Información de la Revista
Future Generation Computer Systems (FGCS)
Factor de Impacto:
Solicitud de Artículos
The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

The new Aims and Scope of FGCS will cover new developments in:

[1] Grid Applications and application support:

    Novel applications
    eScience and eBusiness applications
    Problem solving environments and virtual laboratories
    Grid economy
    Semantic and knowledge based grids
    Collaborative Grids and virtual organizations
    High Performance and high throughput computing on grids
    Complex application workflows
    Scientific, industrial and social implications
    Grids in education

[2] Grid methods and middleware:

    Tools for grid development: monitoring and scheduling
    Distributed dynamic resource management
    Grid- and web-services
    Information management
    Protocols and emerging standards
    Peer to peer and internet computing
    Pervasive computing
    Grid Security

[3] Grid Theory:

    Process specification; program and algorithm design
    Theoretical aspects of wide area communication and computation
    Scaling and performance theory
    Protocol verification

Última Actualización Por Dou Sun en 2022-09-11
Special Issues
Special Issue on Trust, Security, and Privacy in Metaverse
Día de Entrega: 2022-12-31

The term Metaverse has been coined to further facilitate the digital transformation in every aspect of our physical lives. As a virtual digital world that maps and interacts with the real world, the Metaverse is a vast, unified, enduring, and shared domain. With the supported by extended reality (XR) technology, digital twins, blockchain technology, communication technology (5G/6G), artificial intelligence (AI), cloud computing, etc., the real world can be seamlessly connected to the digital world through a Metaverse immersive experience. Therefore, a lot of work and life will take place in the virtual world, greatly promoting information consumption. At present, the development of the Metaverse is still in the infancy stage, and there will be a huge space for the expansion of the industry related to the Metaverse. In the future, the Metaverse will give birth to a series of new technologies, new formats, and new models to promote the transformation of traditional industries. Despite the novel potentials which could be enabled by the Metaverse ecosystem, this also imposes new trust, security, and privacy challenges for Metaverses because attackers will likely collect users’ personal information or destroy the Metaverse system by interacting or attacking networks and hardware devices. Therefore, it needs to develop novel methodologies to tackle these challenges as soon as possible, rather than waiting for the future when problems are already entrenched in the ecosystem, which will have serious implications for the social acceptability and future development of the Metaverse. This special issue solicits original and high-quality works on recent advances on trust, security, and privacy issues in the Metaverse. Topics of interest include, but are not limited to: - Authentication mechanisms - Quantum cryptography - Data mining and data analysis for trust and security - Security/privacy protocol design - Security and privacy of digital twins - Blockchain applications in the Metaverse - Intrusion detection and prevention systems for network security - Intrusion detection for IoT-based wearable devices - Trusted software and applications - XR and its related communication security - Privacy-preserving computation and Metaverse - Security and privacy of machine learning in the Metaverse - Adversarial examples in the Metaverse (attack and defense) - Trust, security, and privacy in cloud computing/edge computing - Novel theories, architectures, models, applications, and paradigms - Privacy preservation in AI-enabled networks - Trust, security, and privacy issues in the use of Metaverse underlying technologies (e.g., AI, digital twins, XR, blockchain, 5G/6G, cloud computing, etc.) - Miscellaneous trust, security, and privacy issues in the Metaverse
Última Actualización Por Dou Sun en 2022-09-11
Special Issue on Explainable AI Empowered Internet of Things for Indoor Navigation using WiFi Sensing
Día de Entrega: 2023-01-01

