Request Integration and Data Prediction Based Energy Efficient Cloud Integrated Wireless Sensor Network

An energy-efficient cloud-integrated wireless sensor network (WSN) model is proposed based on request integration and data prediction method. Many users want to access the same sensors in the sensor cloud environment through the cloud at the same time. So, to optimize the requests, the Hybrid Request Integration (HRI) algorithm is used where redundant requests are merged and executed once by the cloud to reduce the power consumption within the cloud system. To minimize data communication, the Artificial Neural Network (ANN) based prediction method is used in the cloud, which provides future sensor data for one day in advance with 94% accuracy. The users' requests are generated every second, and in the traditional model, all users' queries need to be redirected to the WSN, which requires more energy. Rather than one second, the cloud transmits and receives data with the sensors every 24 hours in our method. Maximum users' requests are answered by the prediction method in the cloud system, which results in less communication and more battery life for the sensor. In case of alert condition, the sensor sends data to the cloud, and our model can also predict the event and alerts the users