Attractor Selection Model Research Articles

Field Evaluation of Adaptive Path Selection for Platoon-Based V2N Communications

Vehicles on the road are expected to connect continuously to the Internet at sufficiently high speeds, e.g., several Mbps or higher, to support multimedia applications. However, even when passing through a well-facilitated city area, Internet access can be unreliable and even disconnected if the travel speed is high. We therefore propose a network path selection technique to meet network throughput requirements. The proposed technique is based on the attractor selection model and enables vehicles to switch the path from a route connecting directly to a cellular network to a relay type through neighboring vehicles for Internet access. We also develop a mechanism that prevents frequent path switching when the performance of all available paths does not meet the requirements. We conduct field evaluations by platooning two vehicles in a real-world driving environment and confirm that the proposed technique maintains the required throughput of up to 7 Mbps on average. We also evaluated our proposed technique by extensive computer simulations of up to 6 vehicles in a platoon. The results show that increasing platoon length yields a greater improvement in throughput, and the mechanism we developed decreases the rate of path switching by up to 25%.

A Bio-Inspired Gateway Selection Scheme for Hybrid Mobile Ad Hoc Networks

The gateway selection is an essential issue in hybrid mobile ad hoc networks (MANETs). Current independent and random selections without supporting routing negotiation protocol may cause links or gateways overloaded when they are selected by multiple nodes simultaneously. Therefore, the network performance may not be in a proper way. Furthermore, the ad hoc nature of MANETs makes the topology change dynamically, so that the gateway selection becomes even more difficult. This paper presents a mathematical model for gateway capability and a novel approach, called bio-inspired gateway selection, where the gateways are selected according to the network status and associated with a cooperative mechanism for optimization. The novelty includes the use of attractor selection model, the self-adaptability and the autonomy of the biological system. The performance of the proposed approach is evaluated by simulation with different scenarios and compared with the conventional approaches being currently used in hybrid MANETs. The illustrated numerical results present the performance of the proposed approach in terms of packet delivery ratio, average delivery latency, normalized routing overhead, and gateway load balance under different network conditions. Furthermore, the numerical results are also able to demonstrate the significant performance gain compared with the conventional gateway selection approaches.

Noise-induced VNE method for software-defined infrastructure with uncertain delay behaviors

Software-defined infrastructure (SDI) provides virtualized infrastructures to customers by slicing computing resources and network resources. One of the important problems for deploying an SDI framework is to control the assignment of physical resources to a virtual network against changes of traffic demand and service demand. For this problem, the virtual network embedding (VNE) problem, which maps a virtual network to physical resources, has been addressed, but a centralized calculation was assumed. It is difficult to adopt the centralized approach as the size of infrastructure increases and the number of VN requests increases because the identification of current demand becomes more complicated. In this paper, we present a VNE method that works with limited information for large, complicated, and uncertain SDI frameworks. To achieve this, our method applies the biological “Yuragi” principle. Yuragi is a Japanese word whose English translation is “a small perturbation to the system.” Yuragi is a mechanism that provides adaptability of organisms and is often expressed as an attractor selection model. This paper develops a Yuragi-based VNE method that deals with node attributes, has the generality to handle a performance objective, and runs in multi-slice environments. Simulation results show that the Yuragi-based method decreases VN migrations by about 29% relative to a heuristic method to adapt to fluctuations in resource requirements.

An adaptive path selection model for WSN multipath routing inspired by metabolism behaviors

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.

