DaRe:<u>Da</u>ta<u>Re</u>covery Through Application Layer Coding for LoRaWAN

Marcelis, Paul; Kouvelas, Nikolaos; Rao, Vijay S.; Prasad, R. Venkatesha

doi:10.1109/tmc.2020.3016654

Cited by 34 publications

(45 citation statements)

References 42 publications

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“…In the surveyed related work [2,10,13,[17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], although some experiments are performed using LoRa (in some cases using only LoRa transmission technology, and in some cases using the LoRa Wide Area Network protocol, LoRaWAN), most of these works are not focused on the application of this technology in mobility services. Furthermore, in works such as [2,10,13,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] a wide range of possible settings (i.e., specific sets of SF, BW and CR values) are not explored.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of LoRa in Transit Vehicle Tracking Service Based on Intelligent Transportation Systems and IoT

et al. 2020

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Long-range (LoRa) technology is a low power wide area network (LPWAN) technology that is currently being used for development of Internet of things (IoT)-based solutions. Transit transport, mainly in medium-sized cities where transit vehicles do not have exclusive lanes, is a service that can be improved with a tracking service using technology such as LoRa. Although some proposals exist, there is not enough experimental information to validate the LoRa technology as adequate. This article: (a) evaluates the operation of LoRa technology in a transit vehicle tracking service in a medium-sized city, based on an Intelligent Transportation Systems architecture and IoT; and (b) investigates optimal LoRa technology configuration parameters for the service. Experiments were performed in a semi-controlled environment using LoRa devices and a gateway, by measuring the received packets and the receive signal strength indicator (RSSI) and modifying: (a) distance; (b) number of devices; and (c) the main LoRa transmission parameters. Obtained results show the ideal values of parameters vary considerably with distance and number of devices used. There were very few settings of the experiments in which the RSSI and packet levels were adequate while distance and number of devices were both changed.

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Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of LoRa in Transit Vehicle Tracking Service Based on Intelligent Transportation Systems and IoT

et al. 2020

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“…Since the massive deployment of LoRa modulation for sensor applications, many papers investigated this new technology concerning its energy consumption. Certain studies have considered the ability of LoRa technology determining the performance for various parameter settings in indoor [17,18] or outdoor [9,19,20] configurations. Bor and Roedig introduce an algorithm for obtaining the most reliable transmission setting for a particular transmission channel in [15].…”

Section: Related Workmentioning

confidence: 99%

“…The sensor node lifetime is illustrated in Figure 12 using the battery characteristics (capacity equals 50 mAh and a supply voltage of 3 V). The sensor node autonomy is about at 164 days when the measurement period is equal to 30 s. According to the theoretical lifetime of an end-device is computed, employing average energy consumption results acquired for unacknowledged transmission and acknowledged transmission by using Equations ( 18), (19) and (20). Figure 13 and Figure 14 shows the results of energy consumption and battery life in different scenarios, respectively.…”

Section: Analysis the Proposed Scenariosmentioning

confidence: 99%

Evaluation of Energy Consumption of LPWAN Technologies

Rajab

Cinkler

Bouguera

2021

Preprint

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The modern technological innovations provide small radios with ability to broadcast over vast areas with minimum energy consumption that will significantly influence the future of the Internet of Things (IoT) communications. The majority of IoT implementations demand sensor nodes run reliably for an extended time. Furthermore, the radio settings can endure a high data rate transmission while optimizing the energy-efficiency. The LoRa/LoRaWAN is one of the primary Low-Power Wide Area Network (LPWAN) technology that has highly enticed much concentration recently from the community. The energy limits is a significant issue in wireless sensor networks since battery lifetime that supplies sensor nodes have a restricted amount of energy and neither expendable nor rechargeable in most cases. A common hypothesis in previous work is that the energy consumed by sensors in sleep mode is negligible. With this hypothesis, the usual approach is to consider subsets of nodes that reach all the iterative targets. These subsets also called coverage sets, are then put in the active mode, considering the others are in the low-power or sleep mode. In this paper, we address this question by proposing an energy consumption model based on LoRa and LoRaWAN, that model optimizes the energy consumption of the sensor node for different tasks for a period of time. The proposed analytical approach permits considering the consumed power of every sensor node element; furthermore, it can be used to analyse different LoRaWAN modes to determine the most desirable sensor node design to reach its energy autonomy.

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“…the unstable network communication, synchronization problems, unreliable sensor devices, environmental factors, and other device malfunctions; [3][4][5][6] the interruption of the data acquisition in long-term monitoring scenarios; [7] the location, firmware may not be consistent across locations. This could mean differences in reporting frequency or formatting of values; [8] the sensor failures, monitoring system failures or network failures; [9] the storage errors, unreliable IoT devices, unstable network status; [10] the incorrect response or nonresponse of the IoT-based sensors; [11] the collision of the nodes when the information passes from sender to receiver; [12] the channel effects and mobility of the end-devices; [13] the errors in data collection and transmission; [14] the data integration from different sources into a unified schema; [15] the lack of battery power, communication errors, and malfunctioning devices. [16] As can be seen from Table 1, there are many reasons for the incompleteness in the datasets collected by IoT devices.…”

Section: Reasons Investigationsmentioning

confidence: 99%

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

Tkachenko

Izonin

Kryvinska

et al. 2020

Sensors

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The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

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DaRe:DataRecovery Through Application Layer Coding for LoRaWAN

Cited by 34 publications

References 42 publications

Experimental Evaluation of LoRa in Transit Vehicle Tracking Service Based on Intelligent Transportation Systems and IoT

Experimental Evaluation of LoRa in Transit Vehicle Tracking Service Based on Intelligent Transportation Systems and IoT

Evaluation of Energy Consumption of LPWAN Technologies

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

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