Información del Diario
Future Generation Computer Systems
http://www.journals.elsevier.com/future-generation-computer-systems/
Factor de Impacto:
3.997
Editor:
ELSEVIER
ISSN:
0167-739X
Visto:
14645
Rastreado:
50

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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 Xin Yao en 2017-08-21
Special Issues
Special Issue on Security, Trust and Privacy in Cyber (STPCyber): Future Trends and Challenges
Día de Entrega: 2017-09-29

"Future Generation Computer Systems", a forum for the publication of peer-reviewed, high-quality original papers in the computer systems sciences, focusing specifically advances and challenges in Cybersecurity involving complex computer systems and communication networks having security, trust and privacy being major issues. This is seeking original manuscripts for a Special Issue on Security, Trust and Privacy in Cyber (STPCyber): Future trends and Challenges scheduled to appear in the second half of 2018. With rapid advancements in Cyber security involving increased complexity of computer systems and communication networks, user requirements for Trust, Security and Privacy are becoming more and more demanding. Therefore, there is a grand challenge that traditional security technologies and measures may not meet user requirements in open, dynamic, heterogeneous, mobile, wireless, and distributed computing environments which are key domains of Cyber Security. As a result, we need to build systems and networks in which various applications allow users to enjoy more comprehensive services while preserving Security, Trust and Privacy at the same time. As useful and innovative technologies, trusted computing and communications are attracting researchers with more and more attention. The special issue will provide a forum for presenting research works showing advances on Security, Trust and Privacy for cyber infrastructures, including new platforms, system software enhancements, security algorithm design and optimization and technologies in complex computer systems and communication networks to defend against known and unknown behaviour of bad guys. The special issue will also be open to any author, but it will also invite extended versions of the selected papers of Trustcom 2017 conference 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.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on Big Data and Internet of Things – Fusion for different services and its impacts
Día de Entrega: 2017-09-30

Big Data and Internet of Things (IoT) have produced profound impacts to our everyday life and are hands in hands to offer better quality of services, better fusion of technologies, instant communications and express deliveries of services. The fusion between Big Data and IoT can produce positive impacts in the next-generation of our development in smart cities, national planning and forecasting of our future activities and investments. Big Data and IoT fusion can be pervasive to our daily life in healthcare, finance, security, transportation and education. To enable next generation of different services, we need to understand and realize the significance of fusion between hardware and software, and between security and reality. By doing so, we can get very light and portable devices that can contain petabytes of data, which need layers of security functions and services to make them protected. We can also use one device that can be a mobile phone, instant messenger, video conferencing center, GPS, database, investment analytics, weather forecaster, camera and data processing center. We can also provide real time security services that can destroy a vast variety of Trojans and viruses, block all security breaches, restore things back to normal and keep the owners alert and safe in real time. Big Data and IoT fusion can help high-tech sectors such as weather forecasting, space technology and biotechnology to enable thousands of simulations to be completed in seconds. All these high tech features have become reality and not just in movies enabled by the impacts of Big Data and IoT fusion. In this call, we seek high quality papers that can demonstrate proofs-of-concept, services, solutions for research challenges, case studies, analytics, real world examples and successful deliveries of Big Data and IoT fusion. Top papers from the international conference on Big Data Analytics and Business Intelligence http://www.xjtlu.edu.cn/en/events/2017/06/international-conference-on-big-data-analytics-and-business-intelligence at Xi’an Jiaotong Liverpool University in China will also be invited and authors must add new contributions of another 60% and above. Topics Submissions could consist of theoretical and applied research in topics including, but not limited to: Big Data and IoT fusion for health informatics and medical services Big Data and IoT fusion for business intelligence and finance Big Data and IoT fusion for modern education Big Data and IoT fusion for energy applications and services Big Data and IoT fusion for natural science, weather forecasting and earth science Big Data and IoT fusion for smart cities Big Data and IoT fusion for security, privacy and trust Big Data and IoT fusion for 5G networks and communications Big Data and IoT fusion for mobile services and computing Big Data and IoT fusion for the next generation architecture Big Data and IoT fusion for any forms of predictive modeling and analytics Big Data and IoT fusion for real world examples and case studies
Última Actualización Por Dou Sun en 2017-01-15
Special Issue on Recent Advances in Big Data Analytics, Internet of Things and Machine Learning
Día de Entrega: 2017-09-30

