【今日新增】IEEE Trans.专刊截稿信息8条

2017 年 6 月 29 日 Call4Papers Call4Papers
【今日新增】IEEE Trans.专刊截稿信息8条
计算机综合与前沿

IEEE Transactions on Intelligent Transportation Systems

Dependable Wireless Vehicular Communications for Intelligent Transportation Systems (ITS)

全文截稿: 2017-08-01
影响因子: 2.534
期刊难度: ★★★★
CCF分类: B类
网址: http://sites.ieee.org/itss/publications/transactions/
Over the past couple of decades, transportation systems begin to receive widespread attention from the scientific community and emerged towards Intelligent Transportation Systems (ITS). Effective vehicular connectivity techniques can significantly enhance efficiency of travel, reduce traffic incidents and improve safety, alleviate the impact of congestion; devising the so-called Intelligent Transportation Systems (ITS) experience. Furthermore, during the past decades the volume and density of vehicles increased significantly, especially the road traffic; this lead to a dramatic increase in number of accidents and congestion, with negative impacts on the economy, environment and quality of people's lives. In particular, according to the World Health Organization (WHO), road traffic injuries are estimated to be the leading cause of death for young people aged (15-29) and the ninth cause of death worldwide in 2015. The enabling communication technologies are intended to realize the frameworks that will spur an array of applications and use cases in the domain of road safety, traffic efficiency, and driver's assistance. Although these applications will allow dissemination and gathering of useful information among vehicles and between transportation infrastructure and vehicles in pursuance of assisting drivers to travel safely and comfortably; still much efforts are required to implement these practices for the success of these applications.

Traffic safety applications such hazard location warnings and collision warnings etc. rely heavily on the timely delivery of safety-critical real-time data. Most of these applications demand strictly bounded timing response and are highly dependent on the performance of the underlying wireless vehicular communication technology. In most cases, these systems are required to have dependable timeliness requirements since data communication must be conducted within predefined temporal bounds along with fulfilling other requirements such as reliability, security etc. This is mainly because the unfulfillment of these requirements may compromise the expected behaviour of the system and cause economic losses or endanger human lives. In addition, the broadcast nature of wireless communications in an open environment makes it more vulnerable to un-wanted external entities compared to the wired communications. Therefore, the consideration of the real-time aspects in the implementation of ITS services offers great potential to improve the level of safety, efficiency and comfort on our roads. In addition, European ITS considered the deterministic medium access methods as one of the criteria for the evaluation of possible ITS communication technologies.

In addition to the fact that applications for traffic safety rely heavily on the support of wireless vehicular communications, the existing standards such IEEE 802.11p do not provide deterministic real-time support and the other enabling technologies such LTE-V from 3GPP and traffic sensors are either in their definition stage or not fully deployed and tested for dependable real-time communication.

Therefore, the purpose of this special issue is to publish high-quality research, expecting both from academic and industrial stakeholders, and serves as an outlet for disseminating innovative solutions towards meeting the expectation of ITS and mainly real-time dependable communication.
Original, high quality contributions that are not yet published, submitted or not currently under review by other journals or peer-reviewed conferences are sought.

Topics of interest include, but are not limited to, the following scope:
- New paradigms for dependable and real-time vehicular communication
- Dependable wireless vehicular communications technologies
- Dependable distributed architectures for ITS
- Dependable aspects of smart mobility and cooperative ITS
- Dependable M2M communication in the scope of ITS
- Dependable data distribution platforms for ITS
- Dependable public transport prioritization
- Real-time and dynamic prediction of traffic flows
- Dependable automatic incident detection and recovery
- Architecture, design, implementation and management of dependable applications supported by wireless vehicular communications
- Physical layer dependability
- Fault-tolerant techniques for wireless vehicular communications
- Harmonizing security and timeliness in wireless vehicular communications
- Dependability evaluation of wireless vehicular communication systems: Analytical and Numerical methods, simulations, experimentation, benchmarking, verification, field data analysis.




计算机综合与前沿

IEEE Transactions on Industrial Informatics

Special Section on 5G and Beyond Mobile Technologies and Applications for Industrial IoT

