Lightweight Carrier Sensing in LoRa: Implementation and Performance Evaluation

Rochester, Edward M.; Yousuf, Asif M.; Ousat, Behnam; Ghaderi, Majid

doi:10.1109/icc40277.2020.9149103

Cited by 13 publications

(11 citation statements)

References 9 publications

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“…In [114], the authors propose their own MAC solution in order to alleviate the low Packet Delivery Ratio (PDR) problem of pure ALOHA-based approach in LoRaWAN. They name their protocol Lightweight Carrier Sensing (LCS) and evaluate it by comparing it with pure ALOHA in a threefold way: mathematical approach, simulation and small scale implementation.…”

Section: A Analytical Approachesmentioning

confidence: 99%

“…QoS requirements are also examined in terms of required PDR for vastly populated LoRa networks. More work with the same simulator includes the further evaluation of the LCS protocol proposed in [114]. The authors perform comparative experiments between LCS and pure ALOHA in terms of energy consumption (based on their proposed energy models) and supported EDs; LCS appears to be superior in both metrics.…”

Section: B Simulation Studiesmentioning

confidence: 99%

“…Also, coverage estimation based on PDR is provided, resulting in an optimal 7.5km range with >95% PDR, an unsatisfactory behavior in indoor scenarios and a non-critical role of velocity in mobility cases, at least not as much as other propagation factors like Line of Sight (LOS). Part of the discussed testbed is also utilized to evaluate the LCS mechanism, presented in [114]. Two of the RFM95W-based EDs and 1 GW were used in an indoor environment to test the transmissions detection precision of the protocol, which proved to be highly accurate.…”

Section: Real System Implementationsmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Study on LPWANs With a Focus on the Potential of LoRa/LoRaWAN Systems

Milarokostas

Tsolkas

Passas

et al. 2023

IEEE Commun. Surv. Tutorials

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The Internet of Things (IoT) ecosystem is emerging rapidly. From the connectivity perspective, it is the vehicle towards connecting massive number of devices to the Internet; whereas, from the application perspective, it paves the way towards collecting huge amounts of data and enabling novel information-based services. The connectivity infrastructure that will support the IoT ecosystem is composed of both expansions applied in the well-deployed mobile network infrastructures, and dedicated deployments provided by vertical-specific setups. In the latter case, the Low-Power Wide Area Networks (LPWANs) have become a cost-effective solution, incorporating efficient technologies for wireless connectivity and control. In this context, a comprehensive study is provided to assist on the currently increasing research and development interest in the area of LPWANs. The study provides state-of-the-art work as well as research directions and tools for the most representative LPWAN technologies. Supporting the concept of openness in the IoT ecosystem, the study targets connectivity solutions that operate in unlicensed spectrum bands, such as the LoRa/LoRaWAN systems, and describes the most recent cloud and open-sourced solutions for IoT service and data management.

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Section: A Analytical Approachesmentioning

confidence: 99%

Section: B Simulation Studiesmentioning

confidence: 99%

Section: Real System Implementationsmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Study on LPWANs With a Focus on the Potential of LoRa/LoRaWAN Systems

Milarokostas

Tsolkas

Passas

et al. 2023

IEEE Commun. Surv. Tutorials

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“…Much research has been conducted in the area of computing the optimal communication-channel parameters used by IoT end nodes and the network server [12], [13], [14], [15]. The work has been especially focused on optimizing energy requirements of end nodes [16], [17], [18], [19], [20], selecting a communication channel [21], [22], [23], reducing the number of collisions [24], [25], [26], predicting collisions [27], or making the communication generally more efficient [28], [29], [10].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, the latest research proved that a lightweight carrier sensing can also make the communication more efficient and energy-wise in a dense and harsh environment with a lot of nodes placed close to each other [24], [33], e.g., a smart-city scenario.…”

Section: Related Workmentioning

confidence: 99%

Improvement of LoRa Communication Scalability using Machine Learning Based Adaptiveness

Valach¹,

Macko²

2021

Preprint

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<div>Number of embedded devices connected to the Internet is rapidly increasing, especially in the era of the Internet of Things (IoT). The growing number of IoT devices communicating wirelessly causes a communication-parameters selection problem, implying the increasing number of communication collisions. Multiple factors of IoT networks signify this problem, such as inability to communication-channel listening prior to the transmission (due to longer distances), energy constrains (due to inability of powering devices from the grid), or limitation of duty cycle and high interference (due to usage of unlicensed band in communication technologies). This article is focused on alleviating this problem in LoRa networks, which is one of the most promising technology for long-range and low-power</div><div>communication. We utilize the existing LoRa@FIIT protocol to achieve energy-efficient communication. The scalability of the LoRa network is increased by modifying the communication-parameters selection algorithm. By ensuring of quality of service mechanism at each node in the infrastructure, the application domain of the proposed architecture is widened. The simulation-based experimental results showed a significantly reduced number of collisions for mobile nodes, which reduces the channel congestion and the wasted energy by retransmissions.</div>

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Optimization of LoRa Networks Using Multi-armed Bandit Algorithms

Valach

Macko

2022

Artificial Intelligence and Sustainable Computing

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Lightweight Carrier Sensing in LoRa: Implementation and Performance Evaluation

Cited by 13 publications

References 9 publications

A Comprehensive Study on LPWANs With a Focus on the Potential of LoRa/LoRaWAN Systems

A Comprehensive Study on LPWANs With a Focus on the Potential of LoRa/LoRaWAN Systems

Improvement of LoRa Communication Scalability using Machine Learning Based Adaptiveness

Optimization of LoRa Networks Using Multi-armed Bandit Algorithms

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