An Efficient and Scalable Approach to Visualizing Sensor Networks

Sensor networks offer the potential to provide timely information in an untethered and unattended real-world domain. Dynamic (real-time) visualization of sensor network data is needed in order to fully harvest that potential, allowing the users to see the patterns and trends – both spatial and temporal – inherent in the sensor data. Such visualization is the goal of this paper. Our approach to visualization is based on sensor terrains, triangulated irregular networks where the (x; y) coordinates of the vertices corresponds to sensor locations, and the z coordinate corresponds to sensor readings. We dynamically maintain such a sensor terrain for our sensor network. Furthermore, we dynamically maintain an isoline (contour) map over this sensor network. The user has the option of continuously viewing either the current shaded triangulation, or the current isoline map, or an overlay of both. For large sensor networks, we assume that complete recomputation of either the sensor terrain or the isoline map at every epoch is impractical. If n is the number of sensors in the network, time complexity show be O(log n) to achieve real-time performance. To achieve this time complexity, our algorithms are based on efficient dynamic data structures that are continuously updated rather than recomputed. Specifically, we use a doubly-balanced interval tree, where both the tree and the edge sets of each node are balanced. Experimental results confirm both the efficiency and the scalability of our approach.