Abstract. In this paper we propose a resilient scheme for multi-path routing using a biologically-inspired attractor selection method. The main advantage of this approach is that it is highly noise-tolerant and capable of operating in a very robust manner under changing environment conditions. We will apply an enhanced attractor selection model to multi-path routing in overlay networks and discuss some general properties of this approach based on numerical simulations. Furthermore, our proposal considers randomization in the path selection which reduces the selfishness and improves the overall network-wide performance.
In the forthcoming future, various means of wireless communication, such as cellular, Wi-Fi, WiMAX, and DSRC, will be available to mobile users and applications. With the development of wireless communication and mobile devices, more and more users and applications will be accommodated in mobile environment. Since mobile users and applications compete for the limited wireless resources whose communication quality dynamically change, we need an adaptive mechanism for mobile users and applications to share the available network resources while satisfying each application's QoS requirements. In this paper, we propose an adaptive resource allocation mechanism where each node autonomously determines wireless network resources to assign to each of networked applications running on it. For this purpose, we adopt an attractor composition model, which is based on an autonomous and adaptive behavior of biological systems. Through numerical analysis, we confirmed that our mechanism could adaptively and stably allocate wireless network resources to applications, while considering their QoS requirements and fairly sharing network resources with other nodes. It also is shown that our mechanism superiors to a mechanism where a node determines resource allocation by solving an optimization problem.
By deploying wireless sensor nodes and composing a sensor network, one can remotely obtain information about the behavior, conditions, and positions of entities in a region. Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable to prolong the lifetime of a sensor network as long as possible. In this paper, we proposed a novel clustering method where energy-efficient clusters are organized in a distributed and self-organizing way through local communication among sensor nodes. Our method is based on an idea of ANTCLUST, a clustering algorithm which applies a colonial closure model of ants. Through simulation experiments, we showed that our method could gather data from more than 80% of the sensor nodes longer than other clustering methods by over 30%.
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