An adaptive vehicular epidemic routing method based on attractor selection model

Vehicular ad hoc networks (VANETs) is an emerging technology that can support many vehicular safety and comfort applications through intervehicle communications. However, VANETs could face a great challenge arising from rapidly varying network topology. It is an essential issue to design an efficient and reliable solution for message dissemination in dynamic VANETs. In this paper, we propose an epidemic routing method with capability of self-adapting to the highly dynamic nature of VANETs. To ensure a good trade-off between reachability and efficiency of message dissemination, an adaptive probabilistic infection and an adaptive limited-time forwarding mechanisms are also proposed for the epidemic broadcasting. Moreover, inspired by the self-adaptability and robustness of the cellular gene regulatory networks, we use the attractor selection mechanism to enhance routing messages in dynamic VANETs. Through comparative simulations under different traffic scenarios, we validate our proposed method and prove that it can guarantee message reachability and achieve high delivery efficiency in terms of message delivery ratio, average routing latency and cost.

Goal-directed multimodal locomotion through coupling between mechanical and attractor selection dynamics

One of the most significant challenges in bio-inspired robotics is how to realize and take advantage of multimodal locomotion, which may help robots perform a variety of tasks adaptively in different environments. In order to address the challenge properly, it is important to notice that locomotion dynamics are the result of interactions between a particular internal control structure, the mechanical dynamics and the environment. From this perspective, this paper presents an approach to enable a robot to take advantage of its multiple locomotion modes by coupling the mechanical dynamics of the robot with an internal control structure known as an attractor selection model. The robot used is a curved-beam hopping robot; this robot, despite its simple actuation method, possesses rich and complex mechanical dynamics that are dependent on its interactions with the environment. Through dynamical coupling, we will show how this robot performs goal-directed locomotion by gracefully shifting between different locomotion modes regulated by sensory input, the robot’s mechanical dynamics and an internally generated perturbation. The efficacy of the approach is validated and discussed based on the simulation and on real-world experiments.

A bio‐inspired OSPF path selection scheme based on an adaptive attractor selection model

SummaryCurrent 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. Copyright © 2015 John Wiley & Sons, Ltd.

Adaptive LSH based on the particle swarm method with the attractor selection model for fast approximation of Gaussian process regression

Gaussian process regression (GPR) is one of the non-parametric methods and has been studied in many fields to construct a prediction model for highly non-linear system. It has been difficult to apply it to a real-time task due to its high computational cost but recent high-performance computers and computationally efficient algorithms make it possible. In our previous work, we derived a fast approximation method for GPR using a locality-sensitive hashing (LSH) and product of experts model, but its performance depends on the parameters of the hash functions used in LSH. Hash functions are usually determined randomly. In this research, we propose an optimization method for the parameters of hash functions by referring to a swarm optimization method. The experimental results show that accurate force estimation of an actual robotic arm is achieved with high computational efficiency.

A Distributed Control of Virtual Network Topologies by Using Attractor Selection Model

A Distributed Control of Virtual Network Topologies by Using Attractor Selection Model

A Bio-Inspired QoS-Oriented Handover Model in Heterogeneous Wireless Networks

We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

2A2-E09 人間上肢型ロボティックアームによるアトラクタ選択モデルを用いた円運動の生成

2A2-E09 人間上肢型ロボティックアームによるアトラクタ選択モデルを用いた円運動の生成

1A1-A19 生体ゆらぎに基づくアトラクタ選択モデルを用いた複数ロボット協調制御手法の提案

1A1-A19 生体ゆらぎに基づくアトラクタ選択モデルを用いた複数ロボット協調制御手法の提案

Implementation of Multi-Agent Object Attention System Based on Biologically Inspired Attractor Selection

A multi-agent object attention system is proposed, which is based on biologically inspired attractor selection model. Object attention is facilitated by using a video sequence and a depth map obtained through a compound-eye image sensor TOMBO. Robustness of the multi-agent system over environmental changes is enhanced by utilizing the biological model of adaptive response by attractor selection. To implement the proposed system, an efficient VLSI architecture is employed with reducing enormous computational costs and memory accesses required for depth map processing and multi-agent attractor selection process. According to the FPGA implementation result of the proposed object attention system, which is accomplished by using 7,063 slices, 640×512 pixel input images can be processed in real-time with three agents at a rate of 9 fps in 48 MHz operation.