In multipath routing of wireless sensor network (WSN), greedy path selection is always prone to cause path oscillation (frequent path changes) between each couple of sensor and sink nodes. To alleviate the side effect, we propose an adaptive path selection model (called a WSN path selection model based on the adaptive response by attractor selection (ARAS) model (WARAS)) inspired by metabolism behaviors of Escherichia Coli. The model consists of two main features. The first one is a new formula for a parameter called path-activity used to indicate adaptation goodness of multipath traffic transmission in dynamic network environments, which is inversely proportional to absolute value of difference between current path quality and best path quality. The second one is a novel attractor expression for attractors of multi-attractor equations to concretely specify stochastic effect of noise items in the equations on the path selection. Then, in an experimental WSN scenario composed of many source nodes and their shared neighbor nodes, we validate a dynamic-adaptive selection characteristic of the WARAS on distributing loads of the neighbor nodes. Subsequently, we design a path quality probe scheme in a multipath ad hoc on-demand distance vector routing (AODV) protocol. Compared with the greedy path selection through the path quality probe scheme, simulation results show the WARAS can perform better on reducing network delay and the path oscillation.
Current Internet Protocol routers only support equal cost multi-path routing, which performs the random path selection or the traffic uniform distribution among equal-cost paths. In biology, an adaptive attractor selection model is presented to simulate the concentration changes of two kinds of Escherichia coli's mRNA in changing nutrition environments with bistability equations. Inspired by the metabolism behaviors of E. coli, we propose an adaptive path selection scheme Open Shortest Path First-path selection by attractor selection to dynamically select the transmission path by the real-time path quality. Here, the mRNA concentration is analogous to the path quality. Then, to reflect the multipath quality, multi-stability equations are adopted and redesigned. Our scheme consists of two main features. The first one is a redefined path-activity to indicate multipath transmission goodness, which is inversely proportional to the offset between current path quality and best path quality. And the second one is a new attractor expression of the multi-stability equations to concretely specify the effect of a stochastic item noise in the equations on the path selection. Compared with the greedy selection and the uniform random selection in file transfer protocol (FTP) service, our scheme gains better performance on reducing file transmission time, traffic throughput, and traffic dropped.the adaptive attractor selection model [8] is introduced into the field of the computer networks to optimize the multipath traffic transmission.Originally, the attractor selection model [9] is proposed to simulate the metabolism behaviors of the Escherichia coli in the dynamic nutrition environments with the revised bistability equations. In nature, each individual E. coli always regulates adaptively its own state in the different nutrition environments. When nutrition is adequate, the metabolism of the E. coli becomes vigorous generally. Then, the mRNA concentrations in the E. coli greatly increase to produce enough nutrition materials for growth and reproduction. Furthermore, the vigorous state will be kept if nutrition is adequate. Subsequently, when nutrition is inadequate, the metabolism of the E. coli is weak. Then, the mRNA concentrations inevitably decrease. Moreover, the concentrations are able to change stochastically to make the E. coli shift to a suitable metabolism state in the current nutrition environment [9]. Mathematically, this adaptation process of the E. coli to the changing nutrition environments can be modeled by the bistability equations, in which each attractor formulates a stable adaptation state of the mRNA concentrations.There are two key parameters in the bistability equations. One is called activity, which indicates the adaptation goodness of the E. coli to the nutrition environment. And the other one is noise, which takes the stochastic effect on the mRNA concentrations. The two parameters have a relation of ebb and flow. When the activity increases, the stochastic effect of the noise on the mRNA concentrati...
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 pathactivity 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.
The signal quality degradation, which occurs due to the aspects of fiber attenuation, splitter switch, and amplified spontaneous noise in erbium-doped fiber amplifier, may substantially erode the performance of optical communications. Among the above-mentioned factors, the physical-layer impairments may significantly weaken the signal quality at the receiver. In this paper, the impact of both the channel power adjustment and the optical signal noise rate on the optimization of optical amplifiers over the optical propagation links is studied. Our objective is to employ a proper control strategy to effectively adjust the signal power level in the chain network. A new algorithm, which takes into account the physical-layer impairments in performance optimization, facilitates an impairment aware-proportional-integral-derivative neuron controller for improving the quality of transmission. Numerical results show that the proposed controller is capable of effectively compensating for the power impairments in optical links.
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