At the last two decades the information world and associated information technologies have been progressed. This progress can be implemented from the concept of the internet to the internet of things. The Internet of things (IoT) refers to billions of electronic devices around the world that are lınk up to each other’s by using wireless communication networks techniques, such as Bluetooth, ZigBee, Wi-Fi, 2G, 3G, 4G mobile protocol and for example about (IoT) Smart infrastructure, Smart city, Smart Mobility, Smart technology, etc... By using like these smart systems will generate a huge of data sensing that captures from the physical world by sensors and will need to store and process in the centralized process (cloud computing) at less time in order to get faster feedback data response. Today the data analytics, which utilized on edge cloud computing has more and more important in order to get near real-time decisions. The Edge cloud computing is a new paradigm from a cloud computing environment which provides support the data distributed processing that store and process the data sensing in the node to stay close from data produced resources instead of sending it to centralized processing (cloud computing) to minimize the data latency and investigate a near real-time response. Because of limitation, storage on edge cloud computing, I am facing challenges to balance between the quality of data and the quantity of data stored on edge computing for taking near real-time decisions. In this thesis, I use three architectural layers for efficient data storage and management of edge cloud computing that include an adaptive algorithm that dynamically finds a trade-off between providing high prediction accuracy necessary to improve a real-time decision and decreasing the amount of data stored in limited space storage. The aim of this thesis focuses on time series data to get a near real-time decision.

Keywords - Internet of Thing, Cloud Computing, Edge Cloud Computing, Intelligent home system, Adaptive algorithm

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