Big data analytics is a rapidly expanding research area spanning the fields of computer science, information management, and has become a ubiquitous term in understanding and solving complex problems in different disciplinary fields such as engineering, applied mathematics, medicine, computational biology, healthcare, social networks, finance, business, government, education, transportation and telecommunications. The utility of big data is found largely in the area of Internet of Things (IoT). Big data is used to build IoT architectures which include things-centric, data-centric, service-centric architecture, cloud-based IoT. Technologies enabling IoT include sensors, radio frequency identification, low power and energy harvesting, sensor networks and IoT services mainly include semantic service management, security and privacy-preserving protocols, design examples of smart services. To effectively synthesize big data and communicate among devices using IoT, machine learning techniques are employed. Machine learning extracts meaning from big data using various techniques which include regression analysis, clustering, bayesian methods, decision trees and random forests, support vector machines, reinforcement learning, ensemble learning and deep learning.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on Internet of Knowledge
Día de Entrega: 2017-09-30

Information quantity has rapidly increased on the web recently. Data size has also increased dramatically as multimedia data, which include visual information and auditory information, and has been used more and more in addition to the existing form of text data. It needs the semantic representation in human language to reduce the semantic gap between low-level and high-level characteristics; considering not only the low-level characteristics but also the high-level ones with the use of heterogeneous knowledge such as large scale text, image, video and so forth. In this context, it is worth noting research that combines heterogeneous knowledge aspects with achievements in designing advanced systems for the acquisition and sophisticated semantic analysis of complex data patterns, group behaviors, and visual information and repositories. Also, advanced radio access technologies are required to support above applications under wireless environments for forthcoming 5G system. Finally, security and privacy concerns when mining and classifying the knowledge collected by personal sensing devices or accessed by external services such as health information systems, city management platforms or Internet of Things is of pivotal importance so as to avoid exposing personal and critical data towards malicious persons or organizations. Therefore, it is demanding to propose proper means to avoid information leakage or falsification without compromising the possibility of performing complex information extraction, inference or classification. This special issue aims at bringing together leading researchers and practitioners from academia, government, and industry to discuss novel research contributions related to Semantic Approaches for Knowledge Classification within the context of various platforms.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on Benchmarking Big Data Systems
Día de Entrega: 2017-10-15

There is no doubt that we are living in the era of Big Data where we are witnessing the radical expansion and integration of digital devices, networking, data storage, and computation systems. For about a decade, the Hadoop framework has dominated the world of Big Data processing, however, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and Big Data processing scenarios. Thus, the Hadoop framework has been slowly replaced by a collection of engines dedicated to specific verticals such as structured data (e.g., Apache Hive, Impala, Presto, Spark SQL), graph data (e.g., Pregel, Giraph, GraphX, GraphLab), streaming data (e.g., Apache Storm, Apache Heron, Apache Flink, Samza) and many others. Even though several big data processing and analytics systems have been introduced with various design architectures, we are still lacking a deeper understanding of the performance characteristics for the various design architectures in addition to lacking comprehensive benchmarks for the various Big Data platforms. There is a crucial need to conduct fundamental research with a more comprehensive performance evaluation for the various Big Data processing systems and architectures. We also lack the availability of validation tools, standard benchmarks, and system performance prediction methods that can help us have a deeper and more solid understanding of the strengths and weaknesses of the various Big Data processing platforms.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on Security and Privacy for RFID and IoTs
Día de Entrega: 2017-10-15

