Optimizing Power Allocation in LoRaWAN IoT Applications

Abstract

Long Range Wide Area Network (LoRaWAN) is one of the most promising IoT technologies that are widely adopted in low-power wide-area networks (LPWAN). LoRaWAN faces scalability issues due to a large number of nodes connected to the same gateway and sharing the same channel. Therefore, LoRa networks seek to achieve two main objectives: successful delivery rate and efficient energy consumption. This paper proposes a novel game theoretic framework for LoRaWAN named Best Equal LoRa (BE-LoRa), to jointly optimize the packet delivery ratio and the energy efficiency (bit/Joule). The utility function of LoRa node is defined as the ratio of the throughput to the transmit power. LoRa nodes act as rational users (players) which seek to maximize their utility. The aim of the BE-LoRa algorithm is to maximize the utility of LoRa nodes while maintaining the same signal-to-interference-and-noise-ratio (SINR) for each SF. The power allocation algorithm is implemented at the network server, which leads to an optimum SINR, spreading factors (SFs) and transmission power settings of all nodes. Numerical and simulation results show that the proposed BE-LoRa power allocation algorithm has a significant improvement in packet delivery ratio and energy efficiency as compared to the Adaptive Data Rate (ADR) algorithm of legacy LoRaWAN. For instance, in very dense networks (624 nodes), BE-LoRa can improve the delivery ratio by 17.44% and reduce power consumed by 46% compared with LoRaWAN ADR.

Experimental Assessment of Common Crucial Factors That Affect LoRaWAN Performance on Suburban and Rural Area Deployments

“…ADR will be disabled in all cases because in some cases it caused more issues than it solved [ 28 ]. It is found that while ADR decides to update SF for a node based on channel conditions, it does not consider the impact of collisions resulting from the increasing number of nodes within the same network [ 29 ] to be able to manually control parameters independently. Within our study, important insights are expected regarding the impact of the data rate allocation of the EDs.…”

LoRa-Based IoT Network Assessment in Rural and Urban Scenarios

“…In [ 32 ], the authors evaluated the impact of SF on performance. Another method to improve the performance based on SF network clustering was presented in [ 33 ], and the authors in [ 34 ] developed an algorithm to further improve the efficiency of the LoRa network compared to the ADR algorithm.…”

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

“…The network server-based Best Equal LoRa (BE-LoRa) was proposed in [12] to optimize the energy consumption by efficiently allocating SF and TP based on optimum SINR. Their proposed method reduced energy consumption up to 36% and PDR by 17.44% compared to the typical ADR of LoRaWAN.…”

“…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.…”