期刊信息

ACM Transactions on Internet of Things (TIOT)

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影响因子:
3.7
出版商:
ACM
ISSN:
2577-6207
浏览:
9760
关注:
1
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征稿

ACM Transactions on Internet of Things (TIOT) is an academic journal published by ACM. (ISSN 2577-6207, impact factor 3.7, CCF C).

About TIOT ACM Transactions on Internet of Things (TIOT) is a new ACM journal that publishes novel research contributions and experience reports in several research domains whose synergy and interrelations enable the IoT vision. TIOT focuses on system designs, end-to-end architectures, and enabling technologies, and on publishing results and insights corroborated by a strong experimental component. This journal is published on a quarterly basis. Topics relevant to the journal are: Real-world applications, application designs, industrial case studies and user experiences of IoT technologies, including standardization and social acceptance Communication networks, protocols, and interoperability for IoT IoT data analytics, machine learning, and associated Web technologies Wearable and personal devices, including sensor technologies Human-machine and machine-machine interactions Edge, fog, and cloud computing architectures Novel IoT software architectures, services, middleware as well as future Internet designs Fusion of social and physical signals in IoT services Non-functional properties of IoT systems, e.g., dependability, timeliness, security and privacy, robustness Testbeds for IoT The submitted contributions are expected to provide experimental evidence of their effectiveness in realistic scenarios (e.g., based on field deployments or user studies), and the related datasets. The submission of purely theoretical or speculative papers is discouraged, and so is the use of simulation as the sole form of experimental validation. Experience reports about the use or adaptation of known systems and techniques in real-world applications are equally welcome, as these studies elicit precious insights for researchers and practitioners alike. For this type of submissions, the depth, rigor, and realism of the experimental component are key, along with the analysis and expected impact of the lessons learned. Surveys covering novel and emerging areas are also welcome and, by their nature, do not require an experimental component.
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