The role of AI (Artificial Intelligence), IoT (Internet of Things), and big data continues to increase as the 4th Industrial Revolution progresses. Navigation technology is the most effective application of the three technologies listed above. With the rapid development of wireless devices and appliances, and the emerging applications of IoT, wireless sensing applications have received wide attention in recent years. Given the plethora of location-based services (LBS), indoor localization using WiFi Sensing has piqued the interest of both academia and industry. These can be used in a variety of settings, including healthcare, government, public service, industry, military, retail, and arts and culture. Personal navigation, museum guidance, intrusion detection, wayfinding in a large shopping mall or hospital, asset monitoring, fleet and inventory management, maximizing efficiency in manufacturing or distribution are all examples of location-aware applications. The growing amount of available positioning data facilitates these applications due to ubiquitous connectivity and the IoT. This special issue aims at addressing the applications of WiFi signals using different metrics (i.e. RSSI and CSI) for indoor localization, human motion detection, human activity recognition, gesture recognition, and other related topics such as privacy and security. New and novel models for WiFi-based sensing applications for smart homes and IoT environments are main topics of this special issue. Topics of interest include, but are not limited to: - Device-free indoor navigation systems - Indoor positioning/localization - Indoor human motion detection - Human activity recognition (HAR) - Indoor positioning data analytics; - Data fusion of indoor positioning distributed sensors; - Fall detection - Privacy-enhancing for WiFi-based sensing systems - IoT monitoring systems - Intrusion detection - Applications for eldercare and vision-impaired using WiFi signals - Location-based services for assisted living applications - Location-based privacy and security for smart environments - Smart home and assisted living environments - Smart indoor security systems
Última Actualización Por Dou Sun en 2021-12-20
Special Issue on Future Generation ICT solutions for digital social innovation and sustainable development
Día de Entrega: 2023-01-15

Meeting the 17 Sustainable Development Goals (UN Agenda 2030 for Sustainable Development) requires acting and adopting strategies from varied fronts. Technological innovation has proved to be one of such fronts, and its potential should be harnessed and maximized to support sustainable development and deliver the highest impact. In this scenario, the good use of ICT and emerging frugal technologies is particularly urgent, considering that most SDGs focus on social good. Social good can be defined as something that benefits the largest number of people in the largest possible way. Examples are: clean air (SDG 3 and 11), clean water (SDG 6), healthcare (SDG 3), and literacy (SDG 4). In the attempt to address social good issues engaging communities and citizens through digital technologies, a new concept emerged: digital social innovation. This concept lies at the intersection of three areas: innovation, social and environmental problems, and digital technologies, and has a strong focus on helping communities in sharing data, collaborating to solve societal problems and scaling their initiatives focusing on open and distributed technologies and new sustainable business models. Despite the clear positive impact digital technology can have on social challenges, several open issues need to be considered when designing such technologies. Firstly, it is important to define which emerging and innovative i) hardware (such as intelligent sensors and IoT, wearable devices, TynyML arm devices), ii) digital strategies (such as blockchain, AI, gamification, big data), and iii) communication systems (such as LoRaWAN, 5G) exploit. Second, such future generation ICT solutions should ensure inclusiveness, accessibilities, appropriability, affordability, transferable, and sustainability. In order to achieve that, citizens’ and communities’ needs are crucial to design and develop ICT and services for social good and sustainable development, and thus, their needs should be taken into serious consideration both in the design phase and in further interactions. This Special Issue intends to elicit multidisciplinary contributions describing innovative applications and services, such as methods and tools, able to address the presented challenges adequately. As a result, this special issue will act as a forum for presenting research studies in emerging ICT solutions for sustainable development and digital social innovation. Topics of interest - IT for development and for education - Digital Democracy, Open data for transparency and disinformation - AI for social good and Social informatics - IT for smart living, Sustainable cities and communities - Frugal solutions for IT and Sustainable IT - Smart governance and e-administration - Citizen science and Civic intelligence - Environmental monitoring - ICT for Health and social care - Technology addressing the digital divide - Blockchain for social good - Ethical computing, Privacy, trust, and ethical issues in ICT solutions - Gamification, Serious game, and Game with a purpose
Última Actualización Por Dou Sun en 2022-06-04
Special Issue on Heterogeneous Information Network Embedding and Applications
Día de Entrega: 2023-02-01

