Channel characterisation for wearable LoRaWAN monitors

Catherwood, Philip A.; McComb, Stephen; Little, Michael J.; McLaughlin, James W.

doi:10.1049/cp.2017.0273

Cited by 13 publications

(17 citation statements)

References 11 publications

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“…The study of the influence of the main indicators of the system on the overall system performance was studied in [11][12][13]. For example, the authors of [11] studied the effect of the spreading factor on the packet time on air.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, the authors of [11] studied the effect of the spreading factor on the packet time on air. In [12], a generalized probability density of a radio channel for portable devices was obtained. In [13], some limitations of the system are presented and a brief comparison with similar technologies, such as a system with channel separation by time, navigation devices, smart radio, and other meters.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In (12), C(−z) = −C(z), S(−z) = −S(z) is taken into account. In the calculations for z 1, you can use the following relations…”

Section: Spectrummentioning

confidence: 99%

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Processing Signals in the Receiving Channel for the LoRa System

Kucherov

Berezkin²,

Onikienko

et al. 2020

Data-Centric Business and Applications

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The chapter devoted studying some characteristics of signal used in receiving devices by LoRa technology. The main feature of this technology relates the ability to build wireless networks for transmitting short messages over long distances under the condition of the long-life battery. Our investigation focused on the impact of interference on the functioning of the receiving set. With this purpose, we studied the basic characteristics of the received radio signal, its frequency properties, the possibilities of spreading the spectrum, encoding and recovering the transmitted information, receiving a packet of individual pulse signals and the quality of packet processing as a whole, and the effect of multipath propagation. We also proposed an approach for studying a receiver in highly populated areas with an estimate of the calculating and experimental data. To check the quality of processing the received information the studies were carried out according to the "point-to-point" and "point-gateway" schemes under the remote server. The test results follow.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Processing Signals in the Receiving Channel for the LoRa System

Kucherov

Berezkin²,

Onikienko

et al. 2020

Data-Centric Business and Applications

View full text Add to dashboard Cite

show abstract

“…Once the technology was selected, we decided to use LoRaWAN, which uses LoRa as a physical layer. LoRaWAN networks have previously been employed in the literature in other domains, for example, in an experimental performance evaluation of LoRaWAN over a real environment in Bangkok by Vatcharatiansakul et al [ 80 ] and in a long range wide area network-based smart pest monitoring system by Yu et al [ 81 ], among others [ 82 ].…”

Section: System Proposedmentioning

confidence: 99%

Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization

Lozano

Caridad

Paz

et al. 2018

Sensors

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New solutions for managing waste have emerged due to the rise of Smart Cities and the Internet of Things. These solutions can also be applied in rural environments, but they require the deployment of a low cost and low consumption sensor network which can be used by different applications. Wireless technologies such as LoRa and low consumption microcontrollers, such as the SAM L21 family make the implementation and deployment of this kind of sensor network possible. This paper introduces a waste monitoring and management platform used in rural environments. A prototype of a low consumption wireless node is developed to obtain measurements of the weight, filling volume and temperature of a waste container. This monitoring allows the progressive filling data of every town container to be gathered and analysed as well as creating alerts in case of incidence. The platform features a module for optimising waste collection routes. This module dynamically generates routes from data obtained through the deployed nodes to save energy, time and consequently, costs. It also features a mobile application for the collection fleet which guides every driver through the best route—previously calculated for each journey. This paper presents a case study performed in the region of Salamanca to evaluate the efficiency and the viability of the system’s implementation. Data used for this case study come from open data sources, the report of the Castilla y León waste management plan and data from public tender procedures in the region of Salamanca. The results of the case study show a developed node with a great lifetime of operation, a large coverage with small deployment of antennas in the region, and a route optimization system which uses weight and volume measured by the node, and provides savings in cost, time and workforce compared to a static collection route approach.

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“…This principle has been proven already with smart implanted insulin pumps which actively monitor blood glucose levels and automatically medicate to exacting levels accordingly [25]. Further emerging technologies such as stretchable electronics [26] and long range low power communication systems [27] increase the potential for effective technology deployment.…”

Section: Opportunitiesmentioning

confidence: 99%

Artificial Intelligence for Long-term Respiratory Disease Management

Catherwood

Rafferty

McLaughlin

2018

Electronic Workshops in Computing

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This paper presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal/external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realise new treatment paradigms. The application of AI is clearly not specific to respiratory disease management; however it is a chronic disease that requires ongoing monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasises the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.

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Channel characterisation for wearable LoRaWAN monitors

Cited by 13 publications

References 11 publications

Processing Signals in the Receiving Channel for the LoRa System

Processing Signals in the Receiving Channel for the LoRa System

Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization

Artificial Intelligence for Long-term Respiratory Disease Management

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