A robust artificial intelligence: smart indoor positioning system

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Abstract

Over the previous few centuries, technology has converted massively from being a desktop personal computer to handheld mobile phones, with lower energy consumption of raw computing power. This computability is now incorporated with other systems as well as isolated to a single device. This paradigm was first noted in cyber-physical systems with the introduction of cloud services. The evolution of Artificial Intelligence(AI) with cloud computing and the importance of this field in human life, induce us to make simple and efficient talkative assistant robot for indoor navigation. The navigation system in outdoor typically rely upon Global Positioning System (GPS) but the indoor navigation systems have to rely on different technologies, as GPS signals cannot be received indoors. Thus, several technologies have been proposed and implemented over the past decade to improve navigation in indoors. But they were costly and less effective. Therefore, we have proposed a system that assists humans to find their location in a conversational manner. The suggested system was constructed by introducing the advantages of a personal assistant device, Amazon Alexa, the cloud services of Amazon and its voice services for indoor navigation. A Raspberry Pi 3 Model B is used as the element of the hardware to provide our system with intelligent characteristics. You can trigger the speech service using the "Alexa" keyword. Using the voice command, the skill/ application we created can be initiated. It operates a script on the cloud once Alexa is enabled, which runs a subroutine on the Raspberry Pi 3 in-turn to provide a path for that specific place. Once the Raspberry Pi calculation is finished, it sends the message back to Alexa. Alexa transforms the text into a voice and informs the user path.

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Subramani, B., & Deepika, S. P. (2019). A robust artificial intelligence: smart indoor positioning system. International Journal of Innovative Technology and Exploring Engineering, 8(10), 1393–1398. https://doi.org/10.35940/ijitee.A1001.0881019

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