Journal Information
Future Generation Computer Systems (FGCS)
http://www.journals.elsevier.com/future-generation-computer-systems/
Impact Factor:
7.187
Publisher:
Elsevier
ISSN:
0167-739X
Viewed:
49907
Tracked:
158
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Call For Papers
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


Last updated by Dou Sun in 2022-01-29
Special Issues
Special Issue on Integration of Communication, Computing, Caching and Learning (3C-L) for 6G Wireless Systems
Submission Date: 2022-07-01

This special issue aims to provide a forum for researchers and practitioners from academia and industry to present their latest research findings on the 3C-L integration for 6G wireless systems. Potential topics include, but are not limited to the following: - AI-based network design and resource allocation for efficient 6G wireless systems - AI for the modeling and analysis of integrating communication, computation and caching in 6G wireless systems - AI for computation offloading in 6G wireless systems - AI for edge caching in 6G wireless systems - Resource management and cross-layer design for AI-based 6G wireless systems - AI-inspired secure and intelligent resource management in 6G wireless systems - Efficient architecture and new protocol design for AI-based wireless systems - Intelligent data processing, communications, and integration in edge intelligence for 6G wireless systems - Efficient resource management for edge intelligence in 6G wireless systems - Performance analysis and evaluation for intelligent 6G wireless systems - Implementation/Testbed/Deployment for AI-based 6G wireless systems
Last updated by Dou Sun in 2021-12-20
Special Issue on Advances in Data Platform Design, Management, and Optimization
Submission Date: 2022-07-15

Big data has imposed a paradigm change in the way data is stored, managed, and queried, fostering the evolution of information systems into complex data platforms or ecosystems. Data platforms enable data-intensive storage, computation, and processing of heterogeneous data, but open to the risk of losing control over data. Collecting proper metadata significantly reduces this risk and supports better data management; this enables advanced functionalities such as data understanding and profiling, provenance control, orchestration of processing pipelines, incremental integration, and efficient querying. The challenges begin with the management of metadata itself in terms of the modeling effort, storage, complexity of retrieval activities, and effective exploitation; these problems are further amplified in the age of data science, which witnesses data scientists prevail over data architects. Since smart and comprehensive support for data scientists and architects to govern the data through the whole life-cycle is still lacking, the candidate papers for this special issue are innovative high-quality contributions positioned at the frontier of research on both theoretical and practitioner advancements of data platforms, with the goal to optimize and simplify the different aspects of (meta)data management and fruition. Besides addressing the Vs of big data, the enabled functionalities must cope with the heterogeneity of storage and computation engines - which include DBMSs supporting multiple data models and cloud storage systems with limited control and predictability – while meeting suitability requirements for less-skilled users. Topics of Interest The scope of the special issue includes but is not limited to the following topics. - Metadata modeling for data platforms - Techniques for metadata discovery and management - Advanced search, exploration, and profiling of data and metadata - Semantic enrichment of metadata - Data governance - Data wrangling - Provenance and data versioning control - Orchestration and optimization of data transformation pipelines - Data integration and querying in multimodel databases, multistores, polystores - Query processing, optimization, and performance - Entity resolution and data fusion - Big data management and querying - Artificial Intelligence solutions for data platforms - AutoML techniques - Cloud computing and architectures - Advanced architectures for data lakes and data platforms - Analysis, design, implementation, and testing of data platforms - Case studies and project experiences
Last updated by Dou Sun in 2021-12-20
Special Issue on Integration of Machine Learning and Edge Computing for Next Generation Smart Wearable Systems
Submission Date: 2022-09-30

