A reduced power wifi multi-sensor node based on cloud

Reduced power wifi multi-sensor node based on cloud

Name: Anekwong Yoddumnern

Address: 333 Moo1, Thasud, Muang, Chiang Rai 57100

Organization : Mae Fah Luang University. School of Information Technology. Computer Engineering

Email : ask.library@mfu.ac.th

keyword: Wifi

LCSH: Computer engineering

Classification :.LCCS: TK7885.A1

LCSH: Computer networks

LCSH: Engineering

LCSH: Communications Engineering, Networks

LCSH: Computer Systems Organization and Communication Networks

LCSH: Information Systems and Communication Service

Abstract: A wireless network is necessary. Especially of WiFi technology is very important there has been run over service of the internet of things (IOT) and any a social network for many years. There is more requirement for a people and popular-up following a smartphone. Most of the WiFi technology has run based on Cloud. Currently, A Cloud technology has several services. It is a full option for a private and public online service. The main point of the processing is a smart device. Many devices have run under a detector for all of the digital and analog processing. This study aims to design a multi-sensor on a microcontroller unit (MCU) over the best methodologies. There have simulated a case of home security. Thereby, the sensor connected to an MCU with a general purpose input output (GPIO) pins. In this connection, some of the communication has erred and invalided value for all portions of the pre-processing and post-processing. First, the temperature sensor does not run immediately. Second, the smoke-CO has read an invalid value. Last, a WiFi module sent data to a Cloud all time and used more power. The new model has implemented with three methods. (1) Sensor Calibration, it was set in the pre-processing. This portion has used calibration-time by the dynamic time following the set mode of GPIO function. There were 1.75, 2.70, and 7.44 seconds upon the number of sensors. The self-calibration by the finite impulse response (FIR) filter is N-tabs following a delay and adder. It got a difference coefficient value. (2) The Full Scale Kalman Filter (FSKF) is to filter and estimate the best value. There run as long time. They got invalid data—such as high and low from the actual and sometimes it swings. As a reason of the device has a heat collected and the inside or outside noise. (3) The OFF-Mode helped to save power consumption of 37.24%. These methods are saving the device to work for a long time within a long life and run as high stable.

Mae Fah Luang University. The Learning Resources and Education Media Center

Address: Chiang Rai

Email: library@mfu.ac.th

Name: Thongchai Yooyativong

Role: Advisor

Email : ask.library@mfu.ac.th

Name: Roungsan Chaisricharoen

Role: Co-Advisor

Email : ask.library@mfu.ac.th

Created: 2018

Modified: 2020-05-07

Issued: 2562

Issued: 2019-05-21

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: Thesis TK7885.A1 A578r 2018

eng

DegreeName: Dotor of Philosophy

Level: Doctoral Degree

Descipline: Computer Engineering

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