AIMS & SCOPE Radio Frequency Identification (RFID) is a technology for automatic identification of remote people and objects without line of sight. The deployment and use of RFID technology is growing rapidly across many different industries. It cannot only be used in traditional applications (e.g., asset or inventory tracking), but also in security services such as electronic passports and RFID-embedded credit cards. At the same time, the Internet of Things (IoT), which will represent the backbone of modern society and the next-generation Internet, have showed a strong potential to meet the information-processing demands of smart environments. However, RFID and IoTs may also bring great challenges for the security and privacy of curernt systems and processes. For example, with the rapid deployment of RFID and a nature of wireless network, a number of concerns regarding security and privacy have been raised, e.g., clandestine tracking and inventorying. On the other hand, certain IoT applications will be tightly linked to sensitive infrastructures and strategic services, like the distribution of water and electricity. As a result, there is a great need to design and implement privacy and security technologies for RFID and IoTs in different domains. This special issue will focus on RFID and IoTs, and attempts to solicit original research papers that discuss the security and privacy issues and opportunities. Topics of interest include, but are not limited to: The goal of this special issue is to collect high-quality contributions to address the security and privacy concerns for RFID and IoTs. Topics of interest include, but are not limited to the ones listed below. Adversarial modeling for RFID and IoTs Vulnerability Assessment and testing for RFID and IoTs Intrusion detection and prevention schemes for RFID and IoTs Tracing back mobile attackers for RFID and IoTs New applications for secure RFID and IoTs Lightweight privacy-preserving RFID protocols & systems Efficient implementation of lightweight cryptographic protocols for RFID and IoTs Cryptographic hardware development for RFID and IoTs Design and analysis of fast and compact RFID based cryptographic algorithms Formal methods for analysis of lightweight cryptographic protocols for RFID and IoTs Security and privacy issues in RFID and IoTs Low-cost side-channel countermeasures for RFID and IoTs Side-channel analysis of exist protocols and implementations for RFID and IoTs Submission Guidelines: Authors should prepare their manuscript according to the Guide for Authors available from the online submission page of the Future Generation Computer Systems at https://www.elsevier.com/journals/future-generation-computer-systems/0167-739x/guide-for-authors. Detailed journal description can be found at http://www.elsevier.com/locate/fgcs. All submitted papers must contain only original work, which has not been published by or is currently under review for any other journal or conference. Authors should select “SI: SP for RFID and IoTs” when they reach the “Article Type” step in the submission process.. All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the Corresponding Guest Editor.
Última Actualización Por Dou Sun en 2017-01-15
Special Issue on Intelligent Algorithms and Standards for Interoperability in Internet of Things
Día de Entrega: 2017-10-31

Interoperability allows the interfaces of a system to work with other system without any restricted access or implementation. This interoperability can be syntactic (intercommunication and data exchange between two or more systems), semantic (automatically interpret the information exchanged meaningfully and accurately in order to produce users defined useful results) or cross domain (Multiple social, organizational, political, legal entities working together for a common interest and/or information exchange) from the perspective of internet connected objects i.e. Internet of Things (IoT). Deployment of these objects put forth a long list of strategic, operational, tactical and technological challenges especially from the perspective of interoperability. Interoperability is one of the biggest barriers keeping businesses from adopting the IoT. Lack of related standards and algorithms significantly increase the complexity, inefficiencies, customer frustration and the cost as well. Exhibiting the intelligence by the IoT objects can adhod big contribution in making this interoperability possible. To resolve thisissue, innovative list of solutions can be hired from computational intelligence domain (Fuzzy Logic, Neural Networks, Artificial Intelligence, Swarm Intelligence, and Genetic Algorithms), Machine learning, Deep learning and their state-of-the-art extensions. Future Generation Computer Systems Journal is soliciting high quality manuscripts presenting original contributions for its specialissue. Thisspecialissueaims to provide a forum that brings together researchers from academia, practicing engineers from industry, standardization bodies, and government to meet and exchange ideas on Intelligent Algorithms and Standards for Interoperability in Internet of Things. Topics of interests include (but are not limited to) the following categories: - Intelligent standards and algorithms for infrastructures, platforms, architectures and designs supporting Interoperability in IoT - Intelligent Algorithms and Standards for interoperability in IoT environment at various levels (data, device, middleware, networking, and application service) - Intelligent Semantic Interoperability Solutions for the IoT - Messaging protocols (MQTT, CoAP, AMQP, and REST) within IoT to enhance the interoperability of various interactive systems - Security- and privacy-aware IoT - Monitoring performances and QoS in applications based on heterogeneous IoT solutions. - Intelligent algorithms for gap Analysis of interoperability in IoT - Intelligent standards and algorithms for Interoperability between different IoT implementations (Agriculture, smart cities, military, surveillance, ocean, transportation and logistics, m-Heath, etc.) - Survey on suitability of intelligent standards and algorithms for interoperability and heterogeneity in IoT infrastructures - Formal and informal methods for intelligent interoperability in IoT - Intelligent algorithms standards for low cost interoperability in IoT - Low power signal processing for embedded IoT devices - Signal processing algorithms and platforms for IoT big data processing
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on New Landscapes of the Data Stream Processing in the era of Fog Computing
Día de Entrega: 2017-11-03

