Adaptive priority-aware LoRaWAN resource allocation for Internet of Things applications

Lima, Eduardo de; Moraes, Jean; Oliveira, Helder; Cerqueira, Eduardo; Zeadally, Sherali; Rosário, Denis

doi:10.1016/j.adhoc.2021.102598

Cited by 28 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Related research on this topic comprises: (i) empirical studies that evaluate performance and other quality attributes of resource allocation strategies (Bor et al [ 19 ]), (ii) proposals of research allocation strategies (Bianchi et al [ 5 ], Goldoni et al [ 20 ], Lima et al [ 11 ], Reynders et al [ 21 ], and Ramli et al [ 8 ]), approaches (Valach and Macko [ 22 ]), methods (Moraes et al [ 23 ], and Slabicki et al [ 24 ]), techniques (Jeon and Jeong [ 25 ], and Figueiredo and Franco [ 14 ]), and algorithms (Abdelfadeel et al [ 17 ], and Farhad et al [ 26 ]), and (iii) studies reporting both proposals of resource allocation strategies and empirical evaluation results (Bianchi et al [ 5 ], Valach and Macko [ 22 ], Moraes et al [ 23 ], Aneda et al [ 27 ], and Moysiadis et al [ 28 ]). The contributions of each one of these studies and a comparative analysis are detailed in the following.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Parameters for LoRa-Based Networks Physical-Layer

Silva

Figueiredo

Oliveira

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Sub-GHz communication provides long-range coverage with low power consumption and reduced deployment cost. LoRa (Long-Range) has emerged, among existing LPWAN (Low Power Wide Area Networks) technologies, as a promising physical layer alternative to provide ubiquitous connectivity to outdoor IoT devices. LoRa modulation technology supports adapting transmissions based on parameters such as carrier frequency, channel bandwidth, spreading factor, and code rate. In this paper, we propose SlidingChange, a novel cognitive mechanism to support the dynamic analysis and adjustment of LoRa network performance parameters. The proposed mechanism uses a sliding window to smooth out short-term variations and reduce unnecessary network re-configurations. To validate our proposal, we conducted an experimental study to evaluate the performance concerning the Signal-to-Noise Ratio (SNR) parameter of our SlidingChange against InstantChange, an intuitive mechanism that considers immediate performance measurements (parameters) for re-configuring the network. The SlidingChange is compared with LR-ADR too, a state-of-the-art-related technique based on simple linear regression. The experimental results obtained from a testbed scenario demonstrated that the InstanChange mechanism improved the SNR by 4.6%. When using the SlidingChange mechanism, the SNR was around 37%, while the network reconfiguration rate was reduced by approximately 16%.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Those applications rely on wireless communication technology to collect and transmit data to a central server for further analysis. Therefore, communication is an important issue for the massive adoption and deployment of IoT applications [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Parameters for LoRa-Based Networks Physical-Layer

Silva

Figueiredo

Oliveira

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other research works, such as those reported in [ 22 , 23 ], aimed to optimize traffic and devices’ energy consumption by assigning a QoS to the nodes depending on the requirements of the applications. These proposals were mainly focused on adjusting the setup parameters of the radio transceiver in end devices and gateways, which is a research aspect that is out of this article’s scope.…”

Section: Related Workmentioning

confidence: 99%

Reducing Operational Expenses of LoRaWAN-Based Internet of Remote Things Applications

Finochietto

Santos

Ochoa

et al. 2022

Sensors

View full text Add to dashboard Cite

LoRaWAN has become the most widely used low-power wide-area network technology to implement monitoring solutions based on the Internet of remote things (IoRT) paradigm. Typically, these solutions interconnect remote sensing areas and data processing infrastructure located in urban centers. The operation expenses of these solutions depend mainly on the traffic sent through the network backhaul, i.e., the link that connects the remote sensing area and the urban area where the data are usually processed and stored. This service is provided by telecommunication companies and represents the main operation cost of IoRT solutions. These expenses usually limit the affordability of IoRT-based systems in developing countries, and also in scenarios where the operational cost is an issue to address. This paper presents an extension to the LoRaWAN protocol, named Node-Aware-LoRaWAN (NA-LoRaWAN), that reduces the traffic in the backhaul, thus decreasing the operational expenses of IoRT-based systems. In order to evaluate the performance of NA-LoRaWAN, it was compared to a regular LoRaWAN implementation. Depending on the network scenario, the proposed extension reduced the traffic through the backhaul in the range of 12–34%. This extension opens several opportunities to use IoRT solutions in application domains with a low operational budget, e.g., precision agriculture, environmental monitoring and natural hazards’ early detection.

show abstract

“…The simulation suggests that the high value of SF provides long coverage but sometimes they contribute to a high number of collisions. To increase LoRaWAN scalability and energy consumption in a dense IoT environment, the authors of [ 33 ] presented an adaptive priority-aware resource allocation approach. In [ 34 ], authors evaluate the coverage of LPWAN technology via real-life measurements.…”

Section: Related Workmentioning

confidence: 99%

Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks

Ali

Qureshi

Mustafa

et al. 2022

Entropy

View full text Add to dashboard Cite

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

show abstract

Adaptive priority-aware LoRaWAN resource allocation for Internet of Things applications

Cited by 28 publications

References 22 publications

Adaptive Parameters for LoRa-Based Networks Physical-Layer

Adaptive Parameters for LoRa-Based Networks Physical-Layer

Reducing Operational Expenses of LoRaWAN-Based Internet of Remote Things Applications

Edge Based Priority-Aware Dynamic Resource Allocation for Internet of Things Networks

Contact Info

Product

Resources

About