全文截稿: 2017-08-30
影响因子: 4.708
期刊难度: ★★★★
CCF分类: 无
网址: http://tii.ieee-ies.org/
Following the tremendous success of 2G and 3G mobile networks and the fast growth of 4G, the next generation mobile networks (5G) was proposed aiming to provide infinite networking capability to mobile users. Differentiated from 4G, benefits offered by 5G is much more than the increased maximum throughput. It aims to involve and benefit from many current technical advances including Industrial Internet of Things (IIoT). As the IIoT integrates many heterogeneous networks, such as Wireless Sensor Networks (WSNs), Wireless Local Area Networks (WLANs), Mobile Communication Networks (3G/4G/LTE/5G), Wireless Mesh Networks (WMNs) and wearable health care systems, it is critical to design self-organizing and smart protocols for heterogeneous ad hoc networks in various IoT applications, such as cyber-physical systems, cloud computing for heterogeneous ad hoc networks, large-scale sensor networks, data acquisition from distributed smart devices, green communication and applications, environmental monitoring and control, etc. Moreover, based on the survey conducted by the World Health Organization, the world will lack 12.9 million health care workers by 2035. Hence, it is important to develop wearable health care systems to perform self-health monitoring. In general, wearable health care systems demands low power consumption and high measurement accuracy. Smart technologies including green electronics, green radios, fuzzy neural approaches and intelligent signal processing techniques play important roles for the developments of the wearable health care systems. This special issue aims at providing a forum to discuss the recent advances on 5G and beyond mobile technologies and applications for IIoT.

Topics include, but are not limited to, the following research topics and technologies:
- Architecture of IIoT in 5G networks
- Software defined solutions for IIoT
- Energy efficiency and energy harvesting in IIoT
- Terminal intelligence and light weight sensors
- Data collection, processing, aggregation, and communication
- Co-existence and device inter-operability of sensors with 5G networks
- Data processing and anomaly detection for IIoT networks
- Machine-type communications in IIoT5G systems
- Emerging IIoT applications in 5G networks
- Experimental results, prototypes and testbeds using sensors for 5G technologies




计算机综合与前沿

IEEE Transactions on Industrial Informatics

Special Section on Industrial and Commercial Demand Response

全文截稿: 2017-08-31
影响因子: 4.708
期刊难度: ★★★★
CCF分类: 无
网址: http://tii.ieee-ies.org/
Non-residential (industrial and commercial) customers have great potential in providing flexibility for power systems through diverse Demand Response (DR) programs. Intelligent energy management can be carried out with DR in industrial and commercial facilities, especially if on-site control, information and communication technologies are available, enabling also the inherent automation capabilities of heating, ventilation and air conditioning systems. Due to the dawn of the Smart Grid era, with increasing distributed generation and the conversion of traditionally passive consumers to newly active energy players in the market, DR is being effectively considered for outage management and network reinforcement deferral. The industrial and commercial potential of DR is not yet completely understood, especially regarding the emerging and advanced technologies associated to the Smart Grid. Advances in smart meter technology that allow monitoring and controlling responsive loads in near real-time will also be key enablers of DR potential. It can be more complex to implement DR for industrial loads if compared to residential loads mainly due to the reliability management that is more vital for industrial plants. An interruption of service may lead to stopping production or violating operational constraints of the plant. Industrial processes can be interdependent and correlated, being difficult to isolate and shed separately. Several manufacturing processes are critically dependent on time and must be scheduled with high precision. DR solutions can reduce costs related to energy consumption and increase renewable sources exploitation. This special section aims at providing a forum to discuss the most recent advances on Industrial and Commercial DR.

Topics include, but are not limited to:
- Advanced informatics for industrial/commercial DR
- Industrial/commercial DR schemes, programs and optimization models
- Information and communication infrastructure of industrial/commercial DR
- Sensors, metering and control technologies for industrial/commercial DR
- Industrial/commercial DR interactions with renewable/distributed generation
- Trading industrial/commercial DR in wholesale and retail markets
- Dynamic prices and tariffs in industrial/commercial DR
- Price prediction and/or load forecasting for industrial/commercial DR
- Strategic market behavior of industrial/commercial DR aggregation agents
- Industrial/commercial DR participation in ancillary services
- Electric vehicles and energy storage participation in industrial/commercial DR
- Integration of multi-energy systems with industrial/commercial DR
- Security and privacy issues related to industrial/commercial DR
- New and existing business models for industrial/commercial DR
- Cost/benefit evaluation, barriers and drivers of industrial/commercial DR
- Regulation, protocols and standards of industrial/commercial DR




数据库管理与信息检索

IEEE Transactions on Big Data

Special Issue on Big Data in Ubiquitous Computing

全文截稿: 2017-09-01
影响因子: 无
期刊难度: ★★★
CCF分类: 无
网址: https://www.computer.org/portal/web/tbd
With the continuous expansion of ubiquitous sensors, devices, networks and Internet of Things, all kinds of data become widely available and large in amount. Generation of huge amounts of data, called big data, reflects the dynamics of physical world and can be the basis for ubiquitous intelligence. Big data in ubiquitous intelligence scenarios exhibit some specific characteristics, like multi-source, heterogeneous, large-scale, real-time streaming, continuous, ever-expanding and spatial-temporal. Traditional ubiquitous computing approaches or systems began to show their limitations. It is difficult to manage and utilize all kinds of big data to accelerate ubiquitous intelligence in real-world. We believe that we need a new way for ubiquitous intelligence and computing where big data is immensely involved, especially for the data trace collected from ambient sensors, wearable, social media and so on. Intensive research is required on the collaboration between big data and ubiquitous computing. This special issue, as a dedicated forum, aims for the scientific and industrial community to present their novel models, methodologies, techniques and solutions which can address theoretical and practical issues.