Nowadays, an increasingly connected ecosystem of heterogeneous devices is continuously producing unbounded streams of data that have to be processed "on the fly" in order to detect operational exceptions, deliver real-time alerts, and trigger automated actions. This paradigm extends to a wide spectrum of applications with high socio-economic impact, like systems for healthcare, emergency management, surveillance, intelligent transportation and many others. High-volume data streams can be efficiently analysed in real-time through the adoption of novel high-performance solutions targeting today's commodity parallel hardware. This comprises multicore-based platforms including mobile devices, heterogeneous systems equipped with GPU and FPGA co-processors, and large-scale distributed-memory systems like multi-Cloud and Fog computing environments. However, despite the large computing power offered by the affordable hardware available nowadays, high-performance data streaming solutions need to be equipped with smart logics in order to adapt the framework/application configuration to rapidly changing execution conditions and workloads. Moreover, the burst in the amount of data streams generated at the network edge by sensors and devices and the emergence of applications with predictable and low latency requirements require a shift from the traditional data stream processing performed in a central data center to a geo-distributed processing environment as represented by Fog computing and multi-Clouds. Such a new and challenging scenario demands for mechanisms and strategies for adapting the data stream computation to changes in the operating environment and workload and for dealing with uncertainty, fostering novel interdisciplinary approaches. The special issue aims at collecting high-quality scientific contributions from the research community working in the fields of data stream processing, data analytics algorithms, big data frameworks and autonomic resource management. The main focus is on parallel and autonomic models and practical implementations on parallel heterogeneous hardware and distributed systems.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on The convergence of the Internet of Things and Cloud for Smart Healthcare
Día de Entrega: 2017-11-15

With the development of smart sensorial media, things, and cloud technologies, "Smart healthcare" is getting remarkable consideration from the academia, the governments, the industry, and from the healthcare community. Recently, the Internet of Things (IoT) has brought the vision of a smarter world into reality with a massive amount of data and numerous services. Cloud computing fits well as an enabling technology in this scenario as it presents a flexible stack of computing, storage and software services at low cost. The cloud-based services can provide a high quality of experience to physicians, clinics, and other caregivers anytime and from anywhere seamlessly. However, the convergence of IoT and cloud can provide new opportunities for both technologies. The said IoT-cloud convergence can play a significant role in the smart healthcare by offering better insight of heterogeneous healthcare content (e.g., X-ray, ECG, MRI, ultrasound image, clinical notes, claims, and so on) to support affordable and quality patient care. It can also support powerful processing and storage facilities of huge IoT data streams (big data) beyond the capability of individual "things," as well as to provide automated decision making in real-time. While researchers have been making advances to the study of IoT and cloud services individually, a very little attention has been given to develop cost-effective and affordable smart healthcare service. The IoT-Cloud convergence for smart healthcare has the potential to revolutionize many aspects of our society; however, many technical challenges need to be addressed before this potential can be realized. Some of these challenges include: How to use the combined potential of IoT and cloud services or application for providing smart healthcare solutions? How these technologies can assist with right patient care at the right time and in the right place? How IoT-Cloud convergence along with healthcare big data analytics can facilitate healthcare data representation, storage, analysis and integration for effective smart healthcare solutions? This special issue is intended to report high-quality research on recent advances toward IoT-Cloud convergence for smart healthcare, more specifically to the state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of those convergence technologies for providing insights into smart healthcare service demands. Authors are solicited to submit complete unpublished papers in the following topics.
Última Actualización Por Dou Sun en 2017-05-10
Special Issue on Emerging Edge-of-Things Computing: Opportunities and Challenges
Día de Entrega: 2017-11-30

