全文截稿: 2021-04-21

影响因子: 4.231

CCF分类: 无

中科院JCR分区:

  • 大类 : 计算机科学 - 3区

  • 小类 : 计算机：软件工程 - 3区

网址：
https://www.computer.org/internet-computing/

The recent resurgence of artificial intelligence (AI) and its expanding applications have led to increased interest in the challenges of ethics and accountability, along with associated concerns such as fairness, liability, and verification. 

Much of the recent work on AI ethics and allied concerns takes an atomistic, single-agent perspective, such as the decision-making involved in the trolley problems, or the statistical aspects of machine-learning algorithms, such as optimizing a metric for the benefit of the decision-maker. 

In contrast, this special issue will focus on system-level perspectives on AI ethics and accountability, which consider how AI is embedded in technology while respecting the needs of society. Specific settings include a human or organization using an AI algorithm to arrive at decisions that affect others, as well as AI agents assisting humans in how they interact with each other and with existing social institutions. Research on statistical and decision-making aspects of AI ethics is welcome as long as it approaches the problem from a system perspective. 

Topics of interest include, but are not limited to, the following: 
- Sociotechnical systems with multiple stakeholders from a computational perspective 
- Models of institutions and norms 
- Conceptions of human values (such as privacy, fairness, and safety) 
- Ethics and accountability in organizations and business processes 
- Trust and trustworthiness in groups, including organizations and loosely structured communities 
- User interactions with AI agents in decision-making (such as loan application processing) and cyber-physical control (such as autonomous vehicles and manufacturing) 
- Methods and tools for supporting ethics and accountability, including requirements modeling, software engineering, and information system design 
- Evaluations of computational approaches for ethics and accountability, including datasets for evaluation 
- Applications and empirical studies in specific domains (such as transportation or healthcare)

全文截稿: 2021-04-30

影响因子: 0.0

CCF分类: 无

网址：
https://www.comsoc.org/publications/journals/ieee-tgcn

Background and Motivation: The future of wireless communication is transcending toward networks where the radio environment becomes controllable, programmable, and intelligent by leveraging emerging technologies such as Intelligent Reflecting Surface and Cognitive Radio. These networks possess perception, learning, reasoning, and decision-making capabilities where different parts of the network might be configured and controlled via user-centric AI as well as through intelligent and software-defined network paradigm. Reconfigurable wireless networks aim at enabling “intelligence” into the existing system to perceive and assess the available resources, to autonomously learn to adapt to the dynamism in the wireless environment, and to reconfigure its operating mode to maximize the utility of the available resources. Therefore, with the help of reconfigurable wireless networks, where nodes are capable of changing their frequencies, the problem of huge spectrum scarcity can also be addressed . 

Energy Efficiency is a critical issue in reconfigurable wireless communications networks, not only due to the technological limitations on energy supplies, but also due to the environmental impact caused by the information and communication technologies. Besides the environmental concerns about energy consumption in wireless networks, and the possible reduction in operational expenditures, the need to improve energy-efficiency becomes apparent, as the number of power consuming functionalities required from these devices increases. The existing wireless communication architectures and technologies may not be able to address these issues, as reconfigurable wireless networks have various distinct characteristics that require novel methods and tools to optimize and to efficiently operate them . Therefore, new approaches are required, using which spectrum and energy resources can be efficiently collected and intelligently managed. 

Technical Scope of the Proposal: This Special Issue addresses this need and primarily covers the following: 
Novel energy-efficient methods for reconfigurable wireless communication and networks. Energy efficiency improvement, including energy saving reconfigurable hardware and devices. Energy-efficient communication techniques for reconfigurable wireless networks/ intelligent reflecting surfaces. Design of energy-aware reconfigurable network architectures/ intelligent reflecting surface aided wireless network and protocols. Energy-friendly software and applications supporting sustainability and use of renewable energy sources. Machine learning algorithms for spectrum-and energy-efficient communications in reconfigurable wireless environments. Smart and reconfigurable environment using intelligent reflecting surface aided wireless network. 

全文截稿: 2021-05-01

影响因子: 11.052

CCF分类: 无

中科院JCR分区:

  • 大类 : 计算机科学 - 1区

  • 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址：
http://www.comsoc.org/commag/

Location awareness is an essential feature for many existing and emerging applications, especially in the context of 5G and Beyond (B5G) networks. However, current solutions generally fail to provide accurate location awareness. For example, the ability of satellite navigation systems to provide location information are hampered in urban canyons or indoor conditions, while 4G and Wi-Fi networks fail to provide the level of accuracy required by several emerging applications. 

