A Novel Power-Efficient Data Aggregation Scheme for Cloud-Based Sensor Networks

Abstract

Nowadays sensor nodes are being deployed anywhere as per the applications and real-time data analysis. A major concern of this implementation is the limited battery power and huge data generation. The data redundancy can also be a cause of battery decay. This scheme spends the energy based on priority. This method also uses a mobile agent for the data collection from the sensor nodes, when it is combined with optimal cluster head along with marking of subtle aggregators gives a satisfying performance. This approach is divided into three phases as clustering of sensor nodes then computing PEDAS and finally deploy a mobile agent. Our approach of PEDAS measure parameters in an optimized manner which develops an energy-efficient system and only spends the energy at the moment when it is needed the most. The proposed model was simulated and verified using network-simulator 3. Implementation and analysis of the algorithm prove that this research study has improved the lifetime of the entire network and also provide a stable and robust network while comparing it with EEDAC and ATL scheme.