Recently, the Internet of Things (IoT) has emerged as a revolutionary technology that promises to offer a fully connected “smart” world. It enables billions of everyday objects such as consumer goods, enduring products, vehicles, utility components, sensors, and other physical devices to be connected with the global Internet that aims to transform the way we live, work, and play. However, a wide-scale realization of IoT is hindered due to the significant constraints of IoT devices in terms of memory, processing resources, energy, or communication bandwidth. The rise of Cloud-assisted Internet of Things or Cloud-of-Things (CoT) paradigm has been seen as an enabler to solve many of these issues as it offers networked and remote computing resources to process, manage, store and share huge volume of IoT data. It has stimulated the development of various innovative and novel applications in areas such as smart cities, smart homes, smart grids, smart agriculture, smart transportation, smart healthcare, etc. to improve all aspects of people’s life. However, currently the CoT paradigm is facing increasing difficulty to handle the Big data that IoT generates from these application use cases. As billions of previously unconnected devices are now generating more than two exabytes of data each day, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability and energy efficiency of IoT devices while moving all data to the Cloud. To cope with these challenges, a recent trend is to deploy anEdge Computinginfrastructure between IoT systems and Cloud computing. This new paradigm termed asEdge-of-Things(EoT) computing, allows data computing, storage and service supply to be moved from Cloud to the local Edge devices such as smart phones, smart gateways or routers and local PCs that can offer computing and storage capabilities on a smaller scale in real-time. EoT pushes data storage, computing and controls closer to the IoT data source(s); therefore, enables each Edge device to play its own role of determining what information should be stored or processed locally and what needs to be sent to the Cloud for further use. Thus, EoT complements CoT paradigm in terms of high scalability, low delay, location awareness, and allowing of using local client computing capabilities in real time. While researchers and practitioners have been making progress within the area of Edge-of Things computing, still there exists several issues that need to be addressed for its large-scale adoption. Some of these issues are: novel network architecture and middleware platform for EoT paradigm considering emerging technologies such as 5G wireless networks, software defined network and semantic computing; Edge analytics for IoT Big data; novel security and privacy methods for EoT; social intelligence into the Edge node to host IoT applications; and context-aware service management on the EoT with effective quality of service (QoS) support and other issues. This special issue targets a mixed audience of researchers, academics and investigators from different communities to share and exchange new ideas, approaches, theories and practice to resolve the challenging issues associated with the leveraging of Edge computing for Edge-of-Things paradigm. Therefore, the suggested topics of interest for this special issue include, but are not limited to: - Novel middleware architecture design for EoT paradigm - Semantic Edge computing for IoT - Edge analytics for Big data in IoT - Edge-enabled 5G network architecture and protocols for IoT - Software Defined Networking for EoT paradigm - Social intelligence in EoT system - EoT operating system design and validation - Interoperability and mobility for Edge to IoT connectivity - Trust, security and privacy issues in EoT system - Resource, service and context management on Edge computing for IoT applications - Software and simulation platform for EoT paradigm - Energy-aware resource scheduling in Edge computing for IoT applications - Emerging Edge commuting services and applications for IoT - Industrial Edge computing in IoT paradigm
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on Big Data for Context-Aware Applications and Intelligent Environments
Día de Entrega: 2017-12-15

This special issue addresses core topics on the design, the use and the evaluation of Big Data enabling technologies to build next-generation context-aware applications and computing systems for future intelligent environments. Disruptive paradigm shifts such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) will create a wealth of streaming context information. Large-scale context-awareness combining IoT and Big Data will drive to creation of smarter application ecosystems in diverse vertical domains, including smart health, finance, smart grids and cities, transportation, Industry 4.0, etc. However, effectively tapping into growing amounts of disparate contextual information streams remains a challenge, especially for large-scale application and service providers that need timely and relevant information to support adequate decision-making. A deeper understanding is necessary on the strengths and weaknesses of state-of-the-art big data processing and analytics systems (Hadoop, Spark, Storm, Samza, Flume, Kafka, Kudu, etc.) to realize large-scale context-awareness and build Big Context architectures. In particular, the key question is how one can help identify relevant context information, ascertain the quality of the context information, extract semantic meaning from heterogeneous distributed information sources, and do this data processing effectively for many concurrent context-aware applications with different requirements for adequate decision-making. At the same time, fundamental research is necessary to understand how context information about these large-scale distributed data processing infrastructures itself can offer the intelligence to self-adapt the configuration of these systems to optimize resource usage, such as the networking, data storage, and computation required to process context data. The particular focus of this special issue is on Big Context solutions covering the modeling, designing, implementation, assessment and systematic evaluation of large-scale context-aware applications and intelligent Big Data systems. We are soliciting high-quality, original research papers and encourage submissions that cover the broad range of research topics combining Big Data and context-aware applications or intelligent environments, including practical applications and case studies, application design methodologies, empirical evaluation of systems and metrics, underpinning theories, and more technical/scientific research topics. The possible topics include but are not limited to: - Big Data architectures for large-scale context-aware applications - Context models and query languages for heterogeneous data streams - Distributed context reasoning with Big Data technologies - Machine learning and prediction of situational awareness with Big Data - Effective data collection and processing for concurrent context-aware applications - Modeling of Quality of Service constraints and enforcing of Service Level Agreements - Context-aware dynamic decision making on streaming Big Data - Context-driven monitoring, adaptation and optimization of Big Data systems - Large-scale Quality of Context management - Systematic comparison of Big Data technologies for context-aware applications - Big Context solutions for finance, health, smart cities, industry 4.0, etc. - Security, privacy, scalability, and sustainability concerns Big Context systems
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on Edge of the Cloud
Día de Entrega: 2017-12-31

