WARAS: An adaptive WSN multipath selection model inspired by metabolism behaviors of Escherichia Coli

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.