Future Generation Computer Systemshttp://www.journals.elsevier.com/future-generation-computer-systems/
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:  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  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  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 2017-02-05
Special Issue on Spatiotemporal Big Data Challenges, Approaches, and SolutionsSubmission Date: 2017-06-011. Theme and topics Today, the growing number of distributed sensors and tracking systems are generating overwhelming amounts of high velocity spatio-temporal data. Executing high performance queries on enormous volumes of spatial data, has become a necessity for numerous domains ranging from atmospheric, climate and ocean simulations to signal processing, traffic, and behaviour modelling. As the dimensions and volume of the data grows to massive scales, processing and storage with conventional methods is challenged. Most interestingly though, even most state of the art “Big Data” processing tools fall short in supporting spatiotemporal data needs efficiently, as they lack support for even basic spatial properties and methods (such as spatial indexing and joins). Combining these challenges with real time requirements (such as sub-second query response times required for collision avoidance and anomaly detection) only exacerbates the problem. To support such applications, the research community has long been exploring methods of data reduction, compression, time-window approaches, parallel processing, distributed storing and many more, while often accepting accuracy and performance trade-offs. This special issue aims to highlight problems originating from real world application fields dealing with spatiotemporal Big Data challenges and invite researchers working towards novel methods for addressing these issues to submit their work. The aim of this special issue publication is to cover novel data science theory and algorithms, data engineering and real world systems architectures, which are aimed at the storage, fusion, processing, learning and ultimately knowledge extraction from real world spatio-temporal datasets. This SI publication is aimed at researchers, scientists and practitioners with interests that lie at the intersection of data science and large-scale data management problems. The issue will focus on technologies and solutions related (but not limited) to: Spatiotemporal compression and clustering techniques effective for big data processing Spatial data mining algorithms and solutions Large-scale parallel and distributed implementations for geospatial datasets Real-time processing and learning based on spatio-temporal features Knowledge discovery implementations from spatiotemporal real world datasets; Visual and data analytics, knowledge representation of big geospatial data Cloud enabled Big data architectures and real world applications;
Special Issue on Future Networking Research Plethora for Smart CitiesSubmission Date: 2017-06-01In recent years, many interest groups have focused on promoting various novel and emerging network paradigms for Smart City planning using IoT-enabled embedded devices and the application of Big Data. The existing Internet architecture was designed with the utmost goal of enabling end-to-end host centric communication that has drawn the attention of both academic and industrial experts to develop new network models for exchanging data between various type of technologies such Bluetooth, ZigBee, etc. Nowadays, “Softwarization” has become an ongoing crucial transformational force in communications technology industry, despite whether its roots are on mobile networks, content delivery, home connectivity, wireless, enterprise, IoT, data centers, cloud computing, and backbone networks. The IoT is progressively using by various firms and industries for the planning and development of future Smart City. However, without utilizing the previous context of the cities, it is quite difficult to design and plan a future Smart City. Therefore, the data generated by various IoT-enabled devices can be efficiently processed through various techniques and tools such as Hadoop ecosystem, etc. to plan a smart city. However, the existing techniques based on Map-Reduce paradigm, etc. are mainly designed to process offline data. Moreover, the existing technologies such as Software Defined Network (SDN), etc. can be made more intelligent and efficient to communicate the huge amount of data over the existing network with high speed. The theme of this special issue is to provide an in-depth analysis both theoretically and analytically of the current advances in processing real-time data for optimal planning and management of a smart city. Moreover, the authors are expected to investigate state-of-art research challenges, results, architecture, application, and other achievements in the field of Big Data analytics used for smart city planning. High quality innovative unpublished papers, which are not currently under consideration in other journals, are solicited. Topics of interest include, but are not limited to: Smart City architectures and planning using WSN The role of IoT in the planning of future Smart Cities Smart management and services Deployment of sensors Smart homes and its applications The role of cloud computing in IoT-based Smart Cities Big Data Analytics and Smart Cities IoT-enabled devices and technologies Real-Time data processing using Hadoop, SPARK, GraphLab, etc. System, design, modeling and evaluation Industrial applications of Smart Cities Future Internet cohesion with applications for Smart Cities
Special Issue on Scheduling Algorithms for Cyber-Physical-Social WorkflowsSubmission Date: 2017-06-01Cyber-Physical-Social Distributed Systems (CPS-DS) are aimed at monitoring and controlling the behavior of the physical world (e.g., rivers, roads, energy grids, homes, factories, shopping malls, etc.) using a vast interlinked network device in the cyber world such as sensors, gateways, switches, routers, computing resources, applications/services and also humans to link the cyber world with the physical and humans-social world. CPS-DS drives the vision of a smart interconnected cyber-physical-social world where the physical world is monitored in real time, and the services in the cyber world uses the data to directly influence decision making in the physical world. With the new challenges imposed by CPS-DS workflows and a rapidly growing cyber (50 billion devices connected to the Internet by 2020) and social (e.g., 1.6 Billion Facebook, 1 Billion WhatsApp, and 320 Million Twitter users in 2015) worlds, current assumptions that all the storage and processing capacity necessary for workflow processing should reside predominantly in remote datacenters is being challenged. Hence, the traditional scheduling model for provisioning enterprise and scientific computing workflows, needs to emerge or evolve into a more distributed and decentralized CPS-DS scheduling model that can cater for new data sources and include the computing and storage power of new types of programmable cyber devices available at the network edge, such as smart gateways, network function virtualization solutions, handheld devices (smart phones and tablets etc.), and smart sensors (e.g., cameras and energy meters). These devices at the network edge can offer small-scale computing and storage capabilities for tackling the new real-time data processing challenges imposed by CPS-DS workflows. The workflows such as FDM, which are highly latency-sensitive, will significantly benefit from analysis of sensor and human data on the Edge as it can: (i) save energy for battery-operated edge devices by reducing the burden of continuously uploading data to the remote datacenters and (ii) save unnecessary network bandwidth consumption (iii) reduce the latency in reacting to events.
