Performance Analysis of Single-Cell Adaptive Data Rate-Enabled LoRaWAN

Hoeller, Alexandre Ademar; Souza, Richard Demo; Montejo‐Sánchez, Samuel; Alves, Hirley

doi:10.1109/lwc.2020.2975604

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…tol s i indicates the sensitivity under the S i parameter [28,29], and if the throughput is equal, the set of parameters that minimizes the energy consumption of the ED transmission is considered min…”

Section: Link Parameter Adaptation Algorithmmentioning

confidence: 99%

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

et al. 2022

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LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

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Section: Link Parameter Adaptation Algorithmmentioning

confidence: 99%

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

et al. 2022

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“…It is shown in [31], [32] that full path-loss inversion suppresses the performance discrepancies among the existing devices. Moreover, it is argued in [33] that the power control in some LPWA IoT networks boils down to the full path-loss inversion power control. Opportunistic spectrum access avoids uplink transmissions during deep channel fades to alleviate wasting the harvested energy on transmissions that are more likely to fail.…”

Section: A Scope and Contributionsmentioning

confidence: 99%

Grant-Free Opportunistic Uplink Transmission in Wireless-Powered IoT: A Spatio-Temporal Model

Gharbieh

ElSawy

Emara

et al. 2021

IEEE Trans. Commun.

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Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for wirelesspower Internet of Things (IoT) networks. This paper jointly characterizes energy harvesting and packet transmissions in grant-free opportunistic uplink IoT networks energized via harvesting downlink energy.To do that, a joint queuing theory and stochastic geometry model is utilized to develop a spatio-temporal analytical model. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a twodimensional discrete-time Markov chain (DTMC). Such two dimensions are utilized to jointly track the scavenged/depleted energy to/from the batteries along with the arrival/departure of packets to/from devices buffers over time. Consequently, the adopted queuing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average delay, and the average buffer size. The effect of base stations (BSs) densification is discussed and several design insights are provided. The results show that the parameters for uplink power control

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“…The server increases the communication power and modifies the SF by considering the signal-to-noise ratio (SNR) to enhance the energy efficiency, airtime, and data [7] transmission speed. The transmit power is changed at each stage using a centralized approach, increasing resource efficiency [8]. A wide range of IoT technology requires a lot of linked devices for resource allocation-based data transmission.…”

mentioning

confidence: 99%

Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

Rao,

Sundar

2024

IEEE Access

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Performance Analysis of Single-Cell Adaptive Data Rate-Enabled LoRaWAN

Cited by 12 publications

References 9 publications

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

Grant-Free Opportunistic Uplink Transmission in Wireless-Powered IoT: A Spatio-Temporal Model

Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

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