Smart City Gnosys
Smart city article details
| Title | Developing Edge Ai For Embedded Systems |
|---|---|
| ID_Doc | 19453 |
| Authors | Nair R.R.; Babu T.; Ebin P.M. |
| Year | 2025 |
| Published | Embedded Artificial Intelligence: Real-Life Applications and Case Studies |
| DOI | http://dx.doi.org/10.1201/9781003481089-4 |
| Abstract | Embedded systems have been revolutionized by the inclusion of artificial intelligence (AI), making them capable. This chapter gives a complete review of Edge AI for embedded systems with regard to its importance, software frameworks, hardware platforms, applications, and use cases. The significance of Edge AI in this paper demonstrates how real-time decision-making can be facilitated by Edge AI as well as the security and privacy aspects improved through local data processing. Moreover, the paper touches on the challenges experienced while implementing AI on embedded devices as well as underscores the importance of an effective algorithm for it. This book chapter examines AI software development and hardware platforms, focusing on popular frameworks such as TensorFlow Lite, ONNX Runtime, and OpenVINO, as well as hardware accelerators like NVIDIA Jetson motherboards, Google Coral USB memory sticks, and Intel Movidius VPUs. Additionally, this section explains their features, performance metrics, and how they fit into different types of signatures. This chapter also examines many examples of advanced AI used in various industries such as healthcare, automotive industry, smart cities, agriculture, or construction. It explains how edge intelligence can be used to perform tasks such as predictive maintenance or troubleshooting in areas with limited resources. © 2025 selection and editorial matter, Arpita Nath Boruah, Mrinal Goswami, Manoj Kumar, Octavio Loyola-González; individual chapters, the contributors. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
21802  | 0.868 | Mahajan S.; Munirathinam S.; Raj P. | Edge Of Intelligence: Exploring The Frontiers Of Ai At The Edge | Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025) |
| 14842 | 0.867 | Grzesik P.; Mrozek D. | Combining Machine Learning And Edge Computing: Opportunities, Challenges, Platforms, Frameworks, And Use Cases | Electronics (Switzerland), 13, 3 (2024) |
| 21790 | 0.864 | Guo Y.; Qian C.; Song J.; Yu W. | Edge Intelligence In Cps | Edge Intelligence in Cyber-Physical Systems: Foundations and Applications (2025) |
| 13948 | 0.864 | Zhang Z.; Li F.; Lin C.; Wen S.; Liu X.; Liu J. | Choosing Appropriate Ai-Enabled Edge Devices, Not The Costly Ones | Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS, 2021-December (2021) |
| 21784 | 0.859 | Hou X.-P.; Lan L.; Tao C.-L.; Kou X.-Y.; Cong P.-J.; Deng Q.-X.; Zhou J.-L. | Edge Intelligence And Collaborative Computing: Frontiers And Advances; [边缘智能与协同计算: 前沿与进展] | Kongzhi yu Juece/Control and Decision, 39, 7 (2024) |
| 21762 | 0.858 | Chandrasekaran S.; Athinarayanan S.; Masthan M.; Kakkar A.; Bhatnagar P.; Samad A. | Edge Computing Revolution: Unleashing Artificial Intelligence Potential In The World Of Edge Intelligence | Edge of Intelligence: Exploring the Frontiers of AI at the Edge (2025) |
| 22694 | 0.857 | Martin J.; Cantero D.; González M.; Cabrera A.; Larrañaga M.; Maltezos E.; Lioupis P.; Kosyvas D.; Karagiannidis L.; Ouzounoglou E.; Amditis A. | Embedded Vision Intelligence For The Safety Of Smart Cities | Journal of Imaging, 8, 12 (2022) |
| 21736 | 0.854 | Anchitaalagammai J.V.; Kavitha S.; Buurvidha R.; Santhiya T.S.; Roopa M.D.; Sankari S.S. | Edge Artificial Intelligence For Real-Time Decision Making Using Nvidia Jetson Orin, Google Coral Edge Tpu And 6G For Privacy And Scalability | Proceedings of International Conference on Visual Analytics and Data Visualization, ICVADV 2025 (2025) |