Self-organizing maps
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Context-Awareness in Wearable and Ubiquitous Computing
ISWC '97 Proceedings of the 1st IEEE International Symposium on Wearable Computers
Modeling Indirect Interaction in Open Computational Systems
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Spray computers: Explorations in self-organization
Pervasive and Mobile Computing
Self-Organizing Sensor Networks for Integrated Target Surveillance
IEEE Transactions on Computers
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
Secure self organization of wireless sensor network: a new approach
COMSNETS'10 Proceedings of the 2nd international conference on COMmunication systems and NETworks
Data aggregation for wireless sensor networks using self-organizing map
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Self-organizing approaches for large-scale spray multiagent systems
Software Engineering for Multi-Agent Systems IV
Information Sciences: an International Journal
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Research in classifying and recognizing complex concepts has been directing its focus increasingly on distributed sensing using a large amount of sensors. The colossal amount of sensor data often obstructs traditional algorithms in centralized approaches, where all sensor data is directed to one central location to be processed. Spreading the processing of sensor data over the network seems to be a promising option, but distributed algorithms are harder to inspect and evaluate. Using self-sufficient sensor boards with short-range wireless communication capabilities, we are exploring approaches to achieve an emerging distributed perception of the sensed environment in real-time through clustering. Experiments in both simulation and real-world platforms indicate that this is a valid methodology, being especially promising for computation on many units with limited resources.