Directed diffusion: a scalable and robust communication paradigm for sensor networks
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
The nesC language: A holistic approach to networked embedded systems
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Programming a paintable computer
Programming a paintable computer
Infrastructure for Engineered Emergence on Sensor/Actuator Networks
IEEE Intelligent Systems
Programming an amorphous computational medium
UPP'04 Proceedings of the 2004 international conference on Unconventional Programming Paradigms
Flexible self-healing gradients
Proceedings of the 2009 ACM symposium on Applied Computing
Fast Self-stabilization for Gradients
DCOSS '09 Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems
Spatial Coordination of Pervasive Services through Chemical-Inspired Tuple Spaces
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Core operational semantics of Proto
Proceedings of the 2011 ACM Symposium on Applied Computing
Description and composition of bio-inspired design patterns: the gradient case
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
An agent framework for agent societies
Proceedings of the compilation of the co-located workshops on DSM'11, TMC'11, AGERE!'11, AOOPES'11, NEAT'11, & VMIL'11
Description and composition of bio-inspired design patterns: a complete overview
Natural Computing: an international journal
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We present CRF-Gradient, a self-healing gradient algorithm that provably reconfigures in O(diameter) time. Self-healing gradients are a frequently used building block for distributed self-healing systems, but previous algorithms either have a healing rate limited by the shortest link in the network or must rebuild invalid regions from scratch. We have verified CRF-Gradient in simulation and on a network of Mica2 motes. Our approach can also be generalized and applied to create other self-healing calculations, such as cumulative probability fields.