III: Small: Reformulating Distributed Source Coding using Graphical Inference for Sensor Networks

III：小：使用传感器网络的图形推理重新设计分布式源编码

• 批准号: 1117886
• 负责人: Samuel Cheng
• 金额: $ 37.64万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117886&HistoricalAwards=false
• 关键词: III; Small; Reformulating; Distributed; Source

Sensor technology has grown tremendously in the recent decades. Using micro- and nano-technologies, sensor devices nowadays can be implemented in extremely small sizes. Networks of inexpensive sensors can now be easily deployed to areas in harsh conditions to provide continuous monitoring for environmental, military, and other scientific applications. As most sensor networks are utilized in hardly accessible areas such as a wilderness or battlefield, it is very costly or even infeasible to keep periodic maintenance of individual sensors. Therefore, the average battery life of the sensors essentially determines the life of the network itself. While the processing power consumption of a sensor can be reduced through the advance of computing technology, power consumed for communications can only be conserved by reducing the net amount of transferred information. Thus, research on advanced signal processing techniques for reducing communications power is essential for next-generation sensor networks.Distributed source coding (DSC) has been considered as the technology to drastically reduce energy consumption due to communications in sensor networks. However, current limitations of DSC prevent its adoption in sensor networks. The objective of this research is to advance the state-of-the-art of DSC for next-generation sensor networks by remodeling DSC as graphical inference problems. The resulting technologies are expected to lead to significant reduction of power consumption for communications and thus prolonged life span of such networks. To accomplish the goal, the researchers study DSC design for sources with more than two terminals, adaptive DSC algorithms for sources with dynamic varying correlation, and variational inference of DSC beyond the belief propagation (BP) algorithm.

近几十年来，传感器技术取得了巨大发展。利用微米和纳米技术，如今的传感器设备可以以极小的尺寸实现。廉价的传感器网络现在可以轻松部署到恶劣条件下的区域，为环境，军事和其他科学应用提供持续监测。由于大多数传感器网络都是在野外或战场等难以到达的地区使用，因此对单个传感器进行定期维护是非常昂贵的，甚至是不可行的。因此，传感器的平均电池寿命基本上决定了网络本身的寿命。虽然传感器的处理功耗可以通过计算技术的进步来降低，但是用于通信的功耗只能通过减少所传输的信息的净量来节省。因此，研究先进的信号处理技术以降低通信功耗对于下一代传感器网络至关重要。分布式信源编码（DSC）被认为是传感器网络中大幅降低通信能耗的技术。然而，DSC目前的局限性，阻止其在传感器网络中的采用。本研究的目的是通过将DSC重构为图形推理问题来推进下一代传感器网络的DSC的最新发展。预计由此产生的技术将导致通信功耗的显著降低，从而延长此类网络的寿命。为了实现这一目标，研究人员研究了具有两个以上终端的源的DSC设计，具有动态变化相关性的源的自适应DSC算法，以及DSC的变分推理超出置信传播（BP）算法。