Implementation of a LoRa Mesh Library

Solé, Joan Miquel; Centelles, Roger Pueyo; Freitag, Félix; Meseguer, Roc

doi:10.1109/access.2022.3217215

Cited by 15 publications

(5 citation statements)

References 16 publications

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“…An enriched search function for querying directly the IoT sensor nodes could open new application cases to LoRaTRUST, such as applications that require near real-time information for short response times. The LoRaMesher technology [35] used in LoRaTRUST principally can route queries from the backend to IoT devices, but such a use case has not yet been enabled in the current implementation.…”

Section: Discussionmentioning

confidence: 99%

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LoRaTRUST: Blockchain-Enabled Trust and Accountability Service for IoT Data

Vilchez

Jacques²,

Freitag

et al. 2023

Electronics

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Environmental monitoring is a growing application of the Internet of Things. The low cost of the sensor nodes, LoRa connectivity, and increased awareness of environmental issues have motivated many citizens to participate in open IoT monitoring applications. However, the value of these applications for decision makers is limited since the data from the IoT sensors do not have sufficient guarantees to be trusted. In this paper, we introduce a new concept that attributes value to both IoT data and devices, such as sensor nodes and gateways, and leverage distributed ledger technology to enable a data trust system. A first design decision was to assign Ethereum addresses with their associated public and private key pairs to all actors. This allows the authentication of data senders and hence the accounting for the contribution of each participant. Secondly, we introduce an auditor to validate the received IoT data. The results of these audits increase the trust in the quality of the data. We present the architectural components that we designed to enhance trust in open IoT monitoring applications and present an operational prototype to show the feasibility of the implementation. By achieving both trust in the data and accounting of contributions for giving rewards, open participatory IoT monitoring applications can become both valuable and sustainable. Then, trusted open monitoring may complement commercial solutions as a technical and economic alternative for addressing the increasing environmental monitoring needs of our society.

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Section: Discussionmentioning

confidence: 99%

“…Sensor nodes are arranged in a mesh network topology, and gateways are connected to the Internet. The firmware uses LoRaMesher technology to facilitate the communication between sensor and gateway nodes [35]. As sensor data, we have used the GPS locations of the devices.…”

Section: Prototypementioning

confidence: 99%

LoRaTRUST: Blockchain-Enabled Trust and Accountability Service for IoT Data

Vilchez

Jacques²,

Freitag

et al. 2023

Electronics

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“…In our own work we have developed the LoRaMesher library to build LoRa mesh networks [16]. LoRaMesher implements a distance-vector routing protocol for exchanging directed messages among LoRa nodes.…”

Section: Technologies To Build Lora Mesh Networkmentioning

confidence: 99%

Position Paper: LoRa Mesh Networks for Enabling Distributed Intelligence on Tiny IoT Nodes

Freitag

Solé

Meseguer

2023

Ambient Intelligence and Smart Environments

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LoRaWAN is a widely used solution in today’s Internet of Things (IoT) applications to connect remote sensor nodes to the Internet. At the same time, microcontroller-based sensor nodes with increased processing capacities are increasingly becoming smart nodes applied for performing machine learning tasks. We argue that LoRaWAN has important connectivity limitations to truly unleash the potential of these smart tiny nodes. We position to apply LoRa mesh networks as a communication substrate to enable novel networking capacities for the new scenario of distributed smart IoT nodes.

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“…However, in many countries, the duty cycle of LoRa communication is limited to 1%, which limits the data rate that can be achieved. The nodeto-node communication with LoRa between geographically spread IoT nodes is enabled by LoRa mesh networks [10]. However, sending the least number of packets in a LoRa communication is, in general, desirable for reducing packet collisions and latency.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Weight Quantization on Online Federated Learning for the IoT: A Case Study

Giménez,

Lee,

Freitag

et al. 2024

IEEE Access

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Many weight quantization approaches were explored to save the communication bandwidth between the clients and the server in federated learning using high-end computing machines. However, there is a lack of weight quantization research for online federated learning using TinyML devices which are restricted by the mini-batch size, the neural network size, and the communication method due to their severe hardware resource constraints and power budgets. We name Tiny Online Federated Learning (TinyOFL) for online federated learning using TinyML devices in the Internet of Things (IoT). This paper performs a comprehensive analysis of the effects of weight quantization in TinyOFL in terms of accuracy, stability, overfitting, communication efficiency, energy consumption, and delivery time, and extracts practical guidelines on how to apply the weight quantization to TinyOFL. Our analysis is supported by a TinyOFL case study with three Arduino Portenta H7 boards running federated learning clients for a keyword spotting task. Our findings include that in TinyOFL, a more aggressive weight quantization can be allowed than in online learning without FL, without affecting the accuracy thanks to TinyOFL's quasi-batch training property. For example, using 7-bit weights achieved the equivalent accuracy to 32-bit floating point weights, while saving communication bandwidth by 4.6×. Overfitting by increasing network width rarely occurs in TinyOFL, but may occur if strong weight quantization is applied. The experiments also showed that there is a design space for TinyOFL applications by compensating for the accuracy loss due to weight quantization with an increase of the neural network size.

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Implementation of a LoRa Mesh Library

Cited by 15 publications

References 16 publications

LoRaTRUST: Blockchain-Enabled Trust and Accountability Service for IoT Data

LoRaTRUST: Blockchain-Enabled Trust and Accountability Service for IoT Data

Position Paper: LoRa Mesh Networks for Enabling Distributed Intelligence on Tiny IoT Nodes

The Effects of Weight Quantization on Online Federated Learning for the IoT: A Case Study

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