systems (DIS). Despite its immense potential, the current research effort is not focused on deploying DLT inside the DIS area. We identified sixteen essential DIS Architectural Properties (APs) that offer issues for DIS system architects using Grounded Theory. Then, we assessed the capabilities of the major DLT platform and highlighted its prospects and limits with regard to supporting DIS APs. Using the meta-analysis research technique, we demonstrated that DLT might serve as a development platform for a blockchain-native DIS (BN-DIS) that enables design and development processes. A BN-DIS utilizes DLT infrastructure and blockchain design principles to expand internationally, support thousands of distributed nodes, and employ DIS APs unique to the Blockchain. Lastly, we give a case study illustrating how DLT handles DIS APs in a real-world context, giving empirical proof of the DLT’s efficacy in servicing DIS APs.

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach. [ABSTRACT FROM AUTHOR]

The distribution of information through web protocols is today based on the client-server model. Recently, decentralized protocols with greater availability appear as well as blockchain-based attestation methods, allowing for proving the existence of information. In combination, these methods promise a secure, decentralized and long-term storage. However, there exist two major problems: (1) the scalability of blockchains limits their storage capacity and (2) various (de)centralized web protocols are in use and could alleviate this problem, but they do not support blockchain-based attestations. In this paper, we extend an approach for blockchain-based attestation with compatibility for multi-protocol storage. Instead of specific protocols or blockchains, the extended approach aims to contribute novel concepts to the discussion on blockchain scalability. It augments the capabilities of existing protocols for applications such as certification or timestamping of digital artifacts. With the use of decentralized protocols such as IPFS, further availability and inherent resilience properties are gained, allowing for applications such as open research repositories and digital registries. We discuss the architecture of the extended approach, a possible implementation in a smart contract on the Ethereum blockchain with IPFS and Git, and evaluate the time and cost of attestations.

This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the $l_{2}$ norm minimization. Therefore, the localization performance may not be satisfying because the $l_{2}$ norm minimization is vulnerable to the large error. Therefore, we propose the convex combination of $l_{1}$ and $l_{2}$ norm because the $l_{1}$ norm minimization is effective in the large noise condition. The mixed-norm maximum Versoria criterion-based unscented Kalman filter, mixed-norm least lncosh unscented Kalman filter, mixed-norm maximum Versoria criterion iterative reweighted least-squares, mixed-norm least lncosh iterative reweighted least squares and closed-form localization approaches are proposed for mixed line-of-sight/ non-line-of-sight environments. The proposed mixed-norm unscented Kalman filter-based algorithms are more superior to the other methods as the line-of-sight noise level increases by the use of the convex combination of $l_{1}$ norm and $l_{2}$ norm. The iterative reweighted least sqaures-based methods employ a weight matrix. The closed-form weighted least squares algorithm has an advantage that its computational complexity is lower than that of other methods. Simulation and experiments illustrate the localization accuracies of the proposed unscented Kalman filter-based methods are found to be superior to those of the other algorithms under large noise level conditions.

One of the most promising enablers for the secure distributed operation of the Internet of Things (IoT) systems could be based on a mathematical construct widely known as blockchain that aims to neglect the system’s centralization and scalability properties. This paper aims to map the requirements and features of both systems, highlight the main co-existence challenges and technological candidates for smoother integration of IoT and blockchain, as well as provide the standartization outlook. Moreover, an architectural approach to an integrated solution is identified based on classic literature review methodology aiming to consider the IoT versus blockchain characteristics mapping and outlining related challenges. Critical solutions to address the integration bottlenecks include moving from Proof-of-Work (PoW) to Distributed Proof-of-Stake (DPoS) consensus, adding a Fog overlay to the architecture model, and leveraging the synergies combining the benefits of blockchain and IoT technology are highlighted.

