NG Smartphone Users Activity and Direction Detection using MQTT in IoT

The next generation smartphone user's activity and direction detection system is a major challenging task because analyzing user activity and its detection using the surveillance camera or any other external sensors requires some fixed body which is not so convenient and also it is a tedious, time-consuming task involving high hardware cost. This book presents NG smartphone-based user's activity signals, positions, and directions detection system using a combination of the smartphone user's inbuilt accelerometer, gyroscope, orientation sensor, and BLE, Wi-Fi via MQTT protocol in IoT. To these detection systems, various types of algorithms were applied to evaluate the performance results of the system and on comparison among them, it can be inferred that random forest was found to be the best one in terms of accuracy and low error rate. Experts forecast that by 2025 there will be a total of 75.44 billion devices or things that are connected to the internet. This research has been included in this book to help educational institutes and can be used by IoT vendors and service providers for training their program developers.

Autorentext

D. Kothandaraman a reçu son B.E. en CSE du Dr. PEC (AU), son M.Tech. en IS du PEC (PU (Govt. of India)) et son Ph.D. en CSE du CEG, Guindy, AU (Govt. of TN). Ses intérêts de recherche sont la CN, WSN, MANETs, ML et IoT. Il a publié plusieurs articles de recherche. Il travaille actuellement comme professeur associé dans le département de CSE, S R E C, Warangal, TS, Inde.

Klappentext

The next generation smartphone user's activity and direction detection system is a major challenging task because analyzing user activity and its detection using the surveillance camera or any other external sensors requires some fixed body which is not so convenient and also it is a tedious, time-consuming task involving high hardware cost. This book presents NG smartphone-based user's activity signals, positions, and directions detection system using a combination of the smartphone user's inbuilt accelerometer, gyroscope, orientation sensor, and BLE, Wi-Fi via MQTT protocol in IoT. To these detection systems, various types of algorithms were applied to evaluate the performance results of the system and on comparison among them, it can be inferred that random forest was found to be the best one in terms of accuracy and low error rate. Experts forecast that by 2025 there will be a total of 75.44 billion devices or things that are connected to the internet. This research has been included in this book to help educational institutes and can be used by IoT vendors and service providers for training their program developers.