Precise time synchronization becomes a vital constituent due to the rigorous needs of several time-sensitive applications. The clock synchronization protocol is one of the fundamental factors that can define the quality of communication. Our study starts with a brief discussion on the application domain of precise time synchronization and comes with an in-depth study of the synchronization with the main focus on the IEEE 1588 Precision Time Protocol (PTP). We have compared the well-known synchronization techniques and conclude that the PTP is the most appropriate answer to robust clock synchronization though challenges are there that requires thoughtful efforts and modification in the current version. The working mechanism and main components of the PTP network are discussed. We have established a testbench using commercially available devices and development boards to evaluate the PTP performance under different configurations. Major sources of synchronization error and other aspects contributing to precision are examined. This paper discussed numerous approaches that could enhance the performance of the PTP protocol. Structures for PTP based wireless clock synchronization required by advanced applications has also been discussed. In the end, paper focuses on the main industrial application areas in which PTP plays an important role, including WLAN, optical data centers, Smart grid, IEC 61850, etc. We conclude the paper by identifying the future trends and research directions for PTP based clock synchronization.
Internet of Things (IoT) platforms applied to health promise to offer solutions to the challenges in healthcare systems by providing tools for lowering costs while increasing efficiency in diagnostics and treatment. Many of the works on this topic focus on explaining the concepts and interfaces between different parts of an IoT platform, including the generation of knowledge based on smart sensors gathering bio-signals from the human body which are processed by data mining and more recently, deep neural networks hosted on cloud computing infrastructure. These techniques are designed to serve as useful intelligent companions to healthcare professionals in their practice. In this work we present details about the implementation of an IoT Platform for real-time analysis and management of a network of bio-sensors and gateways, as well as the use of a cloud deep neural network architecture for the classification of ECG data into multiple cardiovascular conditions.
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