Outdoor Fingerprinting-based location for IoT in machine learning

Description

1.1 Internet of things and Outdoor Location Methods for LPWAN(Low power WAN) 1.1.1 Introduction (Why Locations is important in internet of things application) 1.1.2 Radio Propagation Model in IoT (What is? how work? And related work in IoT) 1.1.3 Network-Based Location Methods in IoT (What is? how work? And related work in IoT) • ToA • TDoA • AoA 1.1.4 Satellite-Based Location Methods in IoT (What is? how work? And related work in IoT) • GPS • AGPS 1.1.5 (RSSI)Receive signal strength Indicator Fingerprinting-Based Location Methods in IoT (what is? how work? And related work in IoT) 1.1.6 RSSI fingerprinting Location using a machine learning models What is? how work? And related work in IoT: – K nearest neighbors in regression – Support vector regression – Decision Tree regression – Random Forest in regression – Gradient boosting regression =========================================================== This work is literature review. The main Idea of this work is: 1- How location methods serve the internet of things application in outdoor environment. 2- I am going to use RSSI fingerprinting location methods and Machin learning in this study, So I have to write all Location methods and why fingerprinting is suitable one(because it’s low cost than GPS, TOA,TODA..etc) 3- The related work should be in the part of Internet of things, and LPWAN devices used like LoRaWAN, SigFox where these devise support long-range and low power. And you can add as (Wi-Fi, LTE, Bluetooth and ZigBee) technology and indoor environment to complete the section and not exceed than 30% and theremain(70%) for outdoor environment, LoRaWAN, SigFox. This also included of the performance of accuracy. 4- 15 pages total. 5- Please I need the reference in the last 3 years and professional work.

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