Error/Erasure-Resilient and Complexity-Constrained Zero-Delay Distributed Coding for Large-Scale Sensor Networks

Abstract

There has been considerable interest in distributed source coding (DSC) in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat to practical deployment of such techniques: the exponential growth of decoding complexity with network size and coding rates and the critical requirement of resilience to bit errors and erasures, given the severe channel conditions in many wireless sensor network applications. This article proposes a novel, unified approach for large-scale, error/erasure-resilient DSC that incorporates an optimally designed, nearest neighbor classifier-based decoding framework, where the design explicitly controls performance versus decoding complexity. Motivated by the highly nonconvex nature of the cost function, we present a deterministic annealing-based optimization algorithm for the joint design of the system parameters, which further enhances the performance over the greedy iterative descent technique. Simulation results on both synthetic and real sensor network data provide strong evidence for performance gains compared to other state-of-the-art techniques and may open the door to practical deployment of DSC in large sensor networks. Moreover, the framework provides a principled way to naturally scale to large networks while constraining decoder complexity, thereby enabling performance gains that increase with network size.