Dynamic Compression-Transmission for Energy-Harvesting Multihop Networks with Correlated Sources

TL;DR

This paper proposes an online, joint optimization scheme for energy-harvesting multihop sensor networks with correlated sources, balancing source coding and transmission to minimize distortion and energy use under dynamic conditions.

Contribution

It introduces a novel perturbation-based Lyapunov approach for real-time joint optimization of source coding and transmission in energy-harvesting sensor networks with correlated data.

Findings

The proposed scheme achieves near-optimal performance with controllable queue sizes.

Side information at the sink improves network efficiency and reduces energy costs.

The method adapts effectively to time-varying channels and source correlations.

Abstract

Energy-harvesting wireless sensor networking is an emerging technology with applications to various fields such as environmental and structural health monitoring. A distinguishing feature of wireless sensors is the need to perform both source coding tasks, such as measurement and compression, and transmission tasks. It is known that the overall energy consumption for source coding is generally comparable to that of transmission, and that a joint design of the two classes of tasks can lead to relevant performance gains. Moreover, the efficiency of source coding in a sensor network can be potentially improved via distributed techniques by leveraging the fact that signals measured by different nodes are correlated. In this paper, a data gathering protocol for multihop wireless sensor networks with energy harvesting capabilities is studied whereby the sources measured by the sensors are correlated. Both the energy consumptions of source coding and transmission are modeled, and distributed source coding is assumed. The problem of dynamically and jointly optimizing the source coding and transmission strategies is formulated for time-varying channels and sources. The problem consists in the minimization of a cost function of the distortions in the source reconstructions at the sink under queue stability constraints. By adopting perturbation-based Lyapunov techniques, a close-to-optimal online scheme is proposed that has an explicit and controllable trade-off between optimality gap and queue sizes. The role of side information available at the sink is also discussed under the assumption that acquiring the side information entails an energy cost. It is shown that the presence of side information can improve the network performance both in terms of overall network cost function and queue sizes.