About this Research Topic
The Internet of Things (IoT) has experienced exponential growth in recent years, resulting in the deployment of billions of devices in our surroundings. This has generated a vast amount of heterogeneous data, which has pushed the limits of traditional distributed and cloud computing systems. However, these large-scale distributed systems are located far from the data-generating devices and use a centralized architecture, leading to challenges such as latency, bandwidth, and privacy concerns. Therefore, there is an increasing need to shift intelligence and decision-making to the edge of the network, where data-generating devices are located. This approach is known as edge intelligence, which aims to bring machine learning and artificial intelligence capabilities to edge devices for real-time data analysis, computing, and decision-making.
The decentralized nature of edge intelligence offers several advantages, such as reducing response time, lowering network traffic and congestion, optimizing resource utilization, improving bandwidth, and enhancing privacy. These advantages can be beneficial in various application domains, including smart cities, smart health, intelligent transportation systems, smart grids, and manufacturing. Overall, edge intelligence provides a promising solution to the challenges posed by the exponential growth of IoT devices and the increasing need for real-time data analysis and decision-making.
The goal of this Research Topic is to bring together researchers, academicians and professionals working in edge intelligence to come up with smart solutions and innovative ideas which can be helpful in the implementation and adoption of edge intelligence across various application domains. The special issue invites original research articles, case studies, and reviews that address different aspects of edge intelligence, including technical and operational challenges, privacy and security concerns, machine learning algorithms for edge devices, resource optimization techniques, and real-time decision-making systems. It is expected that the articles published in this special issue will contribute to advancing the state-of-the-art in edge intelligence.
The scope of this Research Topic includes but is not limited to:
- Machine learning models for edge intelligence
- Intelligent edge architectures
- Federated learning and collaborative intelligence
- Energy efficiency in edge intelligence
- Security, privacy and data integrity in edge intelligence
- Intelligent edge data analytics and processing
- Lightweight learning algorithms for edge devices
- Edge resource management and optimization
- Blockchain and edge intelligence
- Networking and communication in edge intelligence
- Edge intelligence applications
- Performance evaluation and benchmarking in edge intelligence
The decentralized nature of edge intelligence offers several advantages, such as reducing response time, lowering network traffic and congestion, optimizing resource utilization, improving bandwidth, and enhancing privacy. These advantages can be beneficial in various application domains, including smart cities, smart health, intelligent transportation systems, smart grids, and manufacturing. Overall, edge intelligence provides a promising solution to the challenges posed by the exponential growth of IoT devices and the increasing need for real-time data analysis and decision-making.
The goal of this Research Topic is to bring together researchers, academicians and professionals working in edge intelligence to come up with smart solutions and innovative ideas which can be helpful in the implementation and adoption of edge intelligence across various application domains. The special issue invites original research articles, case studies, and reviews that address different aspects of edge intelligence, including technical and operational challenges, privacy and security concerns, machine learning algorithms for edge devices, resource optimization techniques, and real-time decision-making systems. It is expected that the articles published in this special issue will contribute to advancing the state-of-the-art in edge intelligence.
The scope of this Research Topic includes but is not limited to:
- Machine learning models for edge intelligence
- Intelligent edge architectures
- Federated learning and collaborative intelligence
- Energy efficiency in edge intelligence
- Security, privacy and data integrity in edge intelligence
- Intelligent edge data analytics and processing
- Lightweight learning algorithms for edge devices
- Edge resource management and optimization
- Blockchain and edge intelligence
- Networking and communication in edge intelligence
- Edge intelligence applications
- Performance evaluation and benchmarking in edge intelligence
Keywords: Edge Intelligence, Edge Computing, Artificial Intelligence, Internet of Things, Decentralized Systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
