均匀功率Ad Hoc无线网络中的分布式确定性广播
Distributed Deterministic Broadcasting in Uniform-Power Ad Hoc Wireless Networks
摘要：
移动自组网、车载自组网、iThings、纳米设备等未来技术的发展都依赖于多跳自组网中高效的分布式通信协议。这一领域的绝大多数研究集中在设计启发式协议，并通过随机生成的网络或在一些（通常是小规模的）无线网络的实际测量中获得的网络模拟来分析其性能。%的图书馆。此外，它们通常假设访问真正随机的源，这在无线设备的情况下通常是不合理的。本文利用一个形式化的框架来研究任意二维的欧几里德无线网络中具有均匀发射功率的广播问题及其时间复杂度。对于分析，我们考虑两种流行的模型的ad hoc网络的信号干扰噪声比（SINR）的基础上：一个机会主义的链接，和其他随机干扰的SINR。在前一个模型中，我们表明，我们的算法之一完成广播在$O(D\log^2 n)$轮，其中$n$是节点的数量和$D$是网络的直径。如果节点先验地知道网络的粒度$g$，即，任何两个站之间最小距离的最大传输范围的倒数，该算法的修改实现了$O(D\log g)$轮中的广播。最后，我们修改这两种算法，使它们在后一种模型中的效率与随机干扰的信干噪比，只有对数增长的性能。我们的是第一个证明有效的和可扩展的，在这两个模型下，广播任务的分布式确定性解决方案。
英文摘要：
Development of many futuristic technologies, such as MANET, VANET, iThings, nano-devices, depend on efficient distributed communication protocols in multi-hop ad hoc networks. A vast majority of research in this area focus on design heuristic protocols, and analyze their performance by simulations on networks generated randomly or obtained in practical measurements of some (usually small-size) wireless networks. %some library. Moreover, they often assume access to truly random sources, which is often not reasonable in case of wireless devices. In this work we use a formal framework to study the problem of broadcasting and its time complexity in any two dimensional Euclidean wireless network with uniform transmission powers. For the analysis, we consider two popular models of ad hoc networks based on the Signal-to-Interference-and-Noise Ratio (SINR): one with opportunistic links, and the other with randomly disturbed SINR. In the former model, we show that one of our algorithms accomplishes broadcasting in $O(D\log^2 n)$ rounds, where $n$ is the number of nodes and $D$ is the diameter of the network. If nodes know a priori the granularity $g$ of the network, i.e., the inverse of the maximum transmission range over the minimum distance between any two stations, a modification of this algorithm accomplishes broadcasting in $O(D\log g)$ rounds. Finally, we modify both algorithms to make them efficient in the latter model with randomly disturbed SINR, with only logarithmic growth of performance. Ours are the first provably efficient and well-scalable, under the two models, distributed deterministic solutions for the broadcast task.
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