Journal Information
Future Generation Computer Systems
Impact Factor:

Call For Papers
The Grid is a rapidly developing computing structure that allows components of our information technology infrastructure, computational capabilities, databases, sensors, and people to be shared flexibly as true collaborative tools. Over the last 3 years there has been a real explosion of new theory and technological progress supporting a better understanding of these wide-area, fully distributed computing systems. After the advances made in distributed system design, collaborative environments, high performance computing and high throughput computing, the Grid is the logical next step.

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

[1] Grid Applications and application support:

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

[2] Grid methods and middleware:

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

[3] Grid Theory:

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

Last updated by Dou Sun in 2017-02-05
Special Issues
Special Issue on Intelligent Sensing and Applications for Cyber-Physical Systems
Submission Date: 2017-05-01

The cyber-physical system (CPS) has been coming into our view and will be applied in our daily life and business process management. The emerging CPS must be robust and responsive for its implementation in coordinated, distributed, and connected ways. It is expected that future CPS will far exceed today's systems on a variety of characteristics, for example, capability, adaptability, resiliency, safety, security, and usability. With the rapid development of computing and sensing technologies, such as ubiquitous wireless sensor networks, the amount of data from dissimilar sensors and social media has increased tremendously. Conventional data fusion algorithms such as registration, association, and fusion are not effective for massive datasets. New research opportunities and challenges for content analysis on CPS networks have arisen. Making sense of these volumes of Big Data requires cutting-edge tools that can analyze and extract useful knowledge from vast and diverse data streams. How to integrate and analyze the data: How to retrieve knowledge from Big Data, How to share knowledge among smart things, How to ensure security and protect privacy? These are some of the questions in the long list of challenges that are needed to be addressed in future CPS. Current research in Intelligent Sensing addresses the following issues: Intelligent Sensing as a novel methodology for user-centered research; development of new services and applications based on human sensing, computation, and problem solving; engineering of improved Intelligent Sensing platforms including quality control mechanisms; incentive design of work; usage of Participatory Sensing for professional business; and theoretical frameworks for evaluation. This is opening a vast space of opportunities to extend the current networks, communications, and computer applications to more pervasive and mobile applications.
Last updated by Dou Sun in 2016-11-08
Special Issue on Security and Privacy in Big Data-enabled Smart Cities: Opportunities and Challenges
Submission Date: 2017-05-01

The fast pace of Information and Communication Technologies (ICTs) is inextricably linked with the urban development. In last decades, smart cities have become a hope for many of decision makers and people as well to overcome the cumulated urban problems. Smart cities are developed environments where any citizen can use any service anywhere and anytime. Internet of Things (IoT) has become a generator of smart cities aiming at overcoming the problems inherent in urban developments. The wide facilities offered by IoT and other sensing facilities, have led to a huge amount of data generated from versatile domains in smart cities. In turn, big data analytics have emerged as a need to process all data collected from all the sources in the city. Big data analytics (BDA) seeks to gain insights born from the data and often involves disruptive innovations. With implications how research is conducted; instead of inductively creating theories from collected data or deductively confirming theories with collected data to satisfy our thirst for knowledge and prediction, we now process DBA procedure in order to generate knowledge. The core of the shift pertains to the scientific method employed in BDA. From an epistemological perspective, BDA differs significantly from the human cognitive apparatus in modeling phenomena, particularly in terms of storage capacity, computational power, and analytical techniques. Due to the fact that big data and smart city go hand-in-hand, information security has become inevitable requirement not only for personal safety, but also for assuring the sustainability of the city. This special issue aims to ensemble the concepts of smart city, big data, and information security all together for addressing several cross-disciplinary challenges. Moreover, it will provide an up-to-date archive for the documented progress in big data opportunities and challenges in smart city with a special focus on the emerged security and privacy aspects.
Last updated by Dou Sun in 2016-11-08
Special Issue on Fusion of Cognitive Neural Computing Paradigms for Prevailing User Behaviour in Online Social Networks
Submission Date: 2017-05-01

