International Journal of Advanced Technology and Engineering Exploration, Vol 6(61) ISSN (Print): 2394-5443 ISSN (Online): 2394-7454 http://dx.doi.org/10.19101/IJATEE.2019.650078 267 An efficient framework for the automatic and dynamic load distribution in IOT Vijaita Kashyap* and Dimple Kapoor Assistant Professor, Department of Computer Science and Engineering, Chitkara University, Rajpura, Punjab Received: 20-October-2019; Revised: 26-December-2019; Accepted: 28-December-2019 ©2019 Vijaita Kashyap and Dimple Kapoor. This is an open access article distributed under the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.Introduction In today’s world data sharing and communication is very important, especially with the smart devices and grids. Internet of Things (IoT) provides a communication network [1]. The device connection and interconnection depend on the object availability and the connection enhancements applicability [2]. It also provides interconnection between different devices for data sharing through the web environment. The role of IoT is mainly important in the area where the data and resource distribution and communication between sensor, smart devices and smart grids [3−5]. The coverage area is also very wide in case of IoT devices which includes healthcare industry, visualization area, smart monitoring system etc. [5]. The devices used in the IoT are generally supports the heterogeneity with other smart devices. So, data sensitivity is also a crucial aspect in these devices. Data sensitivity can be handled in the way suggested in [6]. For the message transmission and control, there are several protocols which can be used and different objects in terms of communication devices have been used. The research applicability can be extended with data mining, Big Data and cloud computing techniques, etc. [7, 8]. *Author for correspondence If we think about the smart grid mechanism in IoT then in general it is a part of the complete IoT framework. It has been used for the remote monitoring and management [9]. It is helpful in different areas including the congestion control in traffic and for warning systems [9]. In [10] authors have suggested the smart grids have been in demand due to the traditional grid’s drawbacks like energy demand, wastage and security issues. These are the factors which arise the need of smart grid framework. The major aspects covered in the case of smart grids are the network for the data communication [11]. It provides the integration of the data analysis, covering and acquiring the transmission lines and distribution substations [11]. It also provides the full integration of the power grid. The main work is the data collection and analysis [11]. The components used are in the area suggested and discussed above covers the need of the current scenario also. It also shows that the devices have been increased with the connected devices in terms of positive relationship [12]. It also reveals the increasing impact of smart devices and grids in the IoT framework [13]. The main objectives concentrating the limitations are as follows: Research Article Abstract The current era is observing the need of communication among different smart devices in the collaboration of Internet of Things (IoT). Smart device integration along with the load distribution is capable in controlling energy resources with the cost benefits. So, in this paper an efficient framework for the automatic and dynamic load distribution in IOT with smart grids mechanism has been presented. Our efficient dynamic load balancing framework has three phases. First shows the pre-processing, second phase shows the IoT distribution and communication procedure. Final phase is the object interconnection phase with grids. For the evaluation of our framework scaling mechanism has been adopted for testing of load clusters. The results indicate that it is capable in energy resource saving as it found to be uniform. Keywords IoT, Data mining, Big data, Cloud computing, Computation capability.