This special issue aims to draw together a number of papers which address challenges in the area of Edge or Fog computing. The inter-networking of physical devices, vehicles and buildings has led to the Internet of Things (IoT). But such cyber-physical systems need to respond in real-time and traditional Cloud computing does not support systems with such time dependency well. Time-sensitive applications are therefore moving from the centre to the edge of the cloud to avoid latency in communication. But when should a service sit on the edge of the cloud and when in the centre? How can applications be partitioned such that a time-critical part can be at the edge and time independent part in the Cloud? The decision might depend on the particular use at a particular time creating the desirability of migration and leading to a Fog of Things. Artificial intelligence or data-driven methods can be used to ascertain whether a service should sit on the edge or in the centre of the cloud and indeed whether, when and how migration should take place. Platforms and systems need to be developed to support such dynamics. This special issue calls for papers from researchers working making inroads in this fascinating area so that we can gather together a state-of-the-art account. Research or review papers are sought. The following themes are of particular interest: - Implementing time-critical applications in a Cloud environment - Models of implementation for cyber-physical systems - Conditions and systems that support dynamic migration of applications - Trade-off between performance and energy efficiency among cloud and fog computing in complex and large applications - New network systems or architectures for dynamic environments in fog and cloud computing - New economic models for the configuration of cloud and fog environments - New standards for heterogeneous communication and network - New task and storage allocation models for cloud and fog computing - Interesting application stories and lessons - Service models for composing or integrating heterogeneous resources in cloud and fog - Security models for fog computing and the Internet of Things
Última Actualización Por Dou Sun en 2017-09-16
Special Issue on Accountability and Privacy Issues in Blockchain and Cryptocurrency
Día de Entrega: 2017-12-31

Cryptocurrency is a digital version of currency that uses cryptographic technologies to guarantee the security of transactions. In recent years, cryptocurrencies such as Bitcoin and Ether are increasingly popular, including among cybercriminals (e.g. in ransomware cases). Blockchain underpins many of the existing cryptocurrencies, and plays an extremely important role due to its many desirable properties (e.g. transaction distribution and decentralized consensus). Applications of blockchain include identity management, smart contract, e-health, and many others are emerging. Currently, the daily cryptocurrency trading volume reportedly exceeds USD 1 billion. Similar to other technologies, ensuring privacy and accountability is important. For example, how to balance privacy, accountability, and security in blockchain and cryptocurrency is a challenging problem, and one that we focus on in this special issue. This special issue aims to publish state-of-the-art research advances in accountability, security and privacy topics relating to blockchain and cryptocurrency, with emphasis on the following aspects: - Accountability in Blockchain and Cryptocurrency - Anonymity in Blockchain and Cryptocurrency - Application of Blockchain - Auditing in Blockchain and Cryptocurrency - Blockchain Application - Bitcoin & Altcoins - Distributed Ledger Technology - Integrity in Blockchain and Cryptocurrency - Proof-of-Work - Proof-of-State - Privacy-Preserving Authentication - Smart Contract - Scalability of Blockchain and Cryptocurrency - Legal and Regulation Issues in Blockchain and Cryptocurrency
Última Actualización Por Dou Sun en 2017-09-23
Special Issue on Cyber Threat Intelligence and Analytics
Día de Entrega: 2017-12-31

