CLUSTER BASED AGGREGATION FOR REDUNDANCY ELIMINATION IN AGRICULTURAL SYSTEMS

In a Wireless Sensor Network (WSN) with a large number of nodes, the amount of data sensed by the sensor nodes is very high. In this collection of sensed data, there is a large amount of redundancy. Redundant data can decrease the performance of the network in terms of energy consumption, bandwidth and computing overhead due to the limited energy and computational resources of the WSN. Improving the WSN's energy efficiency is crucial in order to have a better lifetime. The purpose of this work is to eliminate the redundant data in a WSN in order to cut down its energy consumption. The strategy adopted for the redundancy elimination process is ‘Cluster based Data aggregation approach to eliminate temporal and spatial redundancy'. In this method, a pattern-based approach is used where each data gets mapped to a pattern code based on a predefined lookup table. The network forms clusters and the cluster members send the pattern code to the Cluster heads (CH) after eliminating temporal redundancy in consecutive iterations. The CHs forward the dataof these nodes to the Base station (BS) after performing spatial redundancy elimination. Thus, redundancy within the same cluster is avoided during all the iterations. It is proved that the energy consumption can be reduced by up to 42% by employing redundancy elimination in comparison with the same network that doesn't eliminate redundancy. This algorithm is then put to use in a moisture detection system employed in agriculture.