信息安全及密码学

IEEE Transactions on Dependable and Secure Computing

Special Issue on Security in Emerging Networking Technologies

全文截稿: 2017-09-30
影响因子: 1.592
期刊难度: ★★★★★
CCF分类: A类
网址: https://www.computer.org/web/tdsc/
Network infrastructure is undergoing a major shift away from ossified hardware-based networks to programmable software-based networks. One compelling example of this paradigm shift is the advent of Software-Defined Networking (SDN). A traditional network mixes control and traffic processing logic in single hardware devices, making the network more complex and harder to manage. SDN has addressed this issue by decoupling the control plane in network devices from the data plane to simplify production networks. On the other hand, enterprise networks are populated with a large number of proprietary and expensive hardware-based middleboxes, such as firewall, IDS/IPS, and load balancing. Hardware-based middleboxes present significant drawbacks such as high costs, management complexity, slow time to market, and unscalability. Network Function Virtualization (NFV) was proposed as another new network paradigm to address those drawbacks by replacing hardware-based network functions with virtualized software systems running on generic and inexpensive commodity hardware. Given their benefits, SDN and NFV have recently attracted significant attention from both academia and industry.

SDN and NFV introduce significant granularity, visibility, flexibility, and elasticity to networking, but at the same time bring forth new security challenges. For example, decoupling the data plane and the control plane in SDN essentially opens a door to attackers for exploiting the vulnerabilities of SDN controllers, APIs, applications, and protocols, and further break their trust relations. Meanwhile, both SDN and NFV could be leveraged to strengthen network defense. The aim of this special issue is to encompass research advances in all areas of security in emerging networking technologies. The special issue intends to provide a venue for interested researchers and practitioners to share their novel research ideas and results.

This special issue calls for original, high-quality, high-impact research papers related to the following broad topics, but are not limited to:
- SDN/NFV-enabled security architecture
- SDN/NFV-based automated network security
- SDN/NFV-based mitigation for attacks
- SDN/NFV-based network forensics and auditing
- Authentication/confidentiality in SDN/NFV-based networks
- Proofs of security in SDN/NFV-based networks
- Logic flaws in SDN/NFV implementations
- Attacks/defense to SDN controllers, protocols, and APIs
- SDN/NFV-oriented security policy enforcement
- Trust management of SDN applications and controllers
- Development and deployment of NFV-based security functions (virtual firewalls, virtual IDSs, virtual DDoS mitigation, etc.)
- SDN/NFV-enabled security for Internet of Things
- Safe state migration in NFV
- Network security as a service
- Privacy-preserving solutions for SDN/NFV
- Security of programmable components




计算机综合与前沿

IEEE Transactions on Industrial Informatics

Special Section on Deep Learning Models for Industry Informatics

全文截稿: 2017-09-30
影响因子: 4.708
期刊难度: ★★★★
CCF分类: 无
网址: http://tii.ieee-ies.org/
Deep learning is a novel research direction in machine learning field. In recent years, it has made breakthrough progress in many applications such as speech recognition, computer vision, industrial control and automation etc. The motivation of deep learning is to establish a model to simulate the neural connection structure of human brain. While dealing with outside complex signals, it adopts a number of transformation stages to give the in-depth interpretation of the data. Shallow learning is to rely on the artificial experience to extract the characteristics of the sample datasets, and the network model is obtained after the study which has no hierarchical structure; while the deep learning treats the original signal with layer by layer feature transformation, and transforms the feature representation of the sample in the original space into the new feature space, and automatically learns the hierarchical representation of the feature, which is more conducive to the classification or feature visualization. Deep learning achieves exceptional power and flexibility by learning torepresent the task as a nested hierarchy of layers, with more abstract representations computed in terms of less abstract ones. The current resurgence is a result of the breakthroughs in efficient layer-wise training, availability of big datasets, and faster computers.

