On the Coverage of Bus-Based Mobile Sensing

Caminha, Pedro Henrique Cruz; Couto, Rodrigo S.; Costa, Luís Henrique M. K.; Fladenmuller, Anne; Amorim, Marcelo Dias de

doi:10.3390/s18061976

Cited by 21 publications

(7 citation statements)

References 21 publications

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“…The use of mobile measurement systems is a common practice too [8]. MoreAir is an IoT platform that comes in the form of nomadic or mobile sensor nodes, developed to collect data related to the quality of the urban environment [19].…”

Section: Low-cost Air Quality Measurement Approachesmentioning

confidence: 99%

Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring

Muntean¹,

Moiş²,

Folea³

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data.

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Section: Low-cost Air Quality Measurement Approachesmentioning

confidence: 99%

Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring

Muntean¹,

Moiş²,

Folea³

2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Urban mobility services have as their essence the use of IoT technologies. Some research focuses on proposing a model to select the correct subset of buses that maximizes the coverage of a city [ 17 ]. Others solve linear optimization problems related to vehicles that follow predetermined routes and, as a solution, propose strategies that use heuristics [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

Ferreira

Azevedo

Estrella

et al. 2023

Sensors

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With the development of mobile communications and the Internet of Things (IoT), IoT devices have increased, allowing their application in numerous areas of Industry 4.0. Applications on IoT devices are time sensitive and require a low response time, making reducing latency in IoT networks an essential task. However, it needs to be emphasized that data production and consumption are interdependent, so when designing the implementation of a fog network, it is crucial to consider criteria other than latency. Defining the strategy to deploy these nodes based on different criteria and sub-criteria is a challenging optimization problem, as the amount of possibilities is immense. This work aims to simulate a hybrid network of sensors related to public transport in the city of São Carlos - SP using Contiki-NG to select the most suitable place to deploy an IoT sensor network. Performance tests were carried out on five analyzed scenarios, and we collected the transmitted data based on criteria corresponding to devices, applications, and network communication on which we applied Multiple Attribute Decision Making (MADM) algorithms to generate a multicriteria decision ranking. The results show that based on the TOPSIS and VIKOR decision-making algorithms, scenario four is the most viable among those analyzed. This approach makes it feasible to optimally select the best option among different possibilities.

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“…Recently, drive-by sensing has emerged as a popular approach for air quality monitoring due to its dynamic nature, extensive spatial coverage, and reduced operational costs while providing high-resolution data [6] [7]. Different vehicles equipped with low-cost sensors have been proposed as mobile platforms for air quality monitoring.…”

Section: Introductionmentioning

confidence: 99%