Dynamic Distributed Power Adaption for Data Aggregation in Ubiquitous Sensor Networks

Abstract

Data Aggregation Scheduling in Ubiquitous sensor networks is a major research interest for many researchers with the objective of minimizing the energy consumption. Very little research is carried to schedule the nodes in ubiquitous sensor networks to reduce energy consumption, maximize the throughput, for effective prolonged network lifetime, scalability, and load balancing. Each ubiquitous node in USN should forward data to the base station. In Data Aggregation methods each node need not directly forward data to the base station instead it may forward data to a special node called head node which in turn forwards data to the base station. In this paper we proposed a new network model for the USN which is power control and collision interference free model. We also proposed an energy-efficient distributed data aggregation scheduling protocol, called DDPA (Dynamic Distributed Power Adaptive), which is adaptive to rate and power. Using the proposed optimal node degree algorithm, we obtained a full reachability network with 99.9% of energy savings even at the denser network conditions.