A study of wireless technologies for IoT applications in terms of power consumption has been presented in this paper. The study focuses on the importance of using low power wireless techniques and modules in IoT applications by introducing a comparative between different low power wireless communication techniques such as ZigBee, Low Power Wi-Fi, 6LowPAN, LPWA and their modules to conserve power and longing the life for the IoT network sensors. The approach of the study is in term of protocol used and the particular module that achieve that protocol. The candidate protocols are classified according to the range of connectivity between sensor nodes. For short ranges connectivity the candidate protocols are ZigBee, 6LoWPAN and low power Wi-Fi. For long connectivity the candidate is LoRaWAN protocol. The results of the study demonstrate that the choice of module for each protocol plays a vital role in battery life due to the difference of power consumption for each module/protocol. So, the evaluation of protocols with each other depends on the module used.

Distributed transformers are imperative equipment in power networks. Due to large amount of transformers distributed over a widespread area in power electric systems, the data acquirement and condition monitoring is essential concern. In spite of security and automation in plants, industrial environment is relatively critical for machines and humans. This study deals with a safety in industrial condition. A model has been designed to detect dangerous situations like breakdown that is the most essential parameter for occurring leakage current in substation and gas leakage based on faults data source file from distinguished sources. Reached outcome will be executed in IoT (Internet of Things) gateway design, to augment technical implementation with scalability. It consists of Node-MCU, node red server, mosquito server that serves as negotiator amid Node-MCU and node red server and Thingspeak IoT platform. Data processing has been completed at Thingspeak IoT platform so that the monitored quantity can be displayed frequently or at scheduled intervals of time. These processed results have been statistically further analyzed using SPSS software package.

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