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

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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.