International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 9, Issue 3, March 2020, ISSN: 2278 – 1323 All Rights Reserved © 2020 IJARCET 103 Survey on Data Management in IoT using Machine Learning Algorithms T.Kalai Selvi, R.Karunamoorthi, R.Narendran, Dr.G.Saravanan Abstract - The Internet of Things (IoT) is a network of items or devices that are connected to the Internet, usually via sensors, and can relate to each other and the data they generate. These connected “things” ranging from smart phones and cars to refrigerators, thermostats, and mirror are gradually incoming every part of our lives. With 41.6 billion connected devices expected by 2025, IoT’s stability is only going to increase. In this paper, we have presented the survey about several papers on the Data Management in IoT using Machine Learning Algorithms. Index Terms- Data Management, IOT Applications, Internet of Things (IoT), Machine Learning Algorithms, I. INTRODUCTION IoT implementation has increased significantly in the last five years due to the accessibility of massive computing power, innovations in data-processing technology, and the initiation of machine learning and natural-language processing algorithms. IoT has opened an entirely new arena for customers to address their long-standing issue of connecting devices and using the resulting data to positively control decision-making process. IoT also opens an entirely new variety of use cases where customers can operationalize actions on the IoT devices in real-time something that was not possible a few years ago. IoT data management helps organization be aware of how environmental conditions and user activities can affect the performance of their products. IoT sensors can also be used to evaluate manufactured goods performance metrics. The data collected by these sensors can be used to improve future versions of products. Manuscript received March, 2020. T.Kalai Selvi, Assistant Professor (SLG-I), Dept. of CSE, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 9842899992, tkalaiselvi1281@gmail.com. R.Karunamoorthi, Assistant Professor, Dept. of CSE, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 9025044806, karunamoorthir@gmail.com. R.Narendran, Assistant Professor, Dept. of CSE, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 9944519941, narenofficial124@gmail.com. Dr.G.Saravanan, Assistant Professor, Dept. of CSE, Erode Sengunthar Engineering College, Erode, Tamil Nadu, 9894379369, gsaravanan.esec @gmail.com. II. IMPLICATION - IOT Significance of IoT Data Management The following are the importance of Data Management in IoT:  How to get back the data from the IoT systems and make it accessible for the analytics systems and for conclusion.  The capability to consume the data from IoT systems into the data lake. In most scenarios, organization also wants to improve and cleanse the data and the analysts have enriched data for their analytics. Prospective for IoT data management Managing data from IoT devices is an important feature of a real-time analytics. The following key ability are needed to handle IoT data demands: 1. Multitalented connectivity and facility to handle data range IoT systems have a variety of standards and IoT data hold on to a wide range of protocols (MQTT, OPC, AMQP, and so on). Also, most IoT data exists in semi- structured. Therefore, data management system must be able to connect to all of those systems and stay to the various protocols. It is essential that the solution to carry both structured and unstructured data. 2. Edge processing and improvements A good data management resolution will be able to riddle out flawed records coming from the IoT systems. It should also be able to improve the data with metadata. 3. Big data processing and machine learning IoT data becomes very large volumes which require the ability to run enrichments so that the data is ready to be consumed in real time. Also, many customers want to put into use ML models so that they can take preventive steps. 4. Concentrate on data flow