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

无线传感器网络有可能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，在许多应用中，应当立即检测到危及生命的异常并对其采取行动，即，系统延迟应保持在最小值。为了解决这些问题，研究人员详细研究了低延迟框架中的数据处理和压缩技术。利用这些技术，更高的网络吞吐量和生命周期，可以实现，特别是当传感器部署在一个一次性的时尚和他们的电池不能被替换相比，最近流行的方法基于Turbo和低密度奇偶校验码，本研究的重点是高效的分布式信源编码算法，具有非常低的延迟。用于此目的的构建块是基于标量量化随后是标量码字分配的编码方案。这种标量编码方法，然后被扩展，以防止信道噪声，信道丢失，和不可靠的传感器，要么测量数据不正确或无法完全发挥作用，并被集成到分布式预测和变换编码方案。与预测和变换的传统使用相反，不能通过去除每个观测数据序列内的所有相关性来最大化编码性能。因此，整体工作的相当大的一部分是为了理解和表征最佳预测滤波器和变换。实际的实施问题，如增加的稳定性与预测滤波器设计的最优性之间的权衡也要研究。

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.