Energy-Aware Resource Allocation for Energy Harvesting Powered Wireless Sensor Nodes

TL;DR

The paper tackles sustaining long-term communication in energy-harvesting powered wireless sensor networks by proposing an energy-aware resource allocation framework for the asynchronous accumulate-then-transmit protocol. It formulates a stochastic optimization problem that maximizes the average network utility $U(p|s)$ with per-frame throughput $r_k(p_k^i[t]) = w_k \log_2(1+\gamma_k[t])$ and $\gamma_k[t] = \frac{p_k^i[t]|h_k[t]|^2}{\sigma^2}$, under data/energy queue stability and power constraints, solved via Lyapunov drift-plus-penalty in combination with inner approximation (IA) and network utility maximization (NUM). An iterative, per-frame algorithm is developed that converges to at least a local optimum and yields improved long-term throughput and queue stability, demonstrating robust performance across time frames. Numerical results validate significant throughput gains over harvest-then-transmit benchmarks and show effective queue management and energy utilization, with convergence typically within six iterations per frame. The work provides a practical framework for energy-aware EH-enabled WSNs and suggests extending the model to nonlinear EH dynamics in future studies.

Abstract

Low harvested energy poses a significant challenge to sustaining continuous communication in energy harvesting (EH)-powered wireless sensor networks. This is mainly due to intermittent and limited power availability from radio frequency signals. In this paper, we introduce a novel energy-aware resource allocation problem aimed at enabling the asynchronous accumulate-then-transmit protocol, offering an alternative to the extensively studied harvest-then-transmit approach. Specifically, we jointly optimize power allocation and time fraction dedicated to EH to maximize the average long-term system throughput, accounting for both data and energy queue lengths. By leveraging inner approximation and network utility maximization techniques, we develop a simple yet efficient iterative algorithm that guarantees at least a local optimum and achieves long-term utility improvement. Numerical results highlight the proposed approach's effectiveness in terms of both queue length and sustained system throughput.

Figures (5)

• Figure 1: The aynchronous accumulate-then-transmit-based system model.
• Figure 2: Convergence behavior of different schemes.
• Figure 3: Sum throughput of difference schemes versus the number of time-frames
• Figure 4: Average total queue length with respect to the scaling factor, $\beta$.
• Figure 5: Average sum of harvested power and throughput versus the total power budget at PB, $P_{\max}$.

Theorems & Definitions (1)

• Remark 1