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In multipath routing of wireless sensor network (WSN), greedy path selection is one of multipath routing schemes to choose optimal paths on demand to transmit traffic, but the greedy selection is always prone to cause path oscillation (frequent path changes) between each source-destination pair. To alleviate the side effect, we propose an adaptive path selection model (called WARAS) inspired by metabolism behaviors of Escherichia Coli (E. coli). The model consists of two main features. The first one is a redefined parameter called path-activity to indicate adaptation goodness of multipath traffic transmission to real-time network environments, which is inversely proportional to absolute difference between current path quality and best path quality. The second one is a novel attractor expression for each attractor of multi-attractor equations redesigned by us to concretely specify stochastic effect of noise on the path selection. Finally, by putting forward a path quality probe scheme in a multipath AODV protocol, we validate our model can perform better on reducing average network delay and path oscillation than the greedy path selection.

For faster timing closure, a parasitic extraction method is developed for the pre-route VLSI design. This method generates virtual route and estimates congestion using the placement information of standard cells, and then extract the interconnect parasitics with the pattern-library method. The techniques of generating parasitic RC tree according to the improved FLUTE algorithm, and capacitance extraction of route segment considering congestion are presented. Experiments are carried out on industrial design cases, whose results show that the proposed method has high computational speed and comparable accuracy as commercial tool.