【今日新增】高引SCI期刊FGCS专刊截稿信息11条

全文截稿: 2017-06-15

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

To make the world smart in service to humanity is the ultimate rank of ICT and IoT is at the forefront in its latest extensions. Smart traffic, smart logistics and transportation, smart meter, smart grid, smart appliance, smart home, smart watch, etc. are encapsulated in the word 'smart city' that is now on board. Singapore, Barcelona, London, San Francisco, Nice, and Oslo, the names at top, are giving its real demonstration. But the dream of smart global village is far beyond it. Among its most indispensable components, socialization between objects in worldwide is the minimum requirement, where the smart objects (micro, macro) turn to social objects to boost the pace of IoT emergence and to make it more universal. The relationships of co-location, co-ownership, co-work and parental among friend objects provide a platform to share services, information, computing, and other resources and output. This modern promising paradigm of technology extension is called Social Internet of Things (SIoT). An inevitable aspect of SIoT is the convergence of smart objects and social media that can introduce new social interactions by enabling the things to have their own social networks and interactions. The smart objects can establish their social relationship based on their activities, interest and profile.

In addition to inherited challenges from its ancestors; IoT and social networksocial media, IoT has its own long list of challenges from the perspectives of architectural design, services, management, interoperability, implementation, operation and maintenance, scalability, navigability, application development, socio-technical networking, privacy, trustworthiness, and security, fault tolerance, interaction and interfaces, just to name a few. Though SIoT is at its infancy, yet its constituents are now well matured and various efforts in presenting the solutions from conventional and non-conventional solutions are seen in literature in support of offering the best out of those technologies. Apart from their enhancements, intelligent techniques (such as swarm intelligence, neural networks, artificial intelligence, fuzzy logic, and genetic algorithms, deep learning, machine learning) can also be incorporated in designing the smart solutions. Whatsoever the modeldesignarchitecturesolution would be, but it is appreciable that it intends to transcend today's available technologies and in so doing can identify technology gaps based on varied requirements.

全文截稿: 2017-06-15

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

"Future Generation Computer Systems", a forum for the publication of peer-reviewed, high-quality original papers in the computer systems sciences, focusing specifically advances in distributed systems, collaborative environments, high performance and high performance computing, Big Data on such infrastructures as grids, clouds and the Internet of Things (IoT), is seeking original manuscripts for a Special Issue on mobile, hybrid, and heterogeneous clouds for cyberinfrastructures scheduled to appear in the second half of 2017.

With the increasing availability of mobile devices and data generated by people, scientific instruments and simulations, today, solving many of our most important scientific and engineering problems requires powerful solutions providing the whole chain to process data and services from the mobile users to the cloud infrastructure, which must also integrate heterogeneous clouds to provide availability, scalability, and data privacy. These solutions are more and more important with the increasing synergies between cloud computing and data intensive applications, which require cyberinfrastructures that must be powerful in a broad sense (computation, storage, I/O capacity, communications, ...) to satisfy the services and data processing requirements from millions of users, but at the same time have to provide strong connectivity and adaptivity utilities to cope with near future mobile applications.

The special issue will provide a forum for presenting research works showing advances on mobile, hybrid, and heterogeneous clouds for cyberinfrastructures, including new platforms, system software enhancements, algorithm design and optimization, programming paradigms and techniques, data processing support in homogeneous and heterogeneous computing systems, tools and environment for MHCC data and computing systems, runtime support for MHCC and performance simulations, measurement, and evaluations. The special issue will also be open to any author, but it will also invite extended versions of the selected papers of CCGrid 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.

This special issue of Future Generation Computing Systems will feature articles that discuss the following areas of interest:
- Mobile cloud platforms.
- Integration solutions for mobile, hybrid, and heterogeneous clouds cyberinfrastructures.
- Management of massive data using mobile and heterogeneous clouds.
- Resource management and scheduling in mobile, hybrid, and heterogeneous clouds.
- Tools/environments for mobile, hybrid, and heterogeneous clouds.
- New programming models as well as machine and application abstractions.
- Resilience issues in mobile and hybrid clouds.
- Adaptive software for cloud computing and data systems
- Big Data applications and mobile clouds.
- Data chain integration in mobile, hybrid, and heterogeneous clouds.
- Collaborative infrastructures and virtual organizations using mobile clouds.
- Protocols and emerging standards for mobile, hybrid, and heterogeneous clouds.
- High-end scientific and engineering computing.
- Novel applications of mobile, hybrid, and heterogeneous clouds cyberinfrastructures.