Distributed Ledger Technology (DLT) is making the first steps toward becoming a solution for the growing number of various decentralized systems worldwide. Unlike pure Blockchain, DLT finds many uses across different industries, including eHealth, finance, supply chain monitoring, and the Internet of Things (IoT). One of the vital DLT features is the ability to provide an immutable and commonly verifiable ledger for larger-scale and highly complex systems. Today's centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of the involved nodes, complicated heterogeneous architectures, and task load, while the publicly available distributed systems are still in their infancy. This paper aims to provide an exhaustive topical review of the state-of-theart of Distributed Ledger Technology applicability in various sectors. It outlines the importance of the practical integration of technology-related challenges, as well as potential solutions. [ABSTRACT FROM AUTHOR]

The blockchain technology is currently penetrating different areas of the modern Information and Communications Technology community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect, i.e., solving the computational puzzle task. At the same time, the use of wearable and mobile devices may also become a part of eCommerce blockchain operation, especially during the on-charge time. The paper considers the possibility of using a large number of constrained devices to support the operation of the blockchain with a low impact on battery consumption. The utilization of such devices is expected to improve the system efficiency as well as to attract a more substantial number of users. This paper contributes to the body of knowledge with a survey of the main applications of blockchain for smartphones along with existing mobile blockchain projects. It also proposes a novel consensus protocol based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS) algorithms for efficient and on-the-fly utilization on resource-constrained devices. The system was deployed in a worldwide testnet with more than two thousand smartphones and compared with other projects from the user-experienced metrics perspective. The results prove that the utilization of PoA systems on a smartphone does not significantly affect the lifetime of the smartphone battery while existing methods based on PoW have a tremendous negative impact. Finally, the main open challenges and future investigation directions are outlined.

Given a large graph, such as a social network or a knowledge graph, a fundamental query is how to find the distance from a source vertex to another vertex in the graph. As real graphs become very large and many distributed graph systems, such as Pregel, Pregel+, Giraph, and GraphX, are proposed, how to employ distributed graph systems to process single-source distance queries should attract more attention. In this paper, we propose a landmark-based framework to optimize the distance computation over distributed graph systems. We also use a measure called set betweenness to select the optimal set of landmarks for distance computation. Although we can prove that selecting the optimal set of landmarks is NP-hard, we propose a heuristic distributed algorithm that can guarantee the approximation ratio. Experiments on large real graphs confirm the superiority of our methods.

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

The article detail with the application of the probabilistic approach to the calculation of the expected performance indicators of the digitalization strategy is substantiated. The process of formation of intellectual assets in the course of implementing this strategy has been analysed. The influence of distributed information systems on increasing the share of intellectual assets of an enterprise is proved.

Distributed information systems that support scientific and educational activities can work with various information systems. The main goal of creating a distributed information system supporting scientific and educational activities is to accelerate the pace and improve the quality of information exchange in the scientific environment. The paper considers technological methods for constructing models of information systems designed to support scientific and educational activities. The model under consideration is that the developed model of an information system for working with scientific materials should solve the problems of long-term storage of information, organizing data search by attributes, accumulating and replacing metadata. Based on the analysis of typical scenarios of information servers, the tasks that should be solved when organizing an access control system for distributed information resources are formulated. Within the framework of this technology, three access control models are discussed, which differ in the degree of integration of information server functions with the Z39.50 technologies. The creation and support of distributed information systems and electronic libraries that integrate heterogeneous information resources and operate in various software and hardware environments require special approaches to managing these systems. If the resources or data themselves can be managed locally, even for distributed information systems, then the task of managing access to distributed resources cannot be solved within the framework of local administration. The justification of the last thesis can be seen when considering typical scenarios of the information server, which we will describe below. [ABSTRACT FROM AUTHOR]

The article presents the development of a sign language recognition information system for people with hearing and / or voice impairments, allowing to improve the quality of life and interaction in society with other people. The device, software, functional blocks and information system subsystems are described. Examples of possible application and placement of the system in various spheres of public life are given. One of the types of implementation of the information system of gesture recognition is described in the local network. The system requirements are listed based on the characteristics of the life of the target audience of people and the life cycle of gesture recognition. The decomposition of information system subsystems was made on the basis of belonging to each of the program blocks: the client and the server.