In today’s Internet-connected world where technologies underpin almost every facet of our society, cyber security and forensics specialists are increasingly dealing with wide ranging cyber threats in almost real-time conditions. The capability to detect, analyze and defend against such threats in near real-time conditions is not possible without employment of threat intelligence, big data and machine learning techniques. For example, when a significant amount of data is collected from or generated by different security monitoring solutions, intelligent and next generation big-data analytical techniques are necessary to mine, interpret and extract knowledge of these unstructured/structured (big) data. Thus, this gives rise to cyber threat intelligence and analytics solutions, such as big data, artificial intelligence and machine learning, to perceive, reason, learn and act against cyber adversaries tactics, techniques and procedures. Cyber threat intelligence and analytics is among one of the fastest growing interdisciplinary fields of research bringing together researchers from different fields such as digital forensics, political and security studies, criminology, cyber security, big data analytics, machine learning, etc. to detect, contain and mitigate advanced persistent threats and fight against malicious cyber activities (e.g. organized cyber crimes and state-sponsored cyber threats). This special issue is focused on cutting-edge research from both academia and industry, with a particular emphasis on novel techniques, combination of tools and so forth to perceive, reason, learn and act on a wide range of cyber threat data collected from different intrusion attempts, malware campaigns and indications of compromise. Only technical papers describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or a journal will be considered. Extended work must have a significant number of "new and original" contributions along with more than 60% brand "new" material. Specifically, this issue welcomes two categories of papers: (1) invited articles from qualified experts; and (2) contributed papers from open call with list of addressed topics. Topics of interest include but not limited to: - Detection and analysis of advanced threat actors tactics, techniques and procedures - Analytics techniques for detection and analysis of cyber threats - Application of machine learning tools and techniques in cyber threat intelligence - Theories and models for detection and analysis of advanced persistent threats - Automated and smart tools for collection, preservation and analysis of digital evidences - Threat intelligence techniques for constructing, detecting, and reacting to advanced intrusion campaigns - Applying machines learning tools and techniques for malware analysis and fighting against cyber crimes - Intelligent forensics tools, techniques and procedures for cloud, mobile and data-centre forensics - Intelligent analysis of different types of data collected from different layers of network security solutions - Threat intelligence in cyber security domain utilising big data solutions such as Hadoop - Intelligent methods to manage, share, and receive logs and data relevant to variety of adversary groups - Interpretation of cyber threat and forensic data utilising intelligent data analysis techniques - Infer intelligence of existing cyber security data generated by different monitoring and defense solutions - Automated and intelligent methods for adversary profiling - Automated integration of analysed data within incident response and cyber forensics capabilities
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on Security and Privacy in Cyber Physical Systems
Día de Entrega: 2017-12-31

A cyber-physical system (CPS) is a complex blend of physical and computer components in which physical systems are usually monitored or controlled by computer-based algorithms with possibly humans in the loop. In cyber physical systems, physical and software components are deeply intertwined, each operating on different spatial and temporal scales, exhibiting multiple and distinct behavioral modalities, and interacting with each other in a myriad of ways that change with context. Examples of CPS include smart grid, autonomous transportation systems, medical monitoring, process control systems, robotics systems, and automatic pilot avionics. New smart CPSs will drive innovation and competition in sectors such as food and agriculture, energy, different modes of transportation including air and automobiles, building design and automation, healthcare and medical implants, and advanced manufacturing. Advances in CPS will enable capability, adaptability, scalability, and usability that will far exceed the simple embedded systems of today. CPSs are subject to security and privacy issues stemming from the increasing reliance on computer and communication technologies. The more complex a system gets, the more vulnerabilities it will have. Cybersecurity and privacy threats exploit the increased complexity and connectivity of critical infrastructure systems, placing the Nation’s security, economy, public safety, and health at risk. Historically, reliance on subtle assumptions at interface boundaries between hardware components, between hardware and software components, and between software components, as well as between a system and its operators and maintainers, has been a source of vulnerability and can be especially troublesome in these critical systems. The goal of this special issue is to foster novel, transformative and multidisciplinary approaches that ensure the security of current and emerging cyber-physical systems by taking into consideration the unique challenges present in this environment. This special issue also aims to foster a research community committed to advancing research and education at the confluence of cybersecurity, privacy, and cyber-physical systems, and to transitioning its findings into engineering practice. The proposed Special Issue of FGCS will promote research and reflect the most recent advances of security and privacy in cyber physical systems, with emphasis on the following aspects, but certainly not limited to: - Adaptive attack mitigation for CPS - Authentication and access control for CPS - Availability, recovery and auditing for CPS - Data security and privacy for CPS - Embedded systems security and privacy - EV charging system security - Intrusion detection for CPS - Key management in CPS - Legacy CPS system protection - Lightweight crypto and security - Security and privacy in industrial control systems - Smart grid security - Threat modeling for CPS - Urban transportation system security - Vulnerability analysis for CPS - Wireless sensor network security and privacy
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on Cloud and Fog Computing for Smart Cities Data Analytics and Visualisation
Día de Entrega: 2018-03-01

