CAREER: LOW-DELAY COMMUNICATION IN SENSOR NETWORKS VIA PREDICTION- AND TRANSFORM-BASED DISTRIBUTED SOURCE CODING

职业：通过基于预测和转换的分布式源编码实现传感器网络中的低延迟通信

• 批准号: 0643695
• 负责人: Ertem Tuncel
• 金额: $ 40万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643695&HistoricalAwards=false
• 关键词: CAREER; LOW; DELAY; COMMUNICATION; SENSOR

Wireless sensor networks have the potential to deliver significant new capabilities in various applications ranging from environmental health and safety to homeland security. However, these networks are severely constrained by limited bandwidth and power. Further, in many applications, life-critical anomalies should immediately be detected and acted upon, i.e., system delay should be kept at a minimum. To address these constraints, the investigator studies in detail data processing and compression techniques in a low-delay framework. Utilizing these techniques, higher network throughput and lifetime can be achieved, especially when the sensors are deployed in a one-time fashion and their batteries cannot be replaced.In contrast with popular recent methods based on turbo and low density parity check codes, this research focuses on efficient distributed source coding algorithms that operate with very low delay. The building block for this purpose is coding schemes that are based on scalar quantization followed by scalar codeword assignment. This scalar coding methodology is then to be extended for protection against channel noise, channel loss, and unreliable sensors that either measure the data incorrectly or fail to function completely, and to be integrated into distributed predictive and transform coding schemes. In contrast with the traditional use of prediction and transforms, the coding performance cannot be maximized by removing all the correlation within each observed data sequence. Thus, a considerable portion of the overall effort is towards understanding and characterizing optimal prediction filters and transforms. Practical implementation issues such as the tradeoff between increased stability versus optimality in prediction filter design are also to be studied.

无线传感器网络有潜力在从环境健康和安全到国土安全的各种应用中提供重要的新功能。然而，这些网络受到有限带宽和功率的严重限制。此外，在许多应用程序中，应该立即检测到危及生命的异常并采取行动，也就是说，应该将系统延迟保持在最低限度。为了解决这些限制，研究者在低延迟框架中详细研究了数据处理和压缩技术。利用这些技术，可以实现更高的网络吞吐量和使用寿命，特别是当传感器以一次性方式部署且电池无法更换时。与最近流行的基于turbo和低密度奇偶校验码的方法相比，本研究侧重于以极低延迟运行的高效分布式源编码算法。用于此目的的构建块是基于标量量化和标量码字赋值的编码方案。然后，该标量编码方法将被扩展，以防止信道噪声、信道损失和不可靠的传感器（要么测量数据不正确，要么完全失效），并集成到分布式预测和转换编码方案中。与传统的预测和变换相比，不能通过去除每个观测数据序列中的所有相关性来最大化编码性能。因此，总体工作的相当一部分是为了理解和描述最佳预测过滤器和转换。实际实现问题，如在预测滤波器设计中增加稳定性与最优性之间的权衡也将被研究。