The Industrial Internet of Things (IIoT) is a key element of industry 4.0, bringing together modern sensor technology, fog and cloud computing platforms, and artificial intelligence to create smart, self-optimizing industrial equipment and facilities. Though, the scale and sensitivity degree of information continuously increases, giving rise to serious privacy concerns. The scope of this article is to provide efficient privacy preservation techniques, by tracking the correlation of multivariate streams recorded in a network of IIoT devices. The time-varying data covariance matrix is used to add noise that cannot be easily removed by filtering, generating obfuscated measurements and, thus, preventing unauthorized access to the original data. To improve communication efficiency between connected IoT devices, we exploit inherent properties of the correlation matrices, and track the essential correlations from a small subset of correlation values. Extensive simulation studies using constrained IIoT devices validate the robustness, efficiency, and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is rapidly expanding, which will enable many devices to be installed in various environments. However, current IoT services cannot maximally utilize devices because of their silo model. To solve this problem, we aim to realize Open IoT, in which services share devices. In this paper, we propose an architecture for an object tracking service, one of the main services of Open IoT. The architecture uses video data from shared devices, such as surveillance cameras or pedestrians' smartphones. An important research task is to discover the most appropriate devices for a service out of a huge number of devices connected to the Internet. We named real-time data generated by devices “live data”and are trying to use these data to discover appropriate devices. However, it is difficult to collect and handle all live data in the cloud because of the network band limit. Therefore, we propose a distributed search architecture. Generally, distributed architecture uses network and computing resources less efficiently than cloud architecture. Our proposed architecture overcomes this by deploying a search function dynamically and copes with arbitrary searches. We developed a system that embodies our proposed architecture and evaluated its feasibility. An experiment simulating a moving object tracking service with network cameras is shown that the architecture reduces the communication bandwidth of the core network to 1000th or less of that when cloud computing is used. In addition, another experiment is shown that the architecture search speed is sufficient for a walking-person tracking service.

The Internet of Things (IoT) describes the concept of physical objects equipped with identifying, sensing, networking, and processing capabilities being connected to the Internet. Architectures for the IoT typically rely on transmitting data to centralized cloud servers for processing. Although cloud services are supposed to enhance the IoT in storage, computation, and communication capabilities, this approach often generates isolated data silos and requires trust in third parties operating the cloud servers, which become single point of failure. In addition, centralized cloud-based applications lack transparency and allow for undetected manipulation and concealment of IoT data. To overcome these downsides, we develop and evaluate a blockchain-based IoT sensor data logging and monitoring system, employing a design science research approach. In this article, we show that such systems should provide modularity, data parsimony, and availability in addition to domain-specific principles. The prototype improves data integrity and availability but uncovers challenges, such as high operating costs through smart contract computation fees. Furthermore, semistructured interviews with practitioners allowed us to derive insights for developing blockchain-based IoT ecosystems and reveal that cooperation with organizations is key for transferring solutions into production. We contribute to the IoT knowledge base by providing design principles as well as managerial and technological recommendations. [ABSTRACT FROM AUTHOR]

Active-passive dynamic consensus filters consist of agents subject to local observations of a process (i.e., active agents) and agents without any observations (i.e., passive agents). The key feature of these filters is that they enable the states of all agents to converge to the average of the observations only sensed by the active agents. Two sweeping generalizations can be made about existing active-passive dynamic consensus filters: 1) they utilize integral action-based distributed control algorithms such that each agent is required to continuously exchange both its current state and integral state information with its neighbors; and 2) they assume that the roles of active and passive agents are fixed; hence, these roles do not change with respect to time. The contribution of this paper is to introduce and analyze a new class of active-passive dynamic consensus filters using results from graph theory and systems science. Specifically, the proposed filters only require agents to exchange their current state information with neighbors in a simple and isotropic manner to reduce the overall information exchange cost of the network. In addition, we allow the roles of active and passive agents to be time-varying for making these filters suitable for a wide range of multiagent systems applications. We show that the proposed active-passive dynamic consensus filters enable the states of all agents to converge to an user-adjustable neighborhood of the average of the observations sensed by a time-varying set of active agents. We also generalize our results using event-triggered control theory such that agents schedule information exchange dependent on errors exceeding user-defined thresholds (not continuously). This generalization allows agents to further reduce the overall cost of interagent information exchange and to determine when to broadcast their information to their neighbors thus eliminating the need to synchronize their states. Four illustrative numerical examples and one experimental study are also presented to demonstrate our theoretical findings. [ABSTRACT FROM AUTHOR]

