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Lin, K., Kansal, A., Lymberopoulos, D. and Zhao, F. (2010) Energy-Accuracy Trade-Off for Continuous Mobile Device Location. Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, San Francisco, California, 5-18 June 2010, 285-298.
https://doi.org/10.1145/1814433.1814462

has been cited by the following article:

• TITLE: Multiple Architectural Approach for Urban Development Using Wearable IoT Devices: A Combined Machine Learning Approach

  AUTHORS: Raghu T Mylavarapu, Bharadwaja Krishnadev Mylavarapu

  KEYWORDS: Cloud Computing, Machine Learning, IoT, Authentication, Architecture

  JOURNAL NAME: Advances in Internet of Things, Vol.8 No.3, July 31, 2018

  ABSTRACT: Machine Learning becomes a part of our life in recent days and everything we do in interlinked with machine learning. As a technocrat, we tried to implement machine learning with Internet of Things (IoT) for better implementation of technology in organizations for security. We designed an sample architecture which will carry the burden of safeguarding the organizational data with IoT using machine learning with an effective manner and in this case we were proposing utilization of cloud computing for better understanding of data storage and retrieval process. Machine learning is used for the prediction models based on which we need to perform high level analysis of data and using IoT we promote authorization mechanism based on which we recognize the appropriate recipient of data and cloud for managing the data services with the three-tier architecture. We present the architecture we are proposing for better utilization of machine learning and IoT with cloud architecture.