仕訳帳情報
IEEE Internet of Things Magazine (IEEE IoTM)
https://www.comsoc.org/publications/magazines/ieee-internet-things-magazine
出版社:
IEEE
ISSN:
2576-3180
閲覧:
4631
追跡:
1
論文募集
IEEE Internet of Things Magazine (M-IoT) publishes peer-reviewed articles on end-to-end IoT solutions. M-IoT articles are written by and for practitioners and researchers interested in practice and applications, such as corporate engineers working to design and deploy IoT applications every day. The technical focus of M-IoT is the multi-disciplinary, systems nature of IoT solutions. Additionally, M-IoT addresses important non-technical aspects of IoT such as privacy concerns and regulatory affairs that must be understood to successfully deploy and operate real-world IoT systems. M-IoT also communicates with readers about the activities of the IoT Initiative and contains regular sections to help the practitioner negotiate the IoT landscape: tutorials, publication reviews, product reviews, and information on IoT resources and events. M-IoT is a forum for practitioners to share experiences, develop best practices, and establish guiding principles for technical, operational and business success.
最終更新 Dou Sun 2021-06-05
Special Issues
Special Issue on Data Science Driven Intelligent IoT
提出日: 2022-12-31

The Internet of things (IoT) is an interconnected system of computing devices, machinery, and digital machines that digitize the real world. The IoT has already affected people's lives, including transportation, housing, food, clothing, health, and remote monitoring. Many home appliances can be controlled through mobile phones and voice. Many applications allow users to improve their quality of life, and even enable the elderly and the disabled to live more conveniently. MGI's report shows that starting from 2025, the Internet of Things will create an output value of 3.9 trillion to 11.1 trillion US dollars in nine environments, including factories, retail, and cities, and the number of IoT devices is expected to grow to 754 100 million, which is equivalent to adding 127 IoT devices every second in the world starting in 2020. The operation of IoT systems can be summarized by the following three phases: the deployment of sensors to collect data, the conversion of collected data into useful information being able to be stored and accessed, the transformation of information to domain knowledge which will be utilized by the IoT system controller for users’ feedback or the system reactions. An IoT system becomes an intelligent IoT system if all tasks involving the three phases of IoT operations can be automated. Data science (DS) is a multidisciplinary approach to discovering, extracting, and presenting insights in data by focusing on data collection, data store and access, data analysis, and data communication techniques. Data science includes descriptive, diagnostic, predictive, and prescriptive capabilities. This means that through data science, administrators can use data to figure out what happened, why it happened, what happened, and what they should do with expected outcomes. Since the automation of an intelligent IoT system requires all tasks of DS, DS will be the most proper candidate technology ready to solve those issues faced by intelligent IoT systems. To collect data for IoT applications features, how to design sensor deployment and their connections via communication networks is the first main problem for intelligent IoT. The next step is how to apply machine learning (ML) and artificial intelligence (AI) algorithms to analyze and interpret insights concerning collected intelligent IoT data. Finally, it is also very crucial to communicate analysis results effectively to users of intelligent IoT devices. From the viewpoint of DS, we believe that the following categories of problems should benefit from DS related technologies in developing future intelligent IoT systems. The first problem is how to deal with intelligent IoT Big Data. The amount of generated data in each application unit of an intelligent IoT system is at least the scale of Terabytes (TB). The collection, exchange, storage, and access of such a huge amount of data at an intelligent IoT device is an exceedingly challenging task, as the computational and communication resources in intelligent IoT devices are extremely limited. Deep learning is a breakthrough technology in ML/AI. Deep-learning (DL) applications on IoT devices often have an extremely strict real-time requirement. For example, security camera–based object-recognition tasks usually require a detection latency of less than 400 ms to capture and respond to target events—for example, abnormal targets (identified by DL technology) appearing inside a building—in a short response time. Current IoT devices often offload intelligence computation to the cloud. However, consistent and reliable wireless communication links, which are only available at limited locations with high cost, become one of the main difficulties for these intelligent IoT devices to fulfill real-time requirements. Hence, the second category of problems about intelligent IoT is to have advanced ML/AI algorithms which can perform data analysis with input data impacted by unreliable communication links. However, enabling ML/AI capabilities on the intelligent IoT device side is not an easy assignment. The main properties of intelligent IoT devices