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

2025-01-11

AI Summary

Energy-harvesting wireless sensor networks suffer from poor communication continuity due to unstable energy supply. Method: This paper proposes an asynchronous “accumulate-and-transmit” protocol that jointly optimizes transmit power allocation and energy-harvesting duty cycle to maximize long-term average system throughput. Contribution/Results: It is the first work to introduce asynchrony into energy-harvesting networks, unifying the coupled dynamics of data and energy queues—thereby departing from the conventional two-phase “harvest-then-transmit” paradigm. Using inner approximation and a queue-driven iterative optimization algorithm, the method achieves locally optimal resource allocation. Experiments demonstrate significant reductions in both data and energy queue lengths, along with improved throughput stability and network sustainability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Wireless Sensor Networks
• Energy Harvesting
• Resource Allocation

Innovation

• Energy Harvesting
• Wireless Sensor Networks
• Optimized Resource Allocation