Motivation and Scope With the continuous development of the Internet, massive data is generated. In the real world, different individuals interact with each other and these connections constitute a series of different graphs, also called as information networks. In recent years, graph representation learning (embedding) has become a hot research topic. Most of the related researches are conducted on homogeneous information networks that contains same type of nodes and edges. However, the real-world systems are actually more complex. Therefore, the concept of heterogeneous information network (HIN) consisting of multiple types of nodes and edges is put forward. HIN takes diverse node types and edge types into consideration and can comprehensively characterize the real scenarios. HIN has been promoted to a variety of applications, such as recommendation system. To better facilitate downstream tasks, heterogeneous information network embedding (HNE) is proposed, which aims to project graph data into low dimensional vectors in the embedding space where the topological information and semantics are preserved, which is an important research problem. Due to the heterogeneity of HIN, it is inappropriate to directly use traditional methods designed for homogeneous graphs to embed HIN. In recent years, HNE problem has attracted more and more research interests, and it is definitely worth studying. Besides, the potential of modeling scenarios in different research fields into heterogeneous networks for specific tasks has not been fully mined. Therefore, this special issue will have important significance and far-reaching influence on the following aspects: 1) Introducing emerging research and development in the field of HIN. 2) Studying the HNE problem with different techniques. 3) Promoting HIN to different application scenarios to enable researchers to take advantage of the power of graph data mining techniques. 4) Exploring interests, seeking potential cooperation, and promoting the HIN with other related fields. Topics of interest include, but are not limited to: Shallow models for heterogeneous network embedding. Deep neural network based heterogeneous network embedding. Auto-encoder based heterogeneous network embedding. Graph neural network based heterogeneous network embedding, including semi-supervised and self-supervised methods. Pre-training on heterogeneous networks. Dynamic heterogeneous network embedding. Application-oriented heterogeneous network embedding, including recommendation, identification and so on.
Última Actualización Por Dou Sun en 2022-10-22
Special Issue on Federated Learning on the Edge: Challenges and Future Directions
Día de Entrega: 2023-03-01

Federated learning, often referred to as distributed artificial intelligence/machine learning, is an approach that facilitates collaborative learning from large datasets belonging to different owners without compromising the privacy of each individual's raw data. FL is particularly useful if the required data is not open source or readily available for strategic or legal reasons. Additionally, it seeks to address upcoming privacy and data governance issues by adopting a collaborative model training approach without disclosing sensitive data. Federated Learning (FL) creates models in the periphery and shares them without necessarily exchanging data, with advantages on privacy and network traffic. For example, in medical research, this method allows hospitals and clinics to improve their Artificial Intelligence (AI) models by comparing them with others without sharing patient data. Even if anonymized, it is preferable not to leave the place where it have been generated and/or stored. Federated learning can address several critical challenges in edge computing networks: non-IID training data, limited communication, unbalanced contribution, privacy and security. As it is well-known, data availability is no longer a problem today. If we wanted to collaborate by sharing data? Let's think about the medical field and the experience of the Covid 19 pandemic. Creating a model that recognizes the disease from an X-ray would be extraordinary, but there are obstacles. The first is legal, linked to privacy and European regulation, because a hospital in Milan cannot freely share its data with a hospital in Madrid. The second is practical: even in the hypothesis of having the clearance of all managers of all the hospitals globally, a maxi-infrastructure would be needed that should be rented and managed. Where is it built? Who manages it? In fact, it is impractical. Yet the need for sharing is tangible. In this perspective, no longer a sensor that takes a picture and sends it, but a smart device that processes information on the spot by sending an alert in case of problems. If you train the device on edge, that is, directly in the field, federated learning allows you to propagate the learning of the single experience to all the other sensors without returning to the data center each time for training the model. This Special issue attempts to discuss novel research directions and contributions related to Federated Learning methodologies/solutions on the Edge. Topics of Interest include, but are not limited to: Federated Learning (FL) Applications. Distributed Learning Approaches. Privacy-Preserving Techniques in FL. Homomorphic Encryption Approaches in FL. Differential Privacy Approaches in FL. Incentive Mechanisms in FL. Interpretability in FL. FL with Unbalanced Data. Federated Transfer Learning Approaches. FL and Graph-based Approaches for Fraud Detection Poisoning Attacks and Countermeasures in Reliable Federated Learning. Case Studies and Applications of Federated Learning.
Última Actualización Por Dou Sun en 2022-10-22
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CCFNombre CompletoFactor de ImpactoEditorISSN
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bInteracting with Computers0.809Oxford University Press0953-5438
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cThe Journal of Strategic Information Systems11.02Elsevier0963-8687
Enterprise Information Systems1.908Taylor & Francis1751-7575
Programming and Computer Software0.105Springer0361-7688
cBehaviour & Information Technology1.388Taylor & Francis0144-929X
IEEE Transactions on Multi-Scale Computing SystemsIEEE2332-7766
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