Kalman Prediction Based Proportional Fair Resource Allocation for a Solar Powered Wireless Downlink

TL;DR

This paper presents an online resource allocation algorithm for solar-powered wireless sensor networks, combining Kalman filter-based energy prediction with a fair scheduling method to optimize downlink transmissions.

Contribution

It introduces PTF-On, an innovative algorithm that predicts solar energy availability and schedules transmissions to improve fairness and throughput in energy harvesting WSNs.

Findings

Effective energy prediction using Kalman filter.

Enhanced fairness and throughput in downlink scheduling.

Adaptability to daily solar energy variations.

Abstract

Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way.