全文截稿: 2017-06-30

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Internet of Things (IoT) connects durable goods, cars and trucks, industrial and utility components, and sensors to Internet with data analytics capabilities. IoT is flourishing due to technology advancements. The key features of IoT Operating Systems (OSs) are modularity, energy-efficient scheduler, hardware support, architecture, network stacks, reliability, interoperability, unified APIs, generic interfaces, and real-time capabilities. The applications for IoT service scenarios are diverse and challenging. These range from smart energy, transportation, etc. to big data analysis. The integration of all these applications is essential to eventually make everything smart. The memory and energy efficient IoT protocols are desirable. The validation of IoT protocols and applications is a key to success. Therefore, an IoT OS requires to support not only a huge variety of  heterogeneous hardware, but also simulators and emulators as well as testbed facilities Further, it should provide the capability to perform small scale to large scale testing with heterogeneous physical devices and communication technologies. The availability of variety of IoT OSs, low-cost IoT devices, heterogeneous telecommunications technologies, big data technologies and standardization is a key of success for IoT deployment. To fully exploit these technological advancements, there exists many issues related to applications, protocols, testing, interoperability; time bounded big data processing and analysis, heterogeneous communication technologies and platform support.

This Special Issue focuses on the most recent advancement in interdisciplinary research areas encompassing IoT OSs, applications and protocols design, development, and validation domain. This Special Issue will bring together researchers from diverse fields such as communication engineering, computer engineering, computer science, electrical and electronics engineering, bio-informatics and mathematics. Through this Special Issue, we invite researchers from industry, academia and government organizations to discuss innovative ideas and contributions, demonstrate results and share standardization efforts on the IoT OSs and related areas.

全文截稿: 2017-07-31

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

High-performance computing has been an important and fundamental research topic over the past decade and has posed many challenging problems. Researchers and industrial professionals have been devoted to designing innovative tools and techniques to keep up with the rapid evolution and increasing complexity of large and complex scientific and engineering problems. Recent years have witnessed a deluge of network data propelled by the vehicular communications, mobile sensing, IoT, M2M communications, emerging online social media, user-generated video contents, and global-scale communications, bringing people into the era of big data. These network data hold much valuable information that could significantly improve the effective and intelligent optimisation of Internet, vehicular networking, mobile networking, and IoT. Big Data processing requires a vast amount of storage and computing resources. In addition, online and robust processing is needed for some circumstances, e.g., life-or-death situations. The high-performance computing techniques have been widely agreed as a promising paradigm to facilitate big data processing, but with tremendous research challenges in recent years, such as the scalability of computing performance for high velocity, high variety, and high volume big data, Deep learning with massive-scale datasets, MapReduce on multi-core, GPU, and hybrid distributed environments, and unstructured data processing with high-performance computing.

This special issue is devoted to the most recent developments and research outcomes addressing the related theoretical and practical aspects on high-performance computing techniques for big data processing, and aims to provide worldwide researchers and practitioners an ideal platform to innovate new solutions targeting at the corresponding key challenges.

全文截稿: 2017-07-31

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Internet of Things is a field that has great prospects for the future and is becoming very popular. Thousands of researchers around the world are currently working in systems based on the Internet of Things. The core of many IoT systems is based in a network of embedded devices (or a network of smart things or connected sensors, etc.).  Based on the communication and collaboration among embedded devices these IoT networks are able to automatize or improve a lot of tasks and processes. These systems are already being applied in a lot of areas like smart cities, health systems, smart homes, etc.

The interconnection and collaboration processes of embedded devices are complex issues, which can vary greatly in every system or environment. For example, it can depend on the physical environment issues, services provided,  specific system requirements (like efficiency, privacy, etc.), amount of devices in the network, devices heterogeneity, network properties, communication technologies, etc. Due to these particularities and different situations, the use of networks of embedded devices presents many different challenges and issues which can be improved: efficiency in communication and collaboration, security and privacy issues, quality of services and problems predictions, network dynamic adaptations, network protocols and architectures, specific middlewares, frameworks and distributed applications for coordinating connected devices, etc.

The main aim of this special issue is to compile original and high quality research works related to innovative solutions in the field of "communications, collaborations and distributed services in networks of embedded devices (Internet of Things)".

全文截稿: 2017-09-29

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

"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.

全文截稿: 2017-09-30

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

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.

全文截稿: 2017-09-30

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

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.

全文截稿: 2017-10-15

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

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.

全文截稿: 2017-11-03

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

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.

全文截稿: 2017-11-15

影响因子: 2.43

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

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. 

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