Error-resilient and complexity-constrained distributed coding for large scale sensor networks

There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design due to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.