In-network data aggregation with temporal and spatial correlation

Wireless Sensor Networks (WSN) have a large number of nodes that require a large amount of energy for their operation. Therefore, saving and using energy is the main constraint in wireless sensor networks. The data that is captured by the sensor nodes is from the environment of the same wireless sensor network. The data measured can be a measurement of temperature or pressure depending on the functionality of the sensor node. We use a data aggregation technique to compute this data. It greatly helps in making the system energy efficient and also cost effective. Spatial and temporal correlation also help to enhance the performance of a WSN. The technique that we are using here is Data Routing for In-Network Aggregation (DRINA). Since there are many nodes in a wireless sensor network there is a possibility that these nodes detect unwanted data, which is a waste of energy. This technique can help in avoiding these unwanted data, and in making the system energy efficient and cost effective. The main aspects of the DRINA are that it uses a minimum number of messages to set up the routing tree, it increases the number of overlapping routes and it provides high aggregation rate and robust transmission rates.