Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Task differentiation in Polistes wasp colonies: a model for self-organizing groups of robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
The AARIA agent architecture: From manufacturing requirements to agent-based system design
Integrated Computer-Aided Engineering
FPGA framework for agent systems using dynamic partial reconfiguration
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Global peer-to-peer classification in mobile ad-hoc networks: a requirements analysis
CONTEXT'11 Proceedings of the 7th international and interdisciplinary conference on Modeling and using context
Analyzing stigmergic learning for self-organizing mobile ad-hoc networks (MANET's)
Engineering Self-Organising Systems
Decentralised smart grids monitoring by swarm-based semantic sensor data analysis
International Journal of Systems, Control and Communications
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Swarming agents in networks of physically distributed processing nodes may be used for data acquisition, data fusion, and control applications. We present an architecture for active surveillance systems in which simple mobile agents collectively process real-time data from heterogeneous sources at or near the origin of the data. We motivate the system requirements with the needs of a surveillance system for the early detection of large-scale bioterrorist attacks on a civilian population, but the same architecture is applicable to a wide range of other domains.The pattern detection and classification processes executed by the proposed system emerge from the coordinated activities of agents of two populations in a shared computational environment. Detector agents draw each other's attention to significant spatio-temporal patterns in the observed data stream. Classifier agents rank the detected patterns according to their respective criterion. The resulting system-level behavior is adaptive, robust, and scalable.