The goal of this Feature Topic (FT) is to present solutions for accurate localization in 5G and B5G networks. This FT is open to contributions that belong to the following areas: i) localization techniques for 5G and B5G networks; ii) location-based services and applications; and iii) use cases of location-aware networks. It represents an opportunity to connect researchers and practitioners from industry and academia to share their recent findings in 5G and B5G localization networks. Areas of interest include, but are not limited to the following: 
- Integration of localization features in 5G and B5G architectures 
- Security and privacy issues of localization algorithms 
- 5G and B5G-based terminal localization techniques 
- Device-free localization 
- Exploiting localization information in smart network-management 
- Localization-assisted self-driving objects 
- Localization-aware emergency services 
- New applications exploiting localization information 
- Analytics, learning, and inference algorithms for localization 
- Case-studies of verticals exploiting localization features 
- Integration of localization, sensing, and communications in 5G and B5G

全文截稿: 2021-05-01

影响因子: 11.052

CCF分类: 无

中科院JCR分区:

  • 大类 : 计算机科学 - 1区

  • 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址：
http://www.comsoc.org/commag/

Over time, the capacity of fixed and backhaul networks has dramatically increased, thanks to advances in optical communications. However, the Internet traffic is still growing every year which forces network operators to look for efficient solutions to manage this traffic. Since the advent of Software-Defined Networking (SDN), the majority of researchers focus their attention on centrally managed networks with efficient traffic management solutions. However, current networks still operate mostly in a distributed fashion. They were developed in this manner to avoid performance bottlenecks and single points of failure. Their transition into centrally-managed networks involves complications. 

Using multipath transmissions improves network utilization by allowing the use of several disjoint paths between any two points in the network. Despite the benefits of this approach being obvious, current networks still do not use such mechanisms. Therefore, there is a renewed interest today in the design and performance evaluation of new traffic management techniques, multipath transmissions, congestion control mechanisms, and other solutions to maximize network efficiency and end-to-end performance in wired networks. 

This Feature Topic (FT) aims at investigating recent advances in traffic management designed for distributed wired packet networks. Prospective authors are invited to submit original high-quality contributions in all areas related to this topic including, but not limited to, the following: 
- Multipath approaches: design, path selection, performance evaluation; 
- New trends and results in congestion control; 
- Flow-based traffic control; 
- Applying machine learning/artificial intelligence for increasing network efficiency; 
- Other solutions aiming at increasing network efficiency and quality of service. 

This FT is prepared for solutions designed for wired packet networks that operate in a traditional, distributed fashion. Therefore, this FT is NOT for 1) wireless networks; or 2) centrally managed networks (such as SDN).

全文截稿: 2021-05-01

影响因子: 0.0

CCF分类: 无

网址：
https://www.comsoc.org/publications/journals/ieee-ojcoms

The IEEE Open Journal of Communications Society (OJ-COMS) invites manuscript submissions in the area of Low-Power Wide-Area Networks (LPWANs). 

Low-power wide-area networks (LPWANs) are one of the key enabling technologies for the Internet of Things (IoT). The main goal of LPWAN technologies is to provide long-range and low-power communications at relatively low bit rates using low-cost devices. Many LPWAN standards have been proposed (e.g, LoRa, Sigfox, LTE-M, NB-IoT, Weightless, and more) and some are starting to be used extensively in practical IoT deployments. However, several key challenges need to be overcome in order for the predicted massive LPWAN-enabled IoT deployments to materialize. 

Scalability limitations are inherent in LPWANs due to their low-power nature, which necessitates simplistic physical layer, MAC layer, and network designs. Mathematical models, simulations, and experimental deployments have demonstrated these limitations. Maintaining the low-power nature of LPWANs without impeding their scalability is a major challenge, which needs to be addressed through innovations on multiple layers as well as across layers. Massive practical deployments will also require the resolution of several privacy, security, and reliability concerns. The solutions to these challenges can be evaluated theoretically or through simulations. Moreover, the experimental evaluation of the proposed solutions for a wide range of future applications is particularly valuable. This can achieved either by using commercial off-the-shelf (COTS) devices or custom software-defined radio (SDR) testbeds. 