Scope and Objectives: Cognitive computing provides a promising solution to the industry that encompasses Artificial Intelligence, machine learning, reasoning, natural language processing, speech and vision, and human-computer interaction it will be help to improve human decision-making. The new era and fusion of cognitive neural network paradigms with reference to Online Social Networks (OSN) has three main components: (a) adapts and learns from user preferences and responses, (b) builds and evaluates evidence based hypothesis, and (c) Understands natural language and human interactions. This special issue is integrating cognitive neural computing paradigms, advanced data analytics and optimization opportunities to bring more compute to the user preferences in OSN. As we know, the exploration of social media, categorizing the user behaviour and representing logical decisions is contrasting to other quantitative analysis methodologies. Similarly, with social media outreach, the prevailing user behaviour or engagement has become crucial for the impact analysis of OSN. Further, it is importance to make a note that cognitive neural computing and its intelligence techniques has not been adequately investigated from the perspective of OSN user behaviour and its related research issues. Furthermore, there are many noteworthy issues (opinion mining, link prediction, recommender system, community detection etc) that need to be addressed in the context of cognitive neural computing and user behaviour for the OSN. Obviously, these challenges also create immense opportunities for researchers. For the aforementioned reasons, this special issue focuses to address comprehensive nature of cognitive neural computing and to emphasize its character in smart thinking and learning systems, complex analysis tasks mimic human cognition and learning behaviour, prediction and control of future OSN systems. This special issue intends to give an overview of state-of-the-art of issues and solution guidelines in the new era of cognitive neural computing and its recent trends of techniques for OSN. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological/application perspective in understanding the fusion of cognitive neural computing paradigms and their capabilities in solving a diverse range of problems in OSN and its real-world applications. Topics of Interest: Note that this special issue emphasizes "real world" applications. Topics include, but are not limited to the following: Emerging approaches for cognitive knowledge acquisition Machine learning techniques (e.g., Deep Learning) with cognitive knowledge acquisition frameworks for OSN Natural Language Processing with human cognition and learning behaviour in social media outreach. Sentiment analysis and opinion mining in OSN Fuzzy cognitive systems for OSN Cognitive techniques for persuasion and recommender systems for OSN Cognitive neural computing techniques for information retrieval Representing, formalizing and reasoning with cognitive knowledge Deployment of cognitive systems: Social Media, News, and Data Streams Cognitive neural computing for dynamic spectrum access Data analytics platform for detailed reporting, assessment, and collaboration for OSN Prototypes for cognitive network devices for OSN Complexity and scalability of cognitive systems Cognitive prediction and decision making systems for OSN Paper Solicitation Papers must be tailored to the emerging fields of cognitive neural computing paradigms for OSN and explicitly consider the recent deployments models, challenges, and novel solutions. The guest editors maintain the right to reject papers they deem to be out of scope of this special issue. Only originally unpublished contributions and invited articles will be considered for the issue. Each paper will go through a rigorous peer-review process by at least three international reviewers. Submissions must be made through Please choose "SI: Cognitive Computing -OSN" when reaching the step of selecting article type name in submission process.
Last updated by Dou Sun in 2017-01-15
Special Issue on Social Internet of Things: Applications, Architectures and Protocols
Submission Date: 2017-05-01

