Energy Allocation for LoRaWAN Nodes with Multi-Source Energy Harvesting

Abstract

Many connected devices are expected to be deployed during the next few years. Energy harvesting appears to be a good solution to power these devices but is not a reliable power source due to the time-varying nature of most energy sources. It is possible to harvest energy from multiple energy sources to tackle this problem, thus increasing the amount and the consistency of harvested energy. Additionally, a power management system can be implemented to compute how much energy can be consumed and to allocate this energy to multiple tasks, thus adapting the device quality of service to its energy capabilities. The goal is to maximize the amount of measured and transmitted data while avoiding power failures as much as possible. For this purpose, an industrial sensor node platform was extended with a multi-source energy-harvesting circuit and programmed with a novel energy-allocation system for multi-task devices. In this paper, a multi-source energy-harvesting LoRaWAN node is proposed and optimal energy allocation is proposed when the node runs different sensing tasks. The presented hardware platform was built with off-the-shelf components, and the proposed power management system was implemented on this platform. An experimental validation on a real LoRaWAN network shows that a gain of 51% transmitted messages and 62% executed sensing tasks can be achieved with the multi-source energy-harvesting and power-management system, compared to a single-source system.

Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

“…Their proposed method reduced energy consumption up to 36% and PDR by 17.44% compared to the typical ADR of LoRaWAN. Furthermore, another optimal energy allocation mechanism was proposed in [13] based on the ED sensing capabilities, and it showed improved energy consumption results compared to the typical ADR. The energy consumption ED can be further enhanced by utilizing a reinforcement learning method [14].…”

“…To improve the energy consumption 2018- [9] NS SF and TP To improve the energy consumption and PDR 2019- [10] NS SF and TP To lower the time-on-air and improve PDR 2020- [11] ED SF To improve the PDR 2021- [12] NS SF and TP To enhance the energy consumption 2021- [13] NS SF To enhance the energy consumption 2021- [14] NS SF and TP To enhance the energy consumption 2018- [5] NS SF and TP Analysis of convergence period only 2020- [3] ED SF To improve PDR and energy consumption 2020- [15] ED and NS SF and TP To reduce the convergence period 2020- [16] ED and NS SF and TP Convergence, PDR, and energy consumption Proposed ADR NS SF and TP Convergence, PDR, and energy consumption This paper proposes an NS-side ADR, which efficiently allocate resources (i.e., SF and TP) to EDs based on the Gaussian filter in conjunction with the received power. The proposed ADR method efficiently enhances the convergence period, PDR, and energy consumption by reducing the packet retransmission and packet loss ratios.…”

LPWAN Networks for Energy Meters Reading and Monitoring Power Supply Network in Intelligent Buildings

“…The problem of resource allocation in LoRaWANs has been studied, among others, in [9,10]. Optimization of power allocation in the networks has been considered in [11], whereas energy allocation and optimization in [12,13]. A multi-hop LoRa network protocol with minimized latency has been proposed in [14].…”