Name
Abstract | ||
|---|---|---|
Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2938639 | ACM Comput. Surv. |
Keywords | Field | DocType |
Sensor Networks,Distributed Algorithms,Data Fusion,Target tracking,particle filter,Kalman filter | Data mining,Energy management,Computer science,Particle filter,Sensor fusion,Kalman filter,Structure (mathematical logic),Wireless sensor network,Trajectory,Computation | Journal |
Volume | Issue | ISSN |
49 | 2 | 0360-0300 |
Citations | PageRank | References |
8 | 0.53 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Efren Lopes Souza | 1 | 24 | 3.00 |
| Eduardo Freire Nakamura | 2 | 320 | 31.97 |
| Richard Werner Nelem Pazzi | 3 | 12 | 1.95 |