Low-cost BLE based Indoor Localization using RSSI Fingerprinting and Machine Learning

Jain, Charu; Sashank, Gundepudi V Surya; Venkateswaran, N.; Markkandan, S.

doi:10.1109/wispnet51692.2021.9419388

Cited by 38 publications

(20 citation statements)

References 9 publications

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“…So, we implement a system using IOT which consists of RFID reader and LCD display, while customer takes an object and placed within the trolley, that time, the reader scan the item and the amount of the product is added to the bill through IOT Server and it will automatically updated to the store in charge through IOT server and consumer also knows the value of money they spend on the items before billing [13]. So, they can add or remove according to the convenience and also they can pay the bill from their card itself using indoor localization [14]. The system also has automatic trolley set up which can be operated using IOT control switches.…”

Section: Proposed Systemmentioning

confidence: 99%

Shopping Wagon: A Smart Shopping System Using RFID for Shopping Malls

Deepa

Nihila.A²,

Prabhavathi.J³

et al. 2021

Recent Trends in Intensive Computing

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This project describes a supermarket automation trolley based on an RFID reader. The trolley is equipped with an RFID reader and an electronic hardware system to make the transaction more convenient. The RFID card, whose price is set into the reader, is used to correct those items that are above a certain number. The value of the item is added to the sales bill and shown on the LCD monitor when the item is shown in front of the reader. The trolley car is programmed in such some way that it’ll move consistent with the user command. It additionally has the supply for removing the things from the trolley car wherever price is aloof from the overall cost. The user can view their bill through IOT along with the number of items purchased and total bill amount. The user can also pay the bill using his card provided and the system will give an alert if the total amount exceeds the amount in the card. The system will also suggest the user whether the purchased product is suitable for their health condition or not through IOT app provided to the user.

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Section: Proposed Systemmentioning

confidence: 99%

Shopping Wagon: A Smart Shopping System Using RFID for Shopping Malls

Deepa

Nihila.A²,

Prabhavathi.J³

et al. 2021

Recent Trends in Intensive Computing

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“…[6] The author address the prediction of consumer patterns helps the firms to plan and allocate the resources efficiently and improve the business wisely [8]. To predict the customer analysis the author proposes some consumer-relevant attributes from previous purchases and use three different ML algorithm to analyzes these data by indoor localization and movement prediction [9]. Experimental results reveal gradient model performs well compared to the other two approaches.…”

Section: Literature Surveymentioning

confidence: 99%

Customer Analysis Using Machine Learning Algorithms: A Case Study Using Banking Consumer Dataset

Subramanıan

Prabha²,

Maheswari³

et al. 2021

Advances in Parallel Computing

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The aim of each enterprise is to achieve high revenue from the business and to stay in a high position from their competitors. To archive high revenue and high position from competitors the need of understanding the business consumers is a crucial one. However the firm business is completely dependent on the consumers the efficient analysis of consumers within the enterprises makes to achieve the business to high position. To perform effective consumer analysis, in this study different machine learning is studied and experimented. ML classifiers make to understand in-depth analysis about the consumer data and further enables to plan wise decision strategies to enhance the business revenue and consumer satisfaction intelligently. The use of different ML classifiers is to sort out how the customer prediction outcome changes accordingly to the ML classifier is applied. This makes to find the best ML classifier for the consumer dataset applied in this study. The experimental procedure is performed using different ML classifiers and the outcome achieved is captured and projected using various validity scores. This work applies different ML classifiers like K-NN, C4.5, Random Forest, Random Tree, LR, MLP and NB for customer analysis. The empirical results illustrate the C4.5 model achieves better accuracy prediction compare to other ML classifiers and also compared with the time complexity NB model works efficiently with running time.

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“… Fingerprint Authentication: Biometric sensor module used in this project is R307. This alludes to the robotized strategy for checking a match between two human fingerprint [8].  Heartbeat Sensor: HW827 is the heartbeat sensor used in this project which is measured by just placing the tip of your index finger delicately ludicrous all over.…”

Section: Modules Descriptionmentioning

confidence: 99%

Smart Ambulance System with Remote Knowledge Communications Through Cloud

Jayashree¹,

Shivaranjani²,

Suvetha³

et al. 2021

Recent Trends in Intensive Computing

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An ambulance is one of saving many lives by taking the people who need health emergencies. Saving the life of the person is one of the challenging and precious ones. Our key idea is to deliver a patient’s health condition before the victim reaches the hospital in this project. Here we use some biomedical sensors like a heartbeat sensor, temperature sensor, and a respiratory sensor to check the patient health status. There will be a continuous update to the hospital about the patient’s condition through the cloud with the help of the internet of things. The hospitals can also track the ambulance’s live location through the GPS placed in the ambulance where it arrives, and they can know at what time the patient reaches the hospital. With this information, if the patient is in critical condition, the hospital staff can make all the earlier arrangements before the patient arrives at the hospital and saves their lives as soon as possible. Here we use the biometric sensor to know the patient’s information by scanning the patient’s fingerprint. The stored database obtains this information. In cases of accident situations, to avoid legal problems, the patient’s information is sent to the cops through the GSM, and it is also intimated to the patient’s relatives as soon as possible. The parameters which are measured by using biomedical sensors are viewed by doctors using the Blynk app.

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Low-cost BLE based Indoor Localization using RSSI Fingerprinting and Machine Learning

Cited by 38 publications

References 9 publications

Shopping Wagon: A Smart Shopping System Using RFID for Shopping Malls

Shopping Wagon: A Smart Shopping System Using RFID for Shopping Malls

Customer Analysis Using Machine Learning Algorithms: A Case Study Using Banking Consumer Dataset

Smart Ambulance System with Remote Knowledge Communications Through Cloud

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