Machine learning (ML) models provide an enabling technology for the development of intelligent computer systems. The capability of ML to learn an inference function from data represents a key strength for developing devices that can deal with complex, non-linear problems by making real-time decisions based on incoming information. Bringing ML to embedded systems is indeed an important requirement for building the next generation of intelligent devices. Under the paradigm of edge computing, ML may play a major role in empowering multiple application areas. The list of use cases includes sensors networks, industrial IoT, robotics, assistive technology, smart healthcare, prostheses and exoskeletons, connected vehicles, and many others. However, the deployment of a ML model on an embedded system faces major challenges. An embedded system imposes constraints in terms of energy consumption, processing speed, size, and cost. The constraint on energy consumption is particularly critical when battery-operated devices are involved. While powerful models (e.g., deep networks) can tackle difficult tasks such as visual recognition or natural language processing, the constrained resources of embedded systems might prevent direct deployment of the designed inference function into an edge device. An additional challenge is how to implement the training process on the device, which is especially relevant for the systems that can adapt online using, for instance, incremental learning. Online learning and adaptation is a critical function when tackling real life challenges in unpredictable and dynamically changing environments. This special issue aims at collecting manuscripts describing methodologies and systems that can sustain the integration of ML and edge computing by properly addressing the challenges discussed above. The focus will be on novel solutions that can stimulate significant improvements in wearable systems across different domains (e.g., consumer, sport technology, industry, robotics in healthcare, bionic limbs etc.). The topics of interest for this special issue include, but are not limited to - embedded machine learning - power-efficient machine learning implementations on FPGAs - online learning on resource-constrained edge devices - lightweight architectures for deep learning - high-performance, low-power computing for deep learning and computer vision - on-chip training of deep neural networks - edge-driven Intelligence for wearable device - intelligent sensors - assistive robots and bionic limbs empowered by edge computing - security of edge-based ML application - adversarial attacks to lightweight deep learning solutions
Last updated by Dou Sun in 2021-12-20
Special Issue on Cluster and Cloud Computing for Life Sciences
Submission Date: 2022-11-15

Computational methods are nowadays ubiquitous in the field of bioinformatics and biomedicine. Besides established fields like molecular dynamics, genomics or neuroimaging, new emerging methods like deep learning models rely heavily on large-scale computational resources. These new methods need to manage Tbytes or Pbytes of data with large-scale structural and functional relationships, TFlops or PFlops of computing power for simulating highly complex models, or many-task processes and workflows for processing and analyzing data. Today, many areas in Life Sciences are facing these challenges, such as biomodelling, predictive models of disease and treatment, evolutionary biology, medical biology, cell biology, biomedical image processing, biosignal sensoring or computer-supported diagnosis. Clouds, Edge/Fogs and Big Data Environments are promising to address research, clinical and medical research community requirements as they allow for significant reduction of computational time to run large experiments, for speeding-up development time for new algorithms, and to reduce barriers for large-scale multi-centric collaborations. The special issue will provide a forum for presenting research works showing advances of bioinformatics and medical applications using distributed IT systems, new ideas and approaches to successfully apply distributed IT-systems in translational research, clinical intervention, and decision-making, and novel proposal to tackle specific challenges in Life Sciences computing such as security, traceability, data interoperability, simulation of complex models, creation of cloud services, or application of artificial intelligence techniques to enhance decisions and to speed up processes. The special issue will be open to any author, but it will also invite extended versions of selected papers of CCGrid-Life 2022 workshop, held with CCGRID 2022, whose topics fit in the scope of this special issue. Each submission will be reviewed by at least three reviewers to ensure a very high quality of papers selected for the Special Issue. AREAS OF INTEREST This special issue of Future Generation Computing Systems will feature articles that address the following areas of interest: - Detailed application use-cases highlighting achievements and roadblocks - Exploitation of distributed IT resources for Life Sciences, HealthCare and research applications, for example medical imaging, disease modeling, bioinformatics, Public health informatics, drug discovery, clinical trials - Service and/or algorithm design and implementation applicable to medical and bioinformatic applications - Improved energy consumption of bioinformatic applications using clouds - Modeling and simulation of complex biological processes - Genomics and Molecular Structure evolution - Molecular Dynamics - Clouds for big data manipulation in bioinformatics and medicine - Ontologies and biomedical text mining - Biological data mining and visualization - Machine Learning in biomedical data analytics - Deep learning experiences in Life Sciences - Error handling and fault tolerance - Distributed and heterogeneous bioinformatic and medical data management - Big Medical and Bioinformatic Data applications and solutions - Data privacy, security and access control - Development environments for distributed bioinformatic applications - Programming paradigms and tools for bioinformatic applications - Scientific gateways and user environments targeting distributed medical and bioinformatic applications - Interoperability for exchanging data, algorithms and analysis pipelines
Last updated by Dou Sun in 2021-12-20
Special Issue on Explainable AI Empowered Internet of Things for Indoor Navigation using WiFi Sensing
Submission Date: 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
Last updated by Dou Sun in 2021-12-20
Special Issue on Future Generation ICT solutions for digital social innovation and sustainable development
Submission Date: 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
Last updated by Dou Sun in 2022-06-04
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