ACM Transactions on

Internet of Things (TIOT)


A New ACM Publication

ACM Transactions on Internet of Things (TIOT) is a new ACM journal that will publish 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.  The submission site is now open and the first issue is expected for publication in the second half of 2019.

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

All submissions 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.

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