In this paper, we propose an effective filtering method for skyline queries in mobile ad hoc networks (MANETs). Most existing researches assume that data is uniformly distributed. Under these assumptions, the previous works focus on optimizing the energy consumption due to the limited battery power. However, in practice, data distribution is skewed in a specific region.In order to reduce the energy consumption, we propose a new filtering method considering the data distribution. We verify the performance of the proposed method through a comparative experiment with an existing method. The results of the experiment confirm that the proposed method reduces the communication overhead and execution time compared to an existing method.

In sensor network environments, each sensor measures the physical environments according to the sampling period, and transmits a sensor reading to the base station. Thus, the sample period influences against importance resources such as a network bandwidth, and a battery power. In this paper, we propose new adaptive sampling technique that adjusts the sampling period of a sensor with respect to the features of sensor readings. The proposed technique predicts a future readings based on KF (Kalman Fiter). By using the differences of actual readings and estimated reading, we identify the importance of sensor readings, and then, we adjust the sampling period according to the importance. In our experiments, we demonstrate the effectiveness of our technique.