AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A distributed event detection scheme for wireless sensor networks
Proceedings of the 7th International Conference on Advances in Mobile Computing and Multimedia
Wireless, collaborative virtual sensors for thermal comfort
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Event recognition via energy efficient voting for wireless sensor networks
ruSMART/NEW2AN'10 Proceedings of the Third conference on Smart Spaces and next generation wired, and 10th international conference on Wireless networking
| Hi-index | 0.00 |
Most of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, several algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and will meet the requirements for sensor networks like: simple parallel-distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Lower communication costs and energy savings can be obtained as a consequence of the dimensionality reduction achieved by the neural-networks clustering algorithms. In this paper we will present three possible implementations of the ART and FuzzyART neural-networks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of deliberately made faulty sensors show the data robustness of these architectures.