A generic internet of things architecture for controlling electrical energy consumption in smart homes

The Internet of Things (IoT) is progressively developing since it was first introduced at the beginning of the 21 st century. The IoT becomes more prominent due to the enormous data generated by the IoT devices called the Big Data. Further, the application of Big Data is widely adopted in many areas such as smart home and city planning, efficient system design, etc. However, during the developing stages of IoT, the researchers have many challenges that need to be addressed before standardizing IoT for general use. These challenges include co-existences of many communication technologies such as Bluetooth, ZigBee, WIFI, and so on. The effect of such technologies on the communication becomes more when these technologies exist in shot communication range. Similarly, other challenges include processing of huge amount of data generated by the IoT devices in real-time. Therefore, in order to address these challenges, we come up with a proposed scheme that enable a generic communication architecture among the IoT devices with less interference. Further, the proposed architecture consists of four main steps i.e. 1) a system to discover and identify electrical appliances in a smart home or smart building, 2) deployment of sensors, 3) applying proposed load balancing on appliances and sensors, and 4) processing the data obtained from these sensors for better usage of home and electrical appliances. The proposed scheme is tested on real electronic appliances and the energy consumption is recorded using the proposed Electronic Device Sleep Scheduling Algorithm (EDSA). Furthermore, the EDSA is responsible for controlling the activities of the sensors while it is active, sleep, and idle modes. The results show that the proposed architecture perform better in a heterogeneous environment compare to simple Wireless Sensor Network (WSN) based technologies. The data is also processed using Hadoop Ecosystem is to maximize the efficiency and minimize the time required to process the data in real-time.