Air Quality Monitoring System and Benchmarking

Liu, Xiufeng; Nielsen, Per Sieverts

doi:10.1007/978-3-319-64283-3_34

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…In recent years, we have experienced the rapid growth and adoption of IoT-based systems in multiple areas of our lives. It is common to see IoT devices improving healthcare systems [1], being used in autonomous vehicles [2], enabling smart city solutions [3], enhancing environmental monitoring [4,5], or powering smart wearable equipment [6], just to name a few scenarios. In all mentioned cases, the IoT devices are responsible for generating massive volumes of data, either in numerical form for various sensor readings or images and videos from camera-equipped devices.…”

Section: Introductionmentioning

confidence: 99%

Combining Machine Learning and Edge Computing: Opportunities, Challenges, Platforms, Frameworks, and Use Cases

Grzesik,

Mrozek

2024

Electronics

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In recent years, we have been observing the rapid growth and adoption of IoT-based systems, enhancing multiple areas of our lives. Concurrently, the utilization of machine learning techniques has surged, often for similar use cases as those seen in IoT systems. In this survey, we aim to focus on the combination of machine learning and the edge computing paradigm. The presented research commences with the topic of edge computing, its benefits, such as reduced data transmission, improved scalability, and reduced latency, as well as the challenges associated with this computing paradigm, like energy consumption, constrained devices, security, and device fleet management. It then presents the motivations behind the combination of machine learning and edge computing, such as the availability of more powerful edge devices, improving data privacy, reducing latency, or lowering reliance on centralized services. Then, it describes several edge computing platforms, with a focus on their capability to enable edge intelligence workflows. It also reviews the currently available edge intelligence frameworks and libraries, such as TensorFlow Lite or PyTorch Mobile. Afterward, the paper focuses on the existing use cases for edge intelligence in areas like industrial applications, healthcare applications, smart cities, environmental monitoring, or autonomous vehicles.

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