ISSN (e): 2250 – 3005 || Volume, 06 || Issue, 05||May – 2016 || International Journal of Computational Engineering Research (IJCER) www.ijceronline.com Open Access Journal Page 65 Database Management in Different Applications of IOT Shona M Assistant professor, Department of CSE,SVCE, Bangalore, India I. INTRODUCTION A network comprised of physical objects capable of gathering and sharing electronic information. The Internet of Things includes a wide variety of “smart” devices, from industrial machines that transmit data about the production process to sensors that track information about the human body. Often, these devices use Internet Protocol (IP), the same protocol that identifies computers over the World Wide Web and allows them to communicate with one another. The connection of physical things to the Internet makes it possible to access remote sensor data and to control the physical world from a distance. All of these things are creating a “perfect storm” for the IoT. It is estimated that by 2020 there will be over 25 billion devices wirelessly connected to the Internet of Things, including embedded and wearable computing devices. At the same time, IOT imposes fewer data quality and integrity constraints. Although IoT sensors generate data rapidly, they do not entail the same kinds of transactions one finds in traditional enterprise business applications. Relational databases (RDBs) work well for many scenarios, but this is not one of them. RDBs were designed for processing structured, highly uniform data sets, but with IOT, gathered data is nothing close to uniform. With over 50 billion objects predicted to be connected to a single network by 2020, the variety of transmitted data already ranges from simple text to a complex combination of information from different sensors. Not only does information need to be transmitted, but it also must be analyzed and calculated. Basically, necessary requirements can be divided into three categories: Diversity of information and sensors – More and more heterogeneous data is generated by an exponentially growing number of diverse sensors and devices. In addition, new sources of data are constantly added, and the structure and scale of that data is always different and complex. Extensive and flexible systems – The systems that are ruling the Internet of Things have to be flexible and agile so there won’t be need to rebuild an application when new sensors and devices are being added. Proficient analytics – Previously, simpler systems communicated using alerts and notifications, where information had to be transmitted between two machines, but in the Internet of Things, analytics is the foundation of the system. And for different types of data, there are different analytical mechanisms. In the Internet of Things, data analysis will require multiple analytical approaches, and, in some cases, significant value is achieved from real-time data analytics or from historical analysis for predictive maintenance services.As we now know, the IoT generates essentially more data to be stored and processed, and even cloud solutions typically don’t meet sufficient capacity. This is happening due to the nature of cloud-based services: they usually store all data on a single server, while IOT requires more distributed databases. II. REQUIREMENTS OF IOT The requirements of IOT fall into three general categories and virtually all applications will require that at least two are satisfied by your database platform simultaneously: 1. Continuous machine-scale ingestion, indexing, and storage. A modest data source may generate millions of complex records per second on a continuous basis. ABSTRACT In the recent years, the Internet of Things (IoT) is considered as a part of the Internet of future and makes it possible for connecting various smart objects together through the Internet. The use of IoT technology in applications has spurred the increase of real-time data, which makes the information storage and accessing more difficult and challenging. This paper discusses the different Databases used for different applications in IOT. Keywords : NoSQL, Hadoop, Time Series Daemon, (TSD), MemSQL, MongoDB