It is expected that the development of deep learning theories and applications would further influence the field of industry informatics. This special issue mainly focuses on deep learning models for industry informatics, addressing both original algorithmic development and new applications of deep learning. We are soliciting original contributions, of leading researchers and practitioners from academia as well as industry, which address a wide range of theoretical and application issues in deep learning for industry informatics. Topics for this special issue include, but are not limited to:

Topics include, but are not limited to, the following research topics and technologies:
- Deep learning for Internet-based monitoring and control systems
- Deep learning for collaborative factory automation
- Deep learning for distributed industrial control and computing paradigms
- Deep learning for real-time control software for industrial processes
- Deep learning for control of wireless sensors and actuators
- Deep learning for systems interoperability and human machine interface
- Deep learning for industrial focused software development
- Deep learning for reusable analytics tools and frameworks
- Deep learning for urban informatics
- Deep learning for statistical tools for electric machine and drives condition monitoring
- Deep learning for DSP and FPGA-based system implementation
- Deep learning for industrial system security and intrusion




计算机综合与前沿

IEEE Transactions on Industrial Informatics

Special Section on Embedded and Networked Systems for Intelligent Vehicles and Robots

全文截稿: 2017-09-30
影响因子: 4.708
期刊难度: ★★★★
CCF分类: 无
网址: http://tii.ieee-ies.org/
Embedded and networked systems for intelligent e-vehicles and robots are emerging, with a high economic, societal and industrial impact. They will improve safety (reducing accidents caused by human errors), sustainability (increasing transport system efficiency), comfort/inclusivity (ensuring user's freedom for other activities and mobility for all), logistic & factory automation (with a key role in industry 4.0, allowing industrial robots moving and operating autonomously and cooperating). There are many key enabling technologies for the revolution, such as networked sensors, actuators and embedded computing/control platforms distributed onboard the vehicle/robot...etc. Moreover, Artificial Intelligence (AI) and deep learning computing platform are emerging to achieve full intelligent autonomous mobility of vehicles and robots.

Solving these issues for intelligent e-vehicles and robots will have benefit for other applications such as unmanned vehicles and Industry 4.0 scenarios. Last but not least, worldwide standardization and homogenization efforts are needed to ensure interoperability of the solutions.

We solicit papers covering the following topics of interest, but not limited to:
- Embedded systems, embedded software, hardware/software partition & real-time systems in vehicles
- Real-time communications in vehicles, e.g., AVB, TSN, CAN, Flexray
- Scheduling and schedulability analysis techniques
- Models, languages and techniques to deal with the complexity of vehicle software
- Advanced powerful execution platforms, e.g., multi-core Electronic Control Units
- Functional safety and certification (e.g., according to ISO 26262) aspects in vehicles.
- Authentication, privacy and security issues in automated and connected vehicles/robots
- Networking for E-transportation and smart grid
- Peer-to-peer and cooperating vehicles and robots
- Over-the-air diagnostic and firmware/software update
- AI and deep learning computing for self-driving vehicles/robots
- V2X/M2X wireless transceivers, data-link, MAC and networking layers
- Synergies among vehicular, robotics and Industry4.0 technologies, tools and industrial case studies
- Homogenization and standardization to ensure interoperability, security and functional safety




数据库管理与信息检索

IEEE Transactions on Big Data

Special Issue on Big Data from Space

全文截稿: 2018-01-31
影响因子: 无
期刊难度: ★★★
CCF分类: 无
网址: https://www.computer.org/portal/web/tbd
Big Data from Space refers to the massive spatio-temporal Earth and Space observation data collected by space-borne sensors, and their use in synergy with data coming from other aerial or ground based sensors or sources. This domain is currently facing sharp development with numerous new initiatives and breakthroughs ranging from computational sensors to space sensor web, covering almost the entire electromagnetic spectrum from Gamma-rays to radiowaves, or from gravitational to quantum principles. The analysis of these data largely contributes to the broad scientific effort to understand the Universe and to enhance life on Earth. The recent multiplication of open access initiatives to Big Data from Space is giving momentum to the field by widening substantially the spectrum of scientific communities and users as well as awareness among the public while offering new benefits at all levels from individual citizens to the whole society.

In this Special Issue, we solicit high-quality scientific research articles, in areas such as, but not limited to, Earth Observation, planetary sciences, Space and Security, deep space exploration, astrophysics, satellite telecommunication, navigation and positioning systems, addressing key challenges and innovative solutions on how Big Data paradigms can improve the space sciences, technologies, and applications.

Topics of interest include, but are not limited to:  
- Data from space and suborbital sensor cooperating networks
- Computational sensing and imaging
- On-board data handling functions and technologies
- Data transfer and telecommunication systems
- Data preservation, storage, dissemination and computing platforms
- Information retrieval and knowledge extraction  
- Data mining and visual analytics  
- Data Science
- Scalability in any sense
- Semantic and knowledge representations  
- Quantum resources
- Performance metrics and benchmarking



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