Special Issue on Enabling Technologies for Social Internet of ThingsSubmission Date: 2017-06-15To 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.
Special Issue on Mobile, hybrid, and heterogeneous clouds for cyberinfrastructures (MHCC2017)Submission Date: 2017-06-15"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.
Special Issue on Big Data Analytics for SustainabilitySubmission Date: 2017-06-30Sustainability is a paradigm for thinking about the future in which environmental, societal and economic considerations are equitable in the pursuit of an improved lifestyle. Most of the economies are developing with breakneck velocities and are becoming epicenters of unsustainable global growth. Immense utilization of natural resources, waste generation and ecological irresponsibility are the reasons for such a dire situation. Big data analytics is clearly on a penetrative path across all arenas that rely on technology. At present scientific area of chemical process engineering and natural hazards management is recognized as a method to integrate an efficient sustainability analysis and strategy. Those two engineering domains provide handful solution to manage systems by enabling the use of modeling, simulation, optimization, planning and control in order to develop a more sustainable product and process. In this context scientific simulation based on big data and collaborative work has to be developed for succeeding Computer-Aided Design/Engineering (CAD/E) of sustainable system. In scientific simulation based High Performance Computing (HPC) area, pre and post-processing technologies are the keys to make the investments valuable. This special issue calls for high quality, up-to-date technology related to big data analytics for Sustainability and serves as a forum for researchers all over the world to discuss their works and recent advances in this field. A few best papers from IoTBDS 2017 and COMPLEXIS 2017 will be invited. In particular, the special issue is going to showcase the most recent achievements and developments in big data discovery and exploration. Both theoretical studies and state-of-the-art practical applications are welcome for submission. All submitted papers will be peer-reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. The list of possible topics includes, but not limited to: Geographical Big Data Analysis Geography Big Data Mining and Exploration Big Data for Smart Cities and Smart Homes Large-scale Sustainable infrastructure and smart buildings Large-scale Human Activities Data Computing Sustainability Analysis of Energy Distributions Internet of Things (IoT) services and applications Internet of Vehicles (IoV) technologies Passenger Sensing, Control and Management Data-Driven Urban Management Environment-Aware Application, analytics and visualization Environment Big Data Processing and Analysis Big Data Information Security for Sustainability Knowledge-based systems, computing and visualization for Sustainability Computational intelligence and algorithms for Sustainability Cloud Computing Platform Based Big Data Mining Energy-Consumption-Aware Ubiquitous Computing Complex information systems for Sustainability Environmental sensor networks, monitoring, environmental and weather studies Energy efficient communication protocol for networks Energy-efficient metrics and modeling for communication networks Network traffic model and characteristics for information-centric networking Future Generation Green ICT 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 http://ees.elsevier.com/fgcs/. Authors should select “SI: BD Analytics Sust” when they reach the “Article Type” step in the submission process. Tentative schedule Submission deadline: June 30, 2017 Pre-screening notification: July 16, 2017 First round notification: September 15, 2017 Revision due: October 30, 2017 Final notification: November 30, 2017 Final Manuscript due: December 30, 2017 Tentative publication date: Spring 2018 Guest editors Dr. Zhihan Lu (Lead guest editor) University College London, UK. Email: firstname.lastname@example.org, email@example.com Google Scholar: https://scholar.google.co.uk/citations?user=Sq_ovbQAAAAJ&hl=en&oi=ao (If you make an enquiry, please state FGCS SI: Big Data Analytics for Sustainability‘ in your email’s subject) Dr. Rahat Iqbal Coventry University, UK Email: firstname.lastname@example.org Google Scholar: https://scholar.google.co.uk/citations?user=ji81dz8AAAAJ&hl=en Dr. Victor Chang Xi'an Jiaotong Liverpool University, China Email: email@example.com Google Scholar: https://scholar.google.co.uk/citations?user=IqIYZ14AAAAJ&hl=en
Last updated by Dou Sun in 2016-07-23
Special Issue on Internet of Things (IoT): Operating System, Applications and Protocols Design, and Validation TechniquesSubmission Date: 2017-06-30Internet 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.