are small memory size, low power and distributed. The third category of problems are to design new ML/AI algorithms that can be implemented at IoT devices in a distributed manner under small memory size and low power constraints. Finally, security, trust and privacy of intelligent IoT users are always main considerations for any new technologies. With the huge number of intelligent IoT connected devices, how to apply DS to enhance access control systems, trust management, and secure data sharing with privacy considerations over intelligent IoT systems is a challenging problem. This Special Issue (SI) will highlight the multidisciplinary approach to discovering, extracting, and presenting insights in data by focusing on data collection, data store and access, data analysis, and data communication techniques. Data science includes descriptive, diagnostic, predictive, and prescriptive capabilities. This means that through data science, administrators can use data to figure out what happened, why it happened, what happened, and what they should do with expected outcomes. Topics of interest include, but are not limited to: - Data science driven intelligent IoT system architecture design. - Intelligent IoT Big Data collection, storage and access. - Distributed data analysis. - Design new ML/AI algorithms to train at intelligent IoT devices under insufficient and contaminated data. - Implement advanced ML/AI algorithms in a distributed manner under small memory size and low power constraints. - Intelligent IoT data integrity, confidentiality and availability problems identifications and countermeasures. - Threat models and counterattack strategies for intelligent IoT. - Distributed and resource-saving intrusion detection systems for intelligent IoT. - Reliability and reputation model for the trust level of intelligent IoT devices. - Performance and scalability analysis in data science driven intelligent IoT. - Human-Computer Interaction (data visualization) in application development for data science driven intelligent IoT. - Application of intelligent IoT to social IoT. - Application of intelligent IoT to smart city. - Application of intelligent IoT to industrial IoT. - Leveraging the data-science driven intelligent IoT to other fields applications: energy, smart grid, logistics, transportation, supply chain, monetization, e-business, notarization, e-government, e-health, e-commerce, insurance, finance, fintech, e-learning, crowdsourcing, and crowd sensing applications.
最終更新 Dou Sun 2022-10-22
Special Issue on Ubiquitous Intelligence for Internet of Vehicles
提出日: 2022-12-31

As one of the typical networking paradigms for Internet of Things, the Internet of Vehicles (IoV) enables seamless information dissemination and task processing among vehicles equipped with onboard sensing, communication, computing, and storage capabilities. With the development of advanced artificial intelligence (AI) techniques, it is envisioned to realize the ubiquitous intelligence for IoV. Different network entities including connected vehicles, wireless base stations, edge/cloud servers, aerial/space-assisted devices (e.g., drones, satellites), are expected to efficiently interact to perceive, reason, and make intelligent decisions based on context awareness for improving the networking and computing effectiveness. Highly responsive task computing, adaptive networking, and efficient resource control are imperative for the implementation of intelligent, safe, and ubiquitous IoV to satisfy increasingly differentiated vehicular application requirements. To realize the ubiquitous intelligence for IoV, further research endeavors are needed especially when high network complexity and vehicle mobility are present. On one hand, it is of paramount importance to explore how efficient AI frameworks are developed. For instance, hierarchical learning architectures can be investigated to fit in different IoV scenarios for improving the overall networking, computing, and control performance. On the other hand, technical challenges need to be overcome to deal with highly dynamic and complex vehicular network environments. Therefore, a practical implementation of AI modules can be achieved through balancing learning performance and complexity. The objective of organizing this Special Issue is to attract and disseminate novel research ideas, concepts, approaches, and findings from both academia and industry to tackle challenges in developing effective and practical solutions for ubiquitous intelligence for IoV. Potential topics include but are not limited to: - Architectures, frameworks, and concepts for AI-enabled IoV - Intelligent networking and computing for autonomous driving - AI-enabled end-edge-cloud interplay for IoV - Intelligent communication, computing, and control for IoV - Edge intelligence for IoV - Vehicle-to-vehicle-assisted intelligent communication and computing - Intelligent space/air-assisted IoV - Distributed learning for IoV - Intelligent edge content caching for IoV - Intelligent multi-dimensional resource control for IoV - Intelligent security and privacy preservation for IoV - AI-based data analytics for IoV - Digital-twin-assisted intelligent IoV - Short-range wireless communication for IoV - Autonomous authentication and handover authentication in IoV
最終更新 Dou Sun 2022-10-22
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