Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Next century challenges: mobile networking for “Smart Dust”
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Ant algorithms for discrete optimization
Artificial Life
Swarm intelligence
Proceedings of the 10th annual international conference on Mobile computing and networking
A biologically-inspired clustering protocol for wireless sensor networks
Computer Communications
ATC '09 Proceedings of the 6th International Conference on Autonomic and Trusted Computing
Dual-swarm features and its challenges for system of sensor networks and mobile multi-robots
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
Swarm behavior control of mobile multi-robots with wireless sensor networks
Journal of Network and Computer Applications
MoBAN: a configurable mobility model for wireless body area networks
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Robotics software frameworks for multi-agent robotic systems development
Robotics and Autonomous Systems
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Swarm behavior as demonstrated by flocks of birds, schools of fish, and swarms of insects provide a useful method for implementing a distributed network of mobile sensor platforms. Such mobile sensor swarm systems are useful for various search or surveillance activities. Swarm behavior ensures safe separation between swarm members while enforcing a level of cohesion. These two properties, when considered in the context of sensors and wireless communications, provide for low redundancy coverage and a robust and reliable communications system. This paper examines particle swarm behavior through simulation with respect to such a sensor network. Analysis of swarm behavior for various parameter settings indicate a classification methodology. This provides a foundation for a proposed taxonomy.