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| Title | Edge Intelligence Paradigm Shift On Optimizing The Edge Intelligence Using Artificial Intelligence State-Of-The-Art Models |
|---|---|
| ID_Doc | 21792 |
| Authors | Chandrasekaran S.; Athinarayanan S.; Masthan M.; Kakkar A.; Bhatnagar P.; Samad A. |
| Year | 2024 |
| Published | Advancing Intelligent Networks Through Distributed Optimization |
| DOI | http://dx.doi.org/10.4018/979-8-3693-3739-4.ch001 |
| Abstract | Edge AI changes data processing (EI) strategy incorporating powerful AI capabilities directly at the network's edge, where devices and sensors gather data. This convergence solves cloud-centric AI's latency and bandwidth challenges by enabling real-time analysis and decision-making. Three paradigm-shifting effects of edge intelligence include localized processing allowing real-time data analysis and quick responses. Second, minimizing large-scale data transfers to centralized cloud services greatly minimizes latency. Third, edge intelligence's distributed architecture reduces centralized system demand, boosting infrastructure resilience and scalability and enabling new developments. Imagine smart cities