Introduction Enabling autonomous interaction between social networks and Internet of Things is another emerging interdisciplinary area and is leveraging modern promising paradigm of Social Internet of Things (SIoT). Among other extensions of IoT, SIoT is the most recent one. It provides a platform for worldwide interconnected objects to establish social relationship by sacrificing their individuality to common interest and better service to users. This relationship among objects can be of co-location, co-work, parental, social or co-ownership. IP-enabled embedded devices and smart objects, short range and long range communication technologies, data collection, analysis, processing and visualization tools from big market giants and its multifaceted advantages in network navigability, scalability, evaluation of objects’ trustworthiness, service composition, object discovery, behavior classification and prediction, just the name of few, are giving it an accelerated momentum for becoming one of the most popular future Technologies. Due to this all-in-one embedded nature of SIoT, its architectural design, implementation, and operational manageability and maintenance are raising numerous prevalent concerns that are the challenges for researchers, academicians, engineers, standardization bodies and other market players. Future Generation Computer Systems (FGCS) is inviting high quality submissions presenting original work for this special issue. Submissions could discuss theoretical and applied research in topics including, but not limited to Application specific architecture and generic architecture of SIoT Applications of SIoT (Agriculture, Smart Cities, Military, Surveillance, Ocean, Transportation and Logistics, m-Heath, etc.) Existing and potential adaptation barriers and their solutions in SIoT Emergence in user experience and quality of service for SIoT Integration of various network and communication technologies (sensors, radio frequency identification, low power and energy harvesting, sensor networks, machine-type communication, resource-constrained networks, real-time systems, IoT data analytics, cloud computing) in SIoT, related issues and their solutions Prototyping of IoT experimental System (Simulation and Real Implementation) Maintenance (problem identification, troubleshooting, recurrent costs) Data Integration, Data Dissemination, Data Management, and Query Processing Algorithms Data processing (on nodes, big data, aggregation, distributed, discovery) Cyber-physical security, data privacy, and trust in SIoT Submission Details: Original, high quality contributions that are not yet published or that are not currently under review by other journals or peer-reviewed conferences are sought. Submitting authors should follow the Author Guidelines available from Paper submissions must follow the instructions described in the FGCS Web site at Authors have to select Choose Article Type “Sp Iss: Social Internet of Things”.
Last updated by Dou Sun in 2017-01-15
Special Issue on Affective Computing in Ambient Intelligence Systems
Submission Date: 2017-05-15

Affective computing (AfC) is a novel computing paradigm that builds on the results of artificial intelligence, biomedical engineering, and psychology to allow computer systems to detect, use, and express emotions. Thus, in order to deliver AfC systems multidisciplinary research approach is needed. The recent development of mobile and pervasive computing systems opened up new possibilities for AfC. In particular, the context-aware systems paradigm in Ambient Intelligence applications plays an important role. The motivation for this special issue is to bring together researchers from several domains to publish state-of-art research findings in the recent developments regarding practical AfC systems. We seek papers focusing on both theoretical and applied techniques, including experimental research on models needed to deliver AfC systems. We aim at emphasizing prospective and practical applications of AfC in Ambient Intelligence systems. The list of the topics includes, but is not limited to: - affective computing (AfC) - ambient intelligence (AmI) - context aware systems (CaS) - neurobiological approaches for AfC - models of emotions and affects - physiological measurements for affect detection - emotive user interfaces - body sensor networks - data mining and knowledge discovery methods for AfC and AmL - mobile platforms for context-aware affective systems - mixed (virtual/augmented) reality environments for affect generation and acquisition - affective games - gamification of AfC experiments
Last updated by Dou Sun in 2017-02-12
Special Issue on Spatiotemporal Big Data Challenges, Approaches, and Solutions
Submission Date: 2017-06-01

1. 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;
Last updated by Dou Sun in 2017-01-15
Special Issue on Future Networking Research Plethora for Smart Cities
Submission Date: 2017-06-01

In 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
Last updated by Dou Sun in 2017-01-15
Special Issue on Scheduling Algorithms for Cyber-Physical-Social Workflows
Submission Date: 2017-06-01

Cyber-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.
Last updated by Dou Sun in 2016-11-08
Special Issue on Big Data Analytics for Sustainability
Submission Date: 2017-06-30

Sustainability 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 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:, Google Scholar: (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: Google Scholar: Dr. Victor Chang Xi'an Jiaotong Liverpool University, China Email: Google Scholar:
Last updated by Dou Sun in 2016-07-23
Special Issue on Emerging Trends, Issues and Challenges in Internet of Things, Big Data and Cloud Computing
Submission Date: 2017-06-30

Cloud 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.
Last updated by Dou Sun in 2016-11-08
Special Issue on Bioinspired Algorithms in Complex Ephemeral Environments
Submission 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
Last updated by Dou Sun in 2017-01-15
Special Issue on Towards Smarter Cities: Learning from Internet of Multimedia Things-Generated Big Data
Submission Date: 2017-09-01

Smart 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.
Last updated by Dou Sun in 2016-11-08
Special Issue on Big Data and Internet of Things – Fusion for different services and its impacts
Submission 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 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
Last updated by Dou Sun in 2017-01-15
Special Issue on Security and Privacy for RFID and IoTs
Submission Date: 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 Detailed journal description can be found at 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.
Last updated by Dou Sun in 2017-01-15
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