This paper presents and evaluates a data centric adaptive in-network aggregation algorithm for wireless sensor networks. In-Network data aggregation is used in wireless sensor networks to reduce the power consumption of sensor nodes. The accuracy of the aggregated results is highly sensitive to delays in the measurements. All existing methods use fixed time limit to accept delayed information for aggregation. The proposed method dynamically calculates the delay limit by using the historical behavior of each sensor. The presented simulation results illustrate the advantage of the developed algorithm.

Localizing sensor nodes is critical in the context of wireless sensor networks applications. It has been shown that, for certain applications, low overhead discrete localization achieves comparable results to costly fine localization. This paper presents a discrete and probabilistic localization method that requires no transmission overhead from the sensor nodes. The method is based on Kalman estimation that iteratively predicts and updates the position of a node with respect to a mobile reference. Simulations show that the method converges to the true position in a relatively short time with an average location accuracy of 91.1%.