Deferred decentralized movement pattern mining for geosensor networks |
| |
| Authors: | Patrick Laube Matt Duckham Marimuthu Palaniswami |
| |
| Institution: | 1. Department of Geomatics , The University of Melbourne, Parkville , Melbourne, Victoria, Australia patrick.laube@geo.uzh.ch;3. Department of Geomatics , The University of Melbourne, Parkville , Melbourne, Victoria, Australia;4. Department of Electrical and Electronic Engineering , The University of Melbourne, Parkville , Melbourne, Victoria, Australia |
| |
| Abstract: | This article presents an algorithm for decentralized (in-network) data mining of the movement pattern flock among mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to ‘graze’ over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge. |
| |
| Keywords: | geosensor networks decentralized spatial computing trajectory data mining movement patterns flocking |
|
|