In this special issue, we aim to bring together leading researchers from both academia and industry to tackle a wide range of LPWAN technology challenges. This special issue solicits contributions both on existing standards and on groundbreaking proposals that could shape the LPWAN standards of the future. The topics of interest include, but are not limited to: 
- Novel physical layer techniques and waveforms 
- Improved receiver architectures (e.g., multi-user receivers, iterative receivers, MIMO receivers) 
- Coexistence of different LPWAN technologies 
- Modeling of interference and its effects on the physical and MAC layers 
- Novel MAC layer techniques and protocols 
- LPWAN scalability analysis using mathematical models and system-level simulators 
- Massive and non-orthogonal multiple access (NOMA) in LPWANs 
- Cross-layer design of LPWAN systems 
- Non-terrestrial LPWANs based on (nano-)satellites, HAPS, and UAVs 
- Localization and tracking using LPWANs 
- Edge computing in LPWANs with particular emphasis on offloading complexity from end devices 
- Privacy, security, and reliability enhancements 
- Applications to Industry 4.0, smart mobility, smart homes, smart cities, agriculture, logistics, and other emerging use cases 
- Software-defined radio (SDR) implementations and testbeds 
- Experimental deployments and measurements for link and network performance evaluation 

全文截稿: 2021-05-01

影响因子: 5.213

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 工程：综合 - 3区

  • 小类 : 数学跨学科应用 - 3区

网址：
https://www.comsoc.org/publications/journals/ieee-tnse

With the rapid development of machine learning and wireless communication technologies, intelligent applications and services, e.g., virtual personal assistants like Siri and Alexa, knowledge representation and reasoning to represent information about the world and solve complex tasks, have gained widespread popularity and large-scale implementation in our daily life: mobile entertainment, automotive, healthcare, education or industrial manufacture. These intelligent applications bring about unparalleled levels of transformation and benefits to human societies and national economies. Although the success of centralized machine learning has laid the foundation for many intelligent applications, the performance of the model usually depends on the availability of data. However, in most intelligent applications (e.g., intelligent transportation, smart finance), a large amount of useful data may be generated on multiple nodes and stored by multiple distributed devices, such as vehicles, smart phones and robots. Collecting such data to a central server for training will incur additional communication overhead, management and business compliance costs, privacy issues, and even regulatory and judicial issues (such as GDPR). Furthermore, it is usually impractical to require all the training data to be uploaded to the remote server with an increasing current network congestion, which hinder the applications of centralized machine learning in wireless networks. 

As a distributed learning technology, Collaborative Machine Learning (CML) has been recently introduced to collaboratively train a model among multiple networking agents by using on-device computation. With the help of advanced communication technologies, e.g., 6G, a large number of networking agents can achieve timely communicate to share the latest model updates for obtaining high-performance learning model. Typical CML scenarios mainly include 1) federated learning and split learning that enable each agent performs local model updates and exchanges locally with the central server or neighbor agents to iteratively improve model accuracy, e.g., different users hold different private images to jointly train an image classifier; and 2) edge learning that edge agents perform parallelizing model training and distributed model co-inference with agent synergy and task offloading. By integrating the high-potential CML with advanced emerging technologies, e.g., edge computing, blockchain, 6G communication and networking, and quantum communication, intelligent applications are evolving towards next-generation intelligent applications that provide more efficient, intelligent, and secure services. 

Although the next-generation intelligent applications dramatically enhance the life experience of humans and revolutionize modern business, there are still many open challenges that are unsolved when applying the fusion of CML and emerging technologies for next-generation intelligent applications. To achieve next-generation intelligent applications, CML needs significant research efforts on theories, algorithms, architecture, and experiences of system deployment and maintenance. Therefore, this Special Issue aims to offer a platform for researchers from both academia and industry to publish recent research findings and to discuss opportunities, challenges, and solutions related to collaborative machine learning. In particular, this Special Issue solicits original research papers about state-of-the-art approaches, methodologies, and technologies enabling efficient and practical collaborative machine learning towards the realization of next-generation intelligent applications. Potential topics of interest include but are not limited to the following: 
- New architectures and frameworks of collaborative machine learning for next-generation intelligent applications 
- Novel concept, theory, principles, and algorithms of collaborative machine learning for next-generation intelligent applications 
- Resource management for collaborative machine learning in next-generation intelligent applications 
- Privacy, trust and security issues in collaborative machine learning for next-generation intelligent applications 
- Adaptive control management for “edge intelligence” and/or “intelligent edge” 
- Incentive mechanism and crowd behavior study for collaborative machine learning in next-generation intelligent applications 
- Channel modeling analysis on collaborative machine learning for next-generation intelligent applications 
- Communication/Energy-efficiency or service throughput optimization issues on collaborative machine learning for next-generation intelligent applications 
- Big data analysis and knowledge discovery from collaborative machine learning for next-generation intelligent applications 
- Experimental studies on the convergence of collaborative machine learning for next-generation intelligent applications 
- Use cases that highlight the open issues and/or potentials of collaborative machine learning for next-generation intelligent applications 
- Emerging technologies (e.g., edge computing, blockchain, 6G or quantum communication) for collaborative machine learning in next-generation intelligent applications