面向开发一种用于manet中聚类计算的稳定性启发式算法的框架

Gaurav Saxena, A. Singhal
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引用次数: 3

摘要

在ad-hoc环境中,层次路由方案优于平面路由方案。一些算法,如最低ID、LCC、最高入度、WCA、IWCA、基于神经网络等,已经被提出用于节点聚类,但它们都没有考虑到异构ad-hoc网络的特定环境动态特性。他们没有考察电池电量、节点度和移动度等参数对簇形成的综合影响。虽然这些因素可以被认为是神经网络的输入,但训练网络和选择训练算法是一个计算量大且耗时的步骤。在本文中,我们通过计算用于决定簇头的稳定性因子来解决这个问题。该因素独立于底层环境,计算不密集,并考虑到环境变化。它不涉及像GPS这样的方案来测量移动性,它显然假设每个计算设备中都有一个预先存在的设施,作为ad-hoc网络的成员来测量另一个节点的位置,或者依赖于外部设备来传递位置。稳定性因子还考虑到干扰异常——我们将其定义为由于节点接收功率水平下降而导致簇头变化的假警报。簇头的这种变化不是由于它们之间的任何相对运动。稳定因子计算可以很容易地构建到软件中,并且可以部署到任何特定环境中进行集群头计算,而不需要任何潜在的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Framework towards developing a stability heuristic for cluster computation in MANETs
Hierarchical routing schemes in an ad-hoc environment outperform the flat routing schemes. Several algorithms like Lowest ID, LCC, Highest in-degree, WCA, IWCA, neural network based etc. have been proposed for clustering of nodes but none of them take into account the environment specific dynamic nature of a heterogenous ad-hoc network. They do not examine the combined effect of parameters like battery power, degree of node and mobility on cluster formation. Although these factors can be considered as inputs to a neural network, training the network and choosing the training algorithm is a computationally intensive hence time consuming step. In this letter we address this issue by computing a Stability factor for deciding cluster-heads. This factor is independent of the underlying environment, computationally un-intensive and takes into account environmental changes. It involves no GPS like schemes to measure mobility which clearly assumes a pre-existing facility in every computing device acting as a member of an ad-hoc network to measure the position of another node or relies on an external device to convey the position. The Stability factor also takes care of the interference anomaly - which we define as a false alarm resulting in change of a cluster-head due to a decrease in the received power levels at a node. This change in cluster-head is not due to any relative motion between them. The stability factor calculation could easily be built into a software and can be deployed for cluster-head calculation in any ad-hoc environment with no underlying assumptions.
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