Special Issue on Emerging Trends, Issues and Challenges in Internet of Things, Big Data and Cloud ComputingSubmission Date: 2017-06-30Cloud computing has emerged as an important computing paradigm, enabling ubiquitous convenient on-demand access through Internet to shared pool of configurable computing resources. In this paradigm, software (applications, databases, or other data), infrastructure and computing platforms are widely used as services for data storage, management and processing. They provide a number of benefits, including reduced IT costs, flexibility, as well as space and time complexity. To benefit, however, from numerous promises cloud computing offers, many issues have to be resolved, including architectural solutions, performance optimization, resource virtualization, providing reliability and security, ensuring privacy, etc. Another significant technology trend that nowadays is gaining increasing attention is Internet of Things (IoT). In IoT, intelligent and self configuring embedded devices and sensors are interconnected in a dynamic and global network infrastructure, enabling scalability, flexibility, agility and ubiquity in fields of massive scale multimedia data processing, storage, access and communications. IoT is driving new interest in Big Data, by generation of enormous amount of new types of data being generated by sensors and other input devices, which have to be stored, processed and accessed. The need to monitor, analyse and act upon these data brings many issues like data confidentiality, data verification, authorization, data mining, secure communication and computation. The future development of cloud computing systems is more and more influenced by Big Data and IoT. There are research and industrial works showing applications, services, experiments and simulations in the Cloud that support the cases related to IoT, Big Data and Security. Cloud users and cloud service providers face a variety of new challenges like encrypted data search, share, auditing, key management security and privacy. There is also a need for protocols that facilitate big data streaming from IoT to the cloud and QoS.
Special Issue on Bioinspired Algorithms in Complex Ephemeral EnvironmentsSubmission Date: 2017-07-15 Overview The concept of ephemeral computing is still under discussion and no standard definition has reach a consensus among the research community. The basic ephemeral properties can be stated as those with a transitory nature that may affect the functioning of distributed versions of computer algorithms. Although the capacity and computer power of small and medium devices (as smartphones or tablets) are increasing swiftly, their computing capacities are usually underexploited. The availability of highly-volatile heterogeneous computer resources capable of running software agents requires an appropriate design and implementation of algorithms. This will allow to make a proper use of the available resources while circumventing the potential problems that may produce such non-reliable systems. Among the desired features for the algorithms under consideration -that will potentially be run on non-dedicated local computers, remote devices, cloud systems, ubiquitous systems, etc.- we look for ephemerality-awareness, which is related to self-capability for understanding the underlying systems where the algorithm is run as well as taking decisions on how to proceed taking into account the non-reliable nature of the system. Algorithms consciously running on this kind of environment require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavour, thanks to some of the features they inherit from their biological sources of inspiration, namely decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. Therefore, this special issue will be focused on: the deployment of bioinspired algorithms such as evolutionary algorithms or swarm intelligence methods (and in general complex metaheuristics and evolutionary multi-agent systems) on computational environments featuring ephemeral-like properties (such as unreliability, dynamicity, and/or heterogeneity, just to mention a few) and the use of bioinspired algorithms to model or analyze systems with the aforementioned properties, including but not limited to social network dynamics, ephemeral clustering and pattern mining, ephemeral computational creativity and content generation, and in general any new and innovative domains with ephemeral-like features. Topics appropriate for this special Issue include, but are not necessarily limited to: Computational creativity Content generation, behaviour and data analysis in video games Social Network analysis Ephemeral pattern mining Ephemeral clustering Evolutionary ephemeral-based algorithms to new and innovative domains Swarm ephemeral-based algorithms to new and innovative domains Online and streaming data analysis Human behavioural modeling in ephemeral environments
Special Issue on High-Performance Computing for Big Data ProcessingSubmission Date: 2017-07-31High-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.
Special Issue on Internet of things: Communications, collaborations and services in networks of embedded devicesSubmission Date: 2017-07-31Internet 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)".
