Sensor networks represent an important component of distributed infrastructure supplying raw data to various applications from military to healthcare. A key challenge is costefficient collection of distributed data streaming from those sensor networks. In this paper we propose the use of mobile data collectors that employ K-NN queries as a cost-efficient approach to collect data within the sensor network. We investigate a 3D sensor network and propose a cost-efficient 3D-KNN algorithm that uses minimal energy and communication overheads to compute k-nearest neighbors. The 3D-KNN algorithm uses a 3 dimensional plane rotation algorithm that maps sensor nodes on a 3D plane to a reference plane identified by the mobile data collector. We propose a cost-efficient KNN boundary estimation algorithm that computes KNN boundary based on network density. We also propose a neighbor prediction algorithm that uses distance, signal to noise ratio and mobile data collector's trajectory information to identify sensor nodes along the mobile data collector's path. We simulate the proposed 3D-KNN algorithm using GlomoSim and validate its cost efficiency by evaluating its energy efficiency and query latency. Lessons and results of extensive simulation conclude the paper