Poor Medication adherence causes significant economic impact resulting in hospital readmission, hospital visits and other healthcare costs. The authors developed a smartwatch application and a cloud based data pipeline for developing a user-friendly medication intake monitoring system that can contribute to improving medication adherence. The developed Android smartwatch application collects activity sensor data using accelerometer and gyroscope. The cloud-based data pipeline includes distributed data storage, distributed database management system and distributed computing frameworks in order to build a machine learning model which identifies activity types using sensor data. With the proposed sensor data extraction, preprocessing and machine learning algorithms, this study successfully achieved a high F1 score of 0.977 with 13.313 seconds of training time and 0.139 seconds for testing. [ABSTRACT FROM AUTHOR]

Practical implementation of the concept of a distributed information system requires the implementation of a complex scientific-technical problems related to the choice of composition and use of storage and processing of information, creation and use of common data arrays, information security, etc. Fundamentally, a modern distributed information system is a system of communication and information transmission as well as a storage and data processing system. Storage and data processing system is designed for organization of reliable and fault-tolerant data storage, high-performance servers to access storage devices and information processing. The measure of quality, and in some cases indicator of functioning of data storage and processing system is proposed. The tasks that need to be tackled when constructing and operating the storage and processing requirements of highly reliable data storage, as well as their following configurations: determination of the number and location of centers of data storage and processing in distributed information systems; choice of the composition of the complex used for storage in the storage centers and data processing, with the organization of a backup subsystem, the backup and restore data. The task of storage structure optimization according to the criterion of maximum values of the readiness factor of the storage system is formed. This problem is reduced to the kind of problems of integer linear programming with Boolean variables, this fact allows to apply the existing methods for its solvation. A method for determining the rational level of expenditure on the formation of the complex used for storage in the storage and data processing system based on the use of elements of probability theory and the theory of well-being (the principle of Pareto optimality) is developed. The optimal solution of the problem by optimizing a linear convolution is obtained.

Due to its very high potential, the Internet of Things (IoT) concept has been integrated in modern telemedicine systems. These systems enable real-time monitoring of patients at home, by using devices for acquiring various medical parameters or wearable devices that allow real-time monitoring of the medical signals. The data are transmitted to a medical specialist's office via the Internet and can be stored in cloud for further analysis. This article proposes an IoT architecture that can be used in healthcare for monitoring ECG signals independently acquired by the patient, using a mobile tele-electrocardiograph, without the help of a specialist. The main features of the mobile device are described, as well as how these features are integrated into the proposed IoT architecture. The article also tackles the security issues that may occur during the using of this system: integrity, confidentiality and authenticity.

The diffusion of mobile devices equipped with sensing, computation, and communication capabilities is opening unprecedented possibilities for high-resolution, spatio-temporal mapping of several phenomena. This novel data generation, collection, and processing paradigm, termed crowdsensing, lays upon complex, distributed cyberphysical systems. Collective data gathering from heterogeneous, spatially distributed devices inherently raises the question of how to manage different quality levels of contributed data. In order to extract meaningful information, it is, therefore, desirable to the introduction of effective methods for evaluating the quality of data. In this paper, we propose an approach aimed at systematic accuracy estimation of quantities provided by end-user devices of a crowd-based sensing system. This is obtained thanks to the combination of statistical bootstrap with uncertainty propagation techniques, leading to a consistent and technically sound methodology. Uncertainty propagation provides a formal framework for combining uncertainties, resulting from different quantities influencing a given measurement activity. Statistical bootstrap enables the characterization of the sampling distribution of a given statistics without any prior assumption on the type of statistical distributions behind the data generation process. The proposed approach is evaluated on synthetic benchmarks and on a real world case study. Cross-validation experiments show that confidence intervals computed by means of the presented technique show a maximum 1.5% variation with respect to interval widths computed by means of controlled standard Monte Carlo methods, under a wide range of operating conditions. In general, experimental results confirm the suitability and validity of the introduced methodology.

