Research On Energy Efficient Data Aggregation In Wireless Sensor Networks

As the sensor nodes in Wireless sensor networks are typically limited in storage capability and energy supply, it is a major consideration and challenge for designing a data-collection strategy of energy efficient under the resource conservation circumstance.The traditional statistical aggregates such as Sum, Max, and Averages over the readings of a group of sensor nodes are always finished in sink nodes or the base station centralized. In this way, each node needs to transmit a large number of original data to sink node, which will make the intermediate node spend a lot of energy in sending the repeated or meaningless data. Afterwards, many studies just let the intermediate node to deal with the aggregation subsequently, which means pre-processing the data that collected from sensor nodes while routing the data. However, which will finally cause the problem of over-counting sensor readings. So many researchers propose duplicate-insensitive synopsis that can avoid over-counting sensor readings together with the multipath-based aggregation, however, each has its’problem, such as the produced synopses is with linear space and is not efficient to be used in computing large aggregates or provides with inaccurate estimates and needs more space to fix the inaccurate. In order to overcoming these shortages, we propose an energy efficient scheme together with its optimized scheme respectively named Fan Aggregation (FA) and Adaptive Fan Aggregation(AFA).To solving the problem of over-counting the sensor readings upon multipath aggregation computing, the approaches as FA, AFA are proposed. The main idea is designing a duplicate-insensitive synopsis and further optimizes the synopsis with the specific property as suppression operation. Besides, we have an accuracy guarantee for an aggregate result.FA techniques produce a duplicate-insensitive synopsis named FA synopsis for representing the node readings and aggregation values, besides, use the FA-estimator for estimating the final aggregation results. It has less space and is more accurate than the FM and LC techniques. By further enhancing the suppression operation during the aggregation, AFA technique can send less data information which behavior with more energy-efficient.Finally, theory analysis and simulation results show that FA and AFA technique performance better both in storage space and accuracy than FM and LC.