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Accelerating distributed averaging in sensor networks: Randomized gossip over virtual coordinates

机译:加速传感器网络中的分布式平均:虚拟坐标上的随机八卦

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Distributed averaging represents a central task in many applications related to sensor networks, ad-hoc networks, and embedded wireless systems. These systems are typically constrained in terms of computation, communication and energy requirements, thus making the design of effective solutions particularly challenging. Algorithms based on iterative rounds of computation among selected nodes of the network, such as randomized gossip algorithms, are regarded as viable approaches because of their robustness in dynamic settings and their simple conceptual design principles. Several efforts have been devoted to reduce the number of transmissions required by gossip algorithms to converge, in order to mitigate energy consumption and prolong the system lifetime. Geographic gossip, for instance, exploits geographic routing to enable values exchange between non-neighboring nodes through multi-hop communication, leading to faster convergence. In this paper we propose to extend this approach to networks whose nodes are not aware of their geographic location by developing a lightweight algorithm based on simple virtual coordinates. Experimental results point out the validity of the proposed method which, despite its simplicity, achieves significant gains in terms of convergence rate with respect to the reference randomized gossip across a wide range of operating conditions.
机译:在与传感器网络,自组织网络和嵌入式无线系统有关的许多应用中,分布式平均代表着一项中心任务。这些系统通常在计算,通信和能源需求方面受到限制,因此使有效解决方案的设计特别具有挑战性。由于在动态设置中的鲁棒性和简单的概念设计原理,基于网络的选定节点之间的迭代计算循环的算法(例如随机八卦算法)被视为可行的方法。为了减少能量消耗并延长系统寿命,已经做出了一些努力来减少八卦算法收敛所需的传输数量。例如,地理八卦利用地理路由来通过多跳通信实现非相邻节点之间的值交换,从而加快收敛速度​​。在本文中,我们建议通过开发基于简单虚拟坐标的轻量级算法,将这种方法扩展到其节点不知道其地理位置的网络。实验结果指出了该方法的有效性,尽管该方法简单易行,但在广泛的工作条件下,相对于参考随机八卦而言,其收敛速度方面取得了显着的进步。

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