About AI Sensors

Aims

The rapid evolution of artificial intelligence (AI) has ushered in an era of unprecedented innovation across various industries. As AI continues to transform our world, the integration of AI sensors is becoming increasingly essential. AI Sensors (ISSN 3042-5999) aims to provide a platform for researchers, engineers, and innovators to explore and share their cutting-edge developments, insights, and breakthroughs in the field of AI sensing technologies, with a particular focus on edge computing and AIoT (AI and Internet of Things) sensing.

The field of AI sensors encompasses a wide range of technologies that enable machines to perceive, interpret, and interact with the world around them. With the rise of edge computing, these sensors are increasingly being deployed in decentralized environments, where data processing and decision making occur closer to the source of data. This paradigm shift is particularly relevant in the context of AIoT, where the convergence of AI and IoT is driving the development of smart, connected systems. AI Sensors aims to bridge the gap between the AI, IoT, and sensor communities, fostering collaboration and knowledge exchange to accelerate innovation in edge computing and AIoT sensing technologies.

Scope

This journal will cover a broad spectrum of topics related to AI sensors, with a specific emphasis on edge computing and AIoT sensing technologies, including but not limited to the following:

• Edge Computing with AI Sensors:
• Exploration of edge computing architectures and frameworks that integrate AI sensors, enabling real-time data processing, analysis, and decision making at the edge of the network.
• Techniques for optimizing the performance, energy efficiency, and scalability of AI sensors in edge computing environments.
• AIoT Sensing Technologies:
• Developments in AIoT sensing technologies that combine AI algorithms with IoT devices to create intelligent, autonomous systems capable of sensing, analyzing, and responding to environmental changes in real time.
• Applications of AIoT sensors in smart cities, healthcare, industrial automation, agriculture, and environmental monitoring.
• Distributed AI and Federated Learning:
• Advances in distributed AI and federated learning methodologies that leverage AI sensors and edge computing to train models across decentralized networks while preserving data privacy and security.
• AI-Enhanced Edge Analytics:
• Research on AI-enhanced edge analytics, where AI algorithms are used to process and analyze data collected by sensors at the edge, enabling timely and context-aware insights.
• Sensor Fusion in Edge Computing:
• Techniques for fusing data from multiple sensors in edge computing environments to enhance the accuracy, reliability, and robustness of AI-driven systems.
• Low-Power AI Sensing:
• Innovations in low-power AI sensing technologies that enable the deployment of AI sensors in energy-constrained environments, such as remote locations or wearable devices.
• Security and Privacy in Edge-AI Systems:
• Exploration of security and privacy challenges in edge-AI systems, including techniques for safeguarding sensitive data and ensuring the integrity of AI algorithms in decentralized environments.
• AI-Driven Optimization of IoT Networks:
• Development of AI-driven approaches for optimizing the performance and efficiency of IoT networks, including resource management, data routing, and network scalability.
• Case Studies and Applications:
• Real-world case studies and research articles showcasing the deployment of edge computing and AIoT sensing technologies in various industries, highlighting their impact on efficiency, productivity, and sustainability.

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Copyright / Open Access

Articles published in AI Sensors will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text:

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