Information and Communication Technologies are becoming the prime enabler for smart and sustainable cities in recent years. This is mainly due to realising and making effective use of the ever-increasing data generated in urban environments. Sensors, smart phones, geo-tagged devices, RFIDs, smart gadgets and Internet of Things are major source of collecting ever increasing temporal and geo-coded land-use, built-environment, transport, energy, health, socio-economic and environmental Big Data. Often data is kept in different repositories and managed by different departments, which raise data access, harmonisation, processing and information visualisation challenges for generating new insights and knowledge. Cloud and Fog/Edge are becoming enablers for managing cross-departmental temporal and geocoded Big Data, developing cross-thematic applications and providing necessary computation power to perform data analytics and present new knowledge to city stakeholders for awareness raising, city planning, policy development and decision making. High-performance visual processing techniques provide opportunity to intuitively present temporal and geo-coded information from neighbourhood scale to city or city-region scale and fosters innovation, co-creation and co-designing sustainable future cities. In the above context, the real value of smart city big data is gained by applying data mining, machine learning or new statistical methods for data analytics, visualisation and decision making. This becomes challenging when applied to large scale or real time data and hence requires appropriate tools and techniques to be applied using Cloud and Fog/Edge computing. These applications also require dealing with privacy and data security issues to avoid sharing intrusive details of citizens or other stakeholders. Topics of interest include use of cloud and fog computing in smart cities but are not limited to: - Smart city data analytics - Geo-processing and innovative visualisation techniques - Spatial data techniques and tools for analytics - City data quality, harmonisation, integration and processing - Real-time city data processing and visualisation - Predictive analytics, visualisation and simulation for future city models - Visual computing and analytics for city applications - Interactive data analysis and visualisation - Smart city services and applications platforms - Security and privacy solutions for smart city applications - Crowd sourcing and establishing trust on data sources - Internet of Things for cross-thematic city applications - Methods and techniques for city data collection and curation - Automated and intelligent city data processing methods - Design patterns and computing models for smart city applications - Open government data for automated processing and knowledge generation - Decision support systems for smart cities - Data provenance techniques for city applications, decision making and policy making - Smart city applications: mobility, energy, public administration & governance, economy, health, security and environment.
Última Actualización Por Dou Sun en 2017-08-05
Special Issue on Computation Intelligence for Energy Internet
Día de Entrega: 2018-05-01

Energy crisis and carbon emission have become two global concerns. As a very promising solution, Energy Internet recently emerges to be able to tackle these challenges. Energy Internet is a radically new power generation and usage paradigm by exploiting the Internet principle to develop a revolutionary vision of smart grid. In Energy Internet, intelligent computation and communications are crucial in both operating and maintaining smart energy systems. In this sense, incorporating computation intelligence becomes the natural feature in all components as well as the whole energy system. Energy Internet applications using intelligent platforms typically need to be connected with users located remotely by using Internet of Things (IoT) and Cloud. This will transform energy system into intelligent designs and systems. New intelligent models, architectures, approaches, algorithms and solutions are needed to cope with the ever-increasing complexity of problems in energy systems, such as sensing intelligence, communications intelligence, machine learning, deep learning, and data mining. Specifically, real-time monitoring and controlling are faced with great challenges in order to collect precise energy management data. New machine learning and knowledge discovery methods are imperative to integrate, process and analyze sensing data from computation sensors for intelligent control and real-time decision making. Further, safeguards are needed to build trust in the data, which is instrumental for making critical decisions for the development of Energy Internet. This special issue will be dedicated to research and advances on the issues in Computation Intelligence for Energy Internet. Original papers describing new and previously unpublished works will be selected addressing all aspects of computation intelligence in Energy Internet. The papers will go through the usual review process, and then further reviewed by the editorial team to ensure quality of publication. The topics of interest in this special issue include, but are not limited to: - Energy Internet system and architecture with computation intelligence - Computation intelligence for Energy Internet applications, e.g., power grid, Vehicle-to-Grid, PV systems, wind farm, buildings, and energy storage - Design and analysis of real-time systems in Energy Internet - Intelligent M2M communications in Energy Internet - Cloud/Fog based intelligence for Energy Internet - Computation security and privacy in Energy Internet - Intelligent algorithms and optimization for Energy Internet - Machine Learning and deep learning for Energy Internet - Big data analysis intelligence for Energy Internet - Data mining and knowledge discovery for Energy Internet - Innovative forecasting methods in Energy Internet
Última Actualización Por Dou Sun en 2017-08-05
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