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
Programming a paintable computer
Programming a paintable computer
Proceedings of the 2008 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
Linda in space-time: an adaptive coordination model for mobile ad-hoc environments
COORDINATION'12 Proceedings of the 14th international conference on Coordination Models and Languages
Description and composition of bio-inspired design patterns: a complete overview
Natural Computing: an international journal
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Self-healing gradients are distributed estimates of the distance from each device in a network to the nearest device designated as a source, and are used in many pervasive computing systems. With previous self-healing gradient algorithms, even the smallest changes in the source or network can produce small estimate changes throughout the network, leading to high communication and energy costs. We observe, however, that in many applications, such as routing and geometric restriction of processes, devices far from the source need only coarse estimates, and that a device need not communicate when its estimate does not change. We have therefore developed Flex-Gradient, a new self-healing gradient algorithm with a tunable trade-off between precision and communication cost. When distance is estimated using Flex-Gradient, the constraints between neighboring devices are flexible, allowing estimates to vary by an amount proportional to a device's distance to the source. Frequent small changes in the network or source thus cause frequent estimate changes only within a distance proportional to the magnitude of the change, as verified in simulation on a network of 1000 devices. This can enable drastic reductions in the communication and energy cost of gradient-based algorithms.