Special Issue on Towards Smarter Cities: Learning from Internet of Multimedia Things-Generated Big DataSubmission Date: 2017-09-01Smart city's IoT-based infrastructures envision improvement in quality of life through optimal utilization of resources. Integrating diverse sensors through communication technologies generate big data which is collected, processed, and analyzed, revealing knowledge and information to realize the goals of smart cities. Multimedia sensors serve as the eyes and ears of smart city administrators, enabling them to monitor activities and assets. The big multimedia data generated by these sensors contain a wealth of information, needed to be processed and analyzed for knowledge extraction. However, the huge volume of this data and its inherent complexity hinders ability of traditional computing infrastructures and algorithms to effectively process and extract actionable intelligence from it. There is a growing demand for efficient yet powerful algorithms to consume internet of multimedia things (IoMT)-generated big data and extract needed information from it to run the affairs of smart cities. Deep learning based methods for multimedia data processing and understanding has shown great promise in the recent years. This special issue aims to highlight problems and future challenges in smart cities and invite researchers working towards smart cities and associated technologies like IoMTs, machine learning for big data, and embedded/cloud computing, to develop novel methods for addressing issues related to the transmission, processing, representation, and storage of IoMT-generated big data. It also invites novel deep learning based solutions for real-time data processing, learning from multi-modal big data, distributed learning paradigms with embedded processing, and efficient inference.
Special Issue on Security, Trust and Privacy in Cyber (STPCyber): Future Trends and ChallengesSubmission Date: 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.
Special Issue on Big Data and Internet of Things – Fusion for different services and its impactsSubmission Date: 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
Special Issue on Recent Advances in Big Data Analytics, Internet of Things and Machine LearningSubmission Date: 2017-09-30Big 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.
Special Issue on Internet of KnowledgeSubmission Date: 2017-09-30Information 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.
Special Issue on Benchmarking Big Data SystemsSubmission Date: 2017-10-15There 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.
Special Issue on Security and Privacy for RFID and IoTsSubmission Date: 2017-10-15AIMS & 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.
Special Issue on New Landscapes of the Data Stream Processing in the era of Fog ComputingSubmission Date: 2017-11-03Nowadays, 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.
Special Issue on The convergence of the Internet of Things and Cloud for Smart HealthcareSubmission Date: 2017-11-15With 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.
|CCF||Full Name||Impact Factor||Publisher||ISSN|
|Multidimensional Systems and Signal Processing||0.857||Springer||0923-6082|
|International Journal of Managing Public Sector Information and Communication Technologies||AIRCC||2230-7958|
|a||ACM Transactions on Computer Systems||ACM||0734-2071|
|b||European Journal of Information Systems||2.892||The OR Society||0960-085X|
|c||The Journal of Strategic Information Systems||2.595||ELSEVIER||0963-8687|
|b||International Journal of Human-Computer Interaction||1.26||Taylor & Francis||1044-7318|
|Photonic Network Communications||0.448||Springer||1387-974X|
|ACM Transactions on Parallel Computing||ACM||2329-4949|
|b||ACM Transactions on Embedded Computing Systems||ACM||1539-9087|
|Engineering with Computers||1.054||Springer||0177-0667|
|Full Name||Impact Factor||Publisher|
|Multidimensional Systems and Signal Processing||0.857||Springer|
|International Journal of Managing Public Sector Information and Communication Technologies||AIRCC|
|ACM Transactions on Computer Systems||ACM|
|European Journal of Information Systems||2.892||The OR Society|
|The Journal of Strategic Information Systems||2.595||ELSEVIER|
|International Journal of Human-Computer Interaction||1.26||Taylor & Francis|
|Photonic Network Communications||0.448||Springer|
|ACM Transactions on Parallel Computing||ACM|
|ACM Transactions on Embedded Computing Systems||ACM|
|Engineering with Computers||1.054||Springer|
|PACT||International Conference on Parallel Architectures and Compilation Techniques||2017-03-14||2017-09-09|
|ECBS||European Conference on the Engineering of Computer Based Systems||2017-06-01||2017-08-31|
|ACN||International Conference on Advanced Communication and Networking||2015-05-15||2015-07-08|
|ScilabTEC||International Scilab Users Conference||2015-01-02||2015-05-21|
|Mobisys||International Conference on Mobile Systems, Applications and Services||2016-12-01||2017-06-19|
|ICGCTI||International Conference on Green Computing, Technology and Innovation||2016-08-18||2016-09-06|
|COMNETSAT||IEEE Communication Network and Satellite Conference||2016-08-08||2016-12-08|
|ICECCS||International Conference on Engineering of Complex Computer Systems||2015-06-21||2014-08-04|
|RAM||International Conference on Robotics, Automation and Mechatronics||2015-01-31||2015-07-15|
|COLLABORATECOM||International Conference on Collaborative Computing: Networking, Applications and Worksharing||2017-07-15||2017-11-11|