Next Generation of Measurement Sensors and Instruments Based on Embedded Artificial Intelligence
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (30 August 2024) | Viewed by 4584
Special Issue Editors
Interests: IoT; AR/VR-based distributed measurement systems; electrical and electronics engineering; measurement; signal processing; wireless sensor networks; embedded artificial intelligence; edge AI
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine learning; deep learning; edge computing; data science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The combined use of the Internet of Things (IoT) and artificial intelligence (AI) has opened new, previously unimaginable opportunities. AI-based applications have resulted in being effective and flexible in disparate domains. IoT increases the value of AI through the ability to collect data with training models and algorithms, and vice versa, AI increases the value of IoT thanks to the transformation of collected data into useful information. In general, IoT and AI are physically distant from each other: IoT is close to the data source; AI still often runs into the cloud. This conventional cloud-based architecture can have critical issues, such as high latency times, lack of bandwidth, data loss, cloud congestion, and energy costs. These factors greatly reduce the user experience or, in the worst case, do not allow the implementation of AI-based real-time applications. To speed up the response, data produced by sensors and IoT devices must be analyzed directly at the edge, close to where the data are located and near the user, rather than being sent to a central location for later analysis. This new model is commonly identified as edge or embedded AI. It is possible to consider embedded artificial intelligence as an evolution of the IoT, by means of which connected objects are no longer just terminals intended to collect data to be conveyed to the cloud but natively exhibit characteristics peculiar to AI technologies. Embedded AI can be exploited to enhance both static and dynamic metrological characteristics of smart sensors, overcoming typical limitations they suffer from, and will find applications in numerous scenarios: industry, smart cities, smart agriculture, smart monitoring, etc. In the instrumentation and measurement field, for example, by leveraging embedded AI, meters will be able to manage better loads based on grid conditions and provide proactive recommendations in real time.
The purpose of this Special Issue is to gather a collection of papers reflecting the latest developments in the design of the next generation of measurement sensors and instruments that use embedded artificial intelligence, such as machine learning, Bayesian networks, neural networks, and fuzzy logic, to improve and enhance sensing, metering, and detection.
Dr. Francesco Bonavolontà
Prof. Dr. Flora Amato
Guest Editors
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Keywords
- AI-powered instruments
- AI-enabled smart sensing and monitoring
- human activity recognition
- acoustic scene classification
- fault detection and recognition
- edge artificial intelligence
- AI-enabled smart applications
- predictive maintenance in manufacturing
- embedded intelligence
- visual recognition
- AI for health
- AI-enhanced smart sensors
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