A Wearable Multi-sensor IoT Network System for Environmental Monitoring

Wu, Fan; Rüdiger, Christoph; Redouté, Jean-Michel; Yuce, Mehmet Rasit

doi:10.1007/978-3-030-02819-0_3

Cited by 13 publications

(12 citation statements)

References 9 publications

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“…The fourth group refers to studies focused on monitoring of CO 2 concentration with simple wearables [ 124 ] or wearables exchanging data with a Wireless Sensor Network (WSN) [ 91 , 125 ] or a LoRa network [ 126 ].…”

Section: Systematic Reviewmentioning

confidence: 99%

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Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

Salamone

Masullo

Sibilio

2021

Sensors

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The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term “smart” alongside the terms “watches”, “glasses”, and “jewellery”. Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

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Section: Systematic Reviewmentioning

confidence: 99%

“…Indoor activities such as cooking and cleaning further increased PM levels and formulated the air quality, while particulate accumulation was evident. [ 125 ] CO 2 (COZIR-GC0012) Thermal: air temperature, relative humidity (BME280). Pressure (BME280).…”

Section: Table A1mentioning

confidence: 99%

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

Salamone

Masullo

Sibilio

2021

Sensors

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“…Wu et al [52] Presented an environmental Internet of Things (IoT) low-power wearable sensor node, creating an XBee-based wireless sensor network (WSN). The wearable sensor node monitors environmental data and then transmits them via WSN to a remote cloud server.…”

Section: Smart Air Quality Monitoring (Saqm) Systemsmentioning

confidence: 99%

IoT for Smart Environment Monitoring Based on Python: A Review

Haji

Sallow

2021

AJRCoS

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Air pollution, water pollution, and radiation pollution are significant environmental factors that need to be addressed. Proper monitoring is crucial with the goal that by preserving a healthy society, the planet can achieve sustainable development. With advancements in the internet of things (IoT) and the improvement of modern sensors, environmental monitoring has evolved into a smart environment monitoring (SEM) system in recent years. This article aims to have a critical overview of significant contributions and SEM research, which include monitoring the quality of air , water pollution, radiation pollution, and agricultural systems. The review is divided based on the objectives of applying SEM methods, analyzing each objective about the sensors used, machine learning, and classification methods. Moreover, the authors have thoroughly examined how advancements in sensor technology, the Internet of Things, and machine learning methods have made environmental monitoring into a truly smart monitoring system.

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“…Manfreda et al [ 4 ] enforce that most monitoring systems are either ground-based, airborne, satellite-based, or a combination of them. For ground-based measurements, researchers should consider the possibility of using multi-sensor- and IoT-based wearable systems [ 5 , 6 , 7 ]. The combined data can be used to perform three-dimensional inferences from the acquired information [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Edge AI Applications for Ecological Environments

Silva

Delabrida

et al. 2021

Sensors

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Ecological environments research helps to assess the impacts on forests and managing forests. The usage of novel software and hardware technologies enforces the solution of tasks related to this problem. In addition, the lack of connectivity for large data throughput raises the demand for edge-computing-based solutions towards this goal. Therefore, in this work, we evaluate the opportunity of using a Wearable edge AI concept in a forest environment. For this matter, we propose a new approach to the hardware/software co-design process. We also address the possibility of creating wearable edge AI, where the wireless personal and body area networks are platforms for building applications using edge AI. Finally, we evaluate a case study to test the possibility of performing an edge AI task in a wearable-based environment. Thus, in this work, we evaluate the system to achieve the desired task, the hardware resource and performance, and the network latency associated with each part of the process. Through this work, we validated both the design pattern review and case study. In the case study, the developed algorithms could classify diseased leaves with a circa 90% accuracy with the proposed technique in the field. This results can be reviewed in the laboratory with more modern models that reached up to 96% global accuracy. The system could also perform the desired tasks with a quality factor of 0.95, considering the usage of three devices. Finally, it detected a disease epicenter with an offset of circa 0.5 m in a 6 m × 6 m × 12 m space. These results enforce the usage of the proposed methods in the targeted environment and the proposed changes in the co-design pattern.

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A Wearable Multi-sensor IoT Network System for Environmental Monitoring

Cited by 13 publications

References 9 publications

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

IoT for Smart Environment Monitoring Based on Python: A Review

Wearable Edge AI Applications for Ecological Environments

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