利用生物启发技术实现无线网络的小世界特性
Achieving Small World Properties using Bio-Inspired Techniques in Wireless Networks
摘要：
无线自组织网络具有自组织特性以实现网络范围的特性是非常可取的和具有挑战性的。研究表明，小世界特性，主要是低平均路径长度和高聚类系数，是一般网络的理想特性。然而，由于无线网络的空间性质，实现小世界属性仍然是极具挑战性的。研究还表明，具有小世界特性的无线自组织网络的度分布介于几何律和幂律之间。在本文中，我们表明，在一个无线ad hoc网络与非均匀的节点密度只有本地信息，我们可以显着减少平均路径长度，并保留聚类系数。为了实现这个目标，我们的算法首先使用侧抑制技术识别逻辑区域，然后识别波束形成的节点，最后使用Flocking识别波束属性。我们使用侧抑制和群集，因为它们使我们能够使用本地状态信息，而不是其他技术。我们通过仿真结果和分析来支持我们的工作，仿真结果和分析表明，对于高密度网络，可以实现高达40%的减少。我们还展示了用于创建区域的跳数对平均路径长度、聚类系数和连通性的影响。
英文摘要：
It is highly desirable and challenging for a wireless ad hoc network to have self-organization properties in order to achieve network wide characteristics. Studies have shown that Small World properties, primarily low average path length and high clustering coefficient, are desired properties for networks in general. However, due to the spatial nature of the wireless networks, achieving small world properties remains highly challenging. Studies also show that, wireless ad hoc networks with small world properties show a degree distribution that lies between geometric and power law. In this paper, we show that in a wireless ad hoc network with non-uniform node density with only local information, we can significantly reduce the average path length and retain the clustering coefficient. To achieve our goal, our algorithm first identifies logical regions using Lateral Inhibition technique, then identifies the nodes that beamform and finally the beam properties using Flocking. We use Lateral Inhibition and Flocking because they enable us to use local state information as opposed to other techniques. We support our work with simulation results and analysis, which show that a reduction of up to 40% can be achieved for a high-density network. We also show the effect of hopcount used to create regions on average path length, clustering coefficient and connectivity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
