| With the advantage of rapid deployment, robustness, flexibility and inherent support for mobility, Wireless Sensor Networks (WSN) are playing a vital role in fields like disaster area monitoring, smart hospitals, intelligent battlefields, earthquake response systems, robot control and guidance in automatic manufacturing environments, and interactive museums.; Energy consumption is a major concern for these WSNs as nodes are battery powered, and it is often very difficult to change or recharge batteries of these nodes. We investigate a distributed and adaptive signal processing approach [1] to reduce the energy consumption. In this model, the correlations among the sensor data is continuously exploited in a distributed manner with efficient adaptive algorithm.; We implemented an enhanced version of the Least Mean Squares (LMS) adaptive algorithm to track existing correlations among the sensor data, which resulted in remarkable energy savings. We then replaced LMS with Reculsive Least Squares (RLS) adaptive algorithm for tracking correlations. This RLS algorithm gave better correlation tracking, which further improved the energy savings significantly. |
