Sampling Based (epsilon, delta)-Approximate Aggregation Algorithm in Sensor Networks

Aggregation operations are important for users to get summarization information in WSN applications. As large numbers of applications only require approximate aggregation results rather than the exact ones, some approximate aggregation algorithms are proposed to save energy. However, the error bounds of these algorithms are fixed and it is impossible to adjust their error bounds automatically. Therefore, these algorithms cannot reach arbitrary precision requirement given by user. This paper proposes a sampling based approximate aggregation algorithm to satisfy the requirement of arbitrary precision. Besides, two sample data adaptive algorithms are also provided. One is to adapt the sample with the varying of precision requirement. The other is to adapt the sample with the varying of the sensed data in networks. The theoretical analysis and experiment results show that the proposed algorithms have high performance in terms of accuracy and energy cost.