Optimization framework with reduced complexity for sensor networks with in-network processing

Sepideh Nazemi, Kin K. Leung, Ananthram Swami

Abstract: We propose a framework for optimizing in-network processing (INP) in wireless sensor networks. INP provides a platform for processing (e.g., fusing, aggregating or compressing) the data along the transmission routes in the sensor network. This can reduce the volume of transmitted data, therefore optimizing the utilization of energy and bandwidth. However, such data processing must ensure that the end result can meet given QoI requirements. We formulate the QoI-aware INP problem as a non-linear optimization problem to identify the optimal degree of data compression at each sensor node subject to satisfying a QoI requirement for the end-user. The formulation arranges all involved sensor nodes in a tree where data is transfered and processed from nodes to their parent nodes toward the root node of the tree. Under the assumption of uniform parameter setting, we show that the processing tree can be collapsed into a linear graph where the number of nodes represents the node levels of the original processing tree. This represents a significant reduction in complexity of the problem. Numerical example are provided to illustrate the performance of the proposed approach.