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Consensus algorithms for power-constrained wireless sensor networks.

机译：功率受限的无线传感器网络的共识算法。

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摘要

Wireless sensor networks (SNets) is a cost-efficient technology that is typically comprised of many low-power, low-cost sensors. Current and potential applications of SNets include tracking, automation, control, surveillance, reconnaissance, security, and monitoring. All of these applications require some form of intelligent signal processing and decision making algorithms. Proposed algorithms involving fusion-center-based architectures do not scale well with increasing number of sensors. Instead, scalable, robust and power-efficient distributed signal processing and decision making algorithms are required in order to realize the full potential of SNets. Therefore, in this thesis, we propose and analyze power-efficient in-network signal processing and decision making algorithms for applications that are modeled as distributed classification, data aggregation and symmetric function computation problems.;The work in this thesis is a step towards solving distributed signal processing and decision making problems in low-power distributed sensor networks in a cost-efficient manner. The low-power, constrained communication architecture of emerging sensor network technologies demand scalable, asynchronous, energy-efficient and robust sensor network architectures and algorithms. We address these concerns in this thesis by declaring the proposed algorithm as: (i) Scalable because communication is restricted to immediate one-hop neighbors. (ii) Asynchronous because no synchronization between sensor nodes or order of transmission of messages are required. (iii) Energy-efficient in terms of reduced total power and average power consumed. (iv) Robust to node and edge failures.;In this thesis we propose our solution in two main categories. (i) We propose and analyze pair-wise message passing algorithms, where sensors achieve the optimal classification performance. (ii) We propose and analyze algorithms in order to calculate the measurement statistics of certain functions. Under both these problems and proposed solutions, we assume each sensor initially has immediate access only to its observation, each communication takes place between sensor and its immediate neighbors, communication is not reliable, and node and edge failures occur.;The proposed distributed classification algorithm is inspired by Belief Propagation algorithm. Our contributions include the calculation of convergence time and energy expenditure of the algorithm for certain sensor network topologies. The distributed function computation algorithm is inspired by coalescing random walks. We prove the convergence time and message count of the algorithms for certain sensor network topologies. We also present detailed comparisons of the proposed function computation algorithm with existing gossip spreading algorithms. In the last part of our work, we generalize the studied communication strategies in order to achieve reduced power consumption. By introducing power-control to the sensor network, we seek for trade-offs between average power consumed and total convergence time. Finally, we show how our work can be applied to realistic sensor network scenarios.
机译：无线传感器网络（SNets）是一种经济高效的技术，通常由许多低功耗，低成本传感器组成。 SNet的当前和潜在应用包括跟踪，自动化，控制，监视，侦察，安全和监视。所有这些应用都需要某种形式的智能信号处理和决策算法。提出的涉及基于融合中心的架构的算法无法随着传感器数量的增加而很好地扩展。相反，为了实现SNet的全部潜能，需要可扩展，健壮且省电的分布式信号处理和决策算法。因此，本论文针对分布式分类，数据聚合和对称函数计算等问题的应用，提出并分析了高能效的网络信号处理和决策算法。低功耗分布式传感器网络中的分布式信号处理和决策问题，具有成本效益。新兴的传感器网络技术的低功耗，受约束的通信架构要求可扩展，异步，高能效且坚固的传感器网络架构和算法。在本文中，我们通过将所提出的算法声明为：（i）可扩展，因为通信仅限于直接的一跳邻居，从而解决了这些问题。 （ii）异步，因为不需要传感器节点之间的同步或消息的传输顺序。 （iii）在减少总功率和平均消耗功率方面具有能源效率。 （iv）健壮的节点和边缘故障。在本文中，我们提出了两种主要的解决方案。 （i）我们提出并分析成对的消息传递算法，其中传感器实现了最佳的分类性能。 （ii）我们提出并分析算法，以计算某些功能的度量统计量。在这些问题和提出的解决方案下，我们假设每个传感器最初只能立即访问其观测值，每个通信都发生在传感器与其直接邻居之间，通信不可靠，并且发生节点和边缘故障。受“信仰传播”算法启发。我们的贡献包括针对某些传感器网络拓扑的算法的收敛时间和能量消耗的计算。分布函数计算算法的灵感来自于合并随机游走。我们证明了某些传感器网络拓扑的算法的收敛时间和消息数。我们还介绍了拟议的函数计算算法与现有的八卦扩展算法的详细比较。在我们工作的最后一部分，我们概括了所研究的通信策略，以实现降低的功耗。通过将功率控制引入传感器网络，我们寻求在平均功耗与总收敛时间之间进行权衡。最后，我们展示了如何将我们的工作应用于现实的传感器网络场景。

著录项

  • 作者

    Savas, Onur.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 136 p.
  • 总页数 136
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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