Dynamic Optimization For Heterogeneous Powered Wireless Multimedia Sensor Networks With Correlated Sources and Network Coding

TL;DR

This paper introduces a dynamic, distributed cross-layer optimization algorithm for heterogeneous powered wireless multimedia sensor networks with correlated sources, balancing distortion minimization and energy cost.

Contribution

It proposes a novel fully distributed algorithm combining source coding, network coding, and energy management for heterogeneous energy sources in WMSN.

Findings

The algorithm achieves optimal distortion and energy cost trade-offs.

Simulation results confirm the theoretical performance guarantees.

The approach effectively manages energy and data routing in complex network scenarios.

Abstract

The energy consumption in wireless multimedia sensor networks (WMSN) is much greater than that in traditional wireless sensor networks. Thus, it is a huge challenge to remain the perpetual operation for WMSN. In this paper, we propose a new heterogeneous energy supply model for WMSN through the coexistence of renewable energy and electricity grid. We address to cross-layer optimization for the multiple multicast with distributed source coding and intra-session network coding in heterogeneous powered wireless multimedia sensor networks (HPWMSN) with correlated sources. The aim is to achieve the optimal reconstruct distortion at sinks and the minimal cost of purchasing electricity from electricity grid. Based on the Lyapunov drift-plus-penalty with perturbation technique and dual decomposition technique, we propose a fully distributed dynamic cross-layer algorithm, including multicast routing, source rate control, network coding, session scheduling and energy management, only requiring knowledge of the instantaneous system state. The explicit trade-off between the optimization objective and queue backlog is theoretically proven. Finally, the simulation results verify the theoretic claims.