Towards Energy-Fairness in LoRa Networks

Gao, Weifeng; Du, Wan; Zhao, Zhiwei; Min, Geyong; Singhal, Mukesh

doi:10.1109/icdcs.2019.00083

Cited by 49 publications

(20 citation statements)

References 58 publications

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“…In [135], the authors present EF-LoRa, a LoRa networking solution that can provide equitable power consumption between end devices based on smart allocation of various network resources such as frequency channels, SFs, and TP, ensuring balanced power consumption between LoRa network end devices and extending the network's life. According to simulation results, the proposed EF-LoRa solution can improve the energy fairness of existing LoRa networks by 177.8% and achieve higher energy efficiency fairness than existing LoRa and RS-LoRa networks.…”

Section: ) Mac Protocolsmentioning

confidence: 99%

Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial

et al. 2022

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Emerging Internet-of-Things (IoT) applications are driving increasing demand for advanced services in wireless networks, prompting the development of new technologies to address the associated challenges. Energy efficiency of IoT standards is a key feature targeted by research efforts and industrial activities, leading to an extensive and growing number of innovative solutions. Low Power Wide Area Networks (LPWANs) define a class of wireless communication technologies seen as highly relevant for future IoT development given its long communication range, low-cost devices and interesting energy management. Long Range Wide Area Network (LoRaWAN) is acknowledged to be the dominant IoT communication technology. It has allowed broad deployment and unlocked new IoT applications such as smart cities, asset tracking, etc. This article provides a comprehensive tutorial on the LoRa standard, and surveys existing solutions, hot topics and future insights for building energy efficient IoT infrastructures and IoT devices. Indeed, energy efficiency is one of the key factors for successful and sustainable deployments of IoT applications. More precisely, this article discusses how to meet LoRa/LoRaWAN energy efficiency across physical layer, medium access control layer, and network layer. Next, extensive pioneering solutions from related literature are compared and assessed. Finally, insightful conclusions are drawn, and open problems are listed at the end of this article.

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Section: ) Mac Protocolsmentioning

confidence: 99%

Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial

et al. 2022

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“…Packet reception ratio can be affected by both the channel quality and the interference from other devices. Since the devices in IIoT systems are usually resource-limited, we consider the network model in low power wireless networks in [14], where the distributed devices experience competing transmission environment, and their signals would conflict with each other if they are using the same wireless channel. Generally, the packet reception ratio can be represented by:…”

Section: Symbols Notationsmentioning

confidence: 99%

“…possible allocations and it is extremely difficult to traverse all of them. The above problem of searching for the best resource allocation is a min-max optimization and is NP-hard [14], which is difficult to solve for large scale IoT networks.…”

Section: B Problem Formulationmentioning

confidence: 99%

Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things

Gao

Zhao

Min

et al. 2021

IEEE Trans. Ind. Inf.

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Federated Learning (FL) has been employed for tremendous privacy-sensitive applications, where distributed devices collaboratively train a global model. In Industrial Internetof-Things (IIoT) systems, training latency is the key performance metric as the automated manufacture usually requires timely processing. The existing works increase the number of effective devices to accelerate the training. However, devices in IIoT systems are usually densely deployed, increasing the number of clients can potentially cause serious interference and prolonged training latency. In this paper, we propose RaFed, a resource allocation scheme for FL. We formulate the problem of reducing training latency as an optimization problem, which is proved to be NP-hard. We propose a heuristic to select appropriate devices to achieve a good trade-off between the interference and convergence time. We conduct experiments using an RGB-D dataset in an IIoT system. The results show that compared to the state-of-the-art works, RaFed significantly reduces the latency by 29.9%.

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“…In [ 29 , 30 ], spreading factor allocation techniques considering on energy have been presented. However, the references [ 29 , 30 ] only consider the energy efficiency and the optimization of the LoRaWAN performance is not considered. The presented technique is effective for the design of LoRaWAN that can demonstrate the best performance under the constraint for the average energy consumption of all EDs.…”

Section: Related Workmentioning

confidence: 99%

“…In the existing studies [17,[22][23][24][25][26][27][28], the performance can be enhanced in exchange for the energy consumption. In [29,30], spreading factor allocation techniques considering on energy have been presented. However, the references [29,30] only consider the energy efficiency and the optimization of the LoRaWAN performance is not considered.…”

Section: Table 1 Summary Of Existing Studiesmentioning

confidence: 99%

Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN

Narieda

Fujii

Umebayashi

2020

Sensors

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This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

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Towards Energy-Fairness in LoRa Networks

Cited by 49 publications

References 58 publications

Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial

Multi-Layered Energy Efficiency in LoRa-WAN Networks: A Tutorial

Resource Allocation for Latency-Aware Federated Learning in Industrial Internet of Things

Energy Constrained Optimization for Spreading Factor Allocation in LoRaWAN

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