Deployment of commercial computer networks sets high requirements for procedures, tools, and approaches for comprehensive testing of these networks. However, in spite of the great efforts of many researchers, the process of test design/generation still tends to be unstructured and bound to the personal experience and/or intuition of individual engineers. To address the problem, this paper introduces an approach of automated generation of abstract test cases based on the concept of multilayer networks. Test cases of that kind cover network infrastructures, including individual components and component-to-component interactions on all coexisting architectural layers, and provide information for subsequent analysis to ensure that the used formal model is consistent with respect to test requirements.

The paper deals with university knowledge transfer. The adjustment of relations between universities and enterprises makes knowledge transfer a necessary requirement for university development to ensure the implementation of market-oriented educational programs. The paper proposes use of modular competency-based and ontological approaches to ensure the university knowledge transfer. The initial data is presented in the databases of the university, authorized bodies of education and science, enterprises, business structures and professional standards. This data is processed and scanned for knowledge to be put into the knowledge database with technological (informational) and modular competency-based approaches. As part of a market-oriented innovation university concept the task is to improve the education system in relation to skills development. To solve this problem it is necessary to build a distributed information system of university knowledge transfer. It is necessary to create a unified educational space - an educational portal - to ensure the university knowledge transfer between all participants - teachers, students and employers. The study offers an architectural solution for a distributed information system.

Semantic web is the growing field whose integration with Distributed Information System helps it in gluing technology. The framework of Semantic web deals with the representation, logic, rules and trust. We in this work surveyed the representation aspect of Semantic web by exploring ontologies proposed in various domains in the last three years. We observed that the research trends in semantic web for distributed information system is going in two directions i) theoretical framework ii) development of ontologies across different domains. We limited our work towards the domain of distributed information systems. Several detailed ontologies have been identified that are developed for integration with distributed information system across different domains. We conclude that ontologies need extensive work in its foundation, while more domains should be further explored in the development of robust information system in distributed environment.

Modern communication and computation systems often consist of large networks of unreliable nodes. Still, it is well known that such systems can provide aggregate reliability via redundancy. While duplication may increase the load on a system, it can lead to significant performance improvement when combined with the judicious management of extra system resources. Prime examples of this abstract paradigm include multi-path routing across communication networks, content access from multiple caches in delivery networks, and master/slave computations on compute clusters. Several recent contributions in the area establish bounds on the performance of redundant systems, characterizing the latency-redundancy tradeoff under specific load profiles. Following a similar line of research, this paper introduces new analytical bounds and approximation techniques for the latency-redundancy tradeoff for a range of system loads and a class of symmetric redundancy schemes, under the assumption of Poisson arrivals, exponential service-rates, and fork-join scheduling policy. The proposed approach can be employed to efficiently approximate the latency distribution of a queueing system at equilibrium. Various metrics can subsequently be derived for this system, including the mean and variance of the sojourn time, and the tail decay rate of the stationary distribution. This paper also establishes the stability region in terms of arrival rates for redundant systems with certain symmetries. Finally, it offers selection guidelines for design parameters to provide latency guarantees based on the proposed approximations. Findings are substantiated by numerical results. [ABSTRACT FROM AUTHOR]

We investigate the distance properties of linear locally recoverable codes (LRC codes) with all-symbol locality and availability. New upper and lower bounds on the minimum distance of such codes are derived. The upper bound is based on the shortening method and generalized Hamming weights that are fundamental parameters of any linear codes with many useful applications. This bound improves existing upper bounds. To reduce the gap in between upper and lower bounds, we do not restrict the alphabet size and propose explicit constructions of codes with locality and availability via rank-metric codes. The first construction relies on expander graphs and is better in low rate region. The second construction utilizes the LRC codes developed by Wang et al. as inner codes and is better in high rate region. We also suggest one possible generalization of LRC codes in which the recovering sets can intersect in a small number of coordinates. This feature allows us to increase the achievable code rate and still meet load balancing requirements. We derive upper and lower bounds on the parameters of such codes and present explicit constructions of codes with such a property. [ABSTRACT FROM AUTHOR]

Traditionally, the overlap between electronics and aerospace technology was limited to avionics, radars, satellites, and telecommunications. Sensors networks are emerging as an additional area of relevant interaction. In fact, in addition to the classical paradigm in which on-board sensors improve the navigation and control of aerial vehicles, e.g., in the fly-by-wire context, novel paradigms of cooperation between fixed networks of ground sensors and airborne ones are emerging today, especially for monitoring the environment. In this paper, we review current development trends and open challenges with reference to practical examples. We focus on the role of sensor miniaturization, energy harvesting, energy-aware communication architectures, and data fusion. Multidisciplinary development teams, with expertise spanning from microelectronics and data analysis, to operation of UAV swarms are expected to be necessary to address these challenges. [ABSTRACT FROM AUTHOR]

A description, the scientific and technical tasks, and the development projects of the system of objective remote monitoring of facilities and resources in Russia are presented. The main objective of such a system is to obtain objective operational information for assessing the current state of regions, their resource potential, planning directions for the development of the country, and objective monitoring of the implementation of infrastructure projects. The proposed system can be an integral part of complex scientific and technological projects and programs aimed at solving the problems of connectedness of the territory of Russia. [ABSTRACT FROM AUTHOR]

In the distributed processing environment characteristic of the management information systems of today, a need to superimpose an additional information flow onto an existing computer communication network arises frequently. The situation may result from a company's expanding needs for communication over its private or a shared network, or because a new user company is joining an existing shared network. In order to accommodate the new information flow without negatively affecting the waiting times for the previously contracted communications, network capacity may have to be expanded. We present an algorithmic method for least-cost capacity expansion of a set of network paths preselected to carry the incremental information flows. Owing to the insights gained as a result of extensive computational experiments, we then provide a simplified technique applicable to a broad class of cases. Polynomial time complexity of the algorithms included in the presented methodology points to its practicality. [ABSTRACT FROM AUTHOR]

Invisible to all but small groups of elite practitioners, distributed computing (DC) is a foundational technology that undergirds much of the modern global economy. Hosted by massive data centers humming with thousands (or hundreds of thousands) of servers, DC facilitates e-commerce, social media, logistics, government, finance, gaming, and other mass-digital phenomena for hundreds of millions of people every day. Without it, many of today?s online services would either not exist or be greatly diminished in usefulness. [ABSTRACT FROM AUTHOR]

This paper presents the Virtual Research Environment (VRE) enabling two European GEO Geohazard Supersites in Italy. According to GEO (Group on Earth Observation) vision, Geohazard Supersites provide access to spaceborne and in-situ geophysical data and models for selected sites prone to natural hazards -noticeably, earthquakes and volcano eruptions. The VRE was implemented in the framework of the Mediterranean Supersite Volcanoes (MED-SUV) project, funded by the European Commission. MED-SUV realized one of the European supersite demonstrators covering the two Permanent Supersites selected in Italy: Mt. Etna and Campi Flegrei/Vesuvius. The MED-SUV VRE provides advanced services for heterogeneous data and information management and sharing. MED-SUV started identifying the main supersite requirements including: the interoperability with existing data/information supply systems, the support of policy-based access control, the access to processing capabilities provided by external platforms, the management resources for publishing and sharing new products, the integration with significant global systems such as GEOSS and EPOS. MED-SUV adopted a System of Systems (SoS) approach to address interoperability with the identified heterogeneous systems supplying data and information. The SoS approach is based on a brokering architecture, where a specialized component (i.e the MED-SUV Broker: MSB) connects the existing and next-coming data sources leaving them autonomous. MSB carries out all the necessary mediation and harmonization tasks exposing standard interfaces enabling the interconnection with external systems like GEOSS and EPOS. In addition, MSB is accessible via a JavaScript library implementing Web APIs to facilitate the development of Web and mobile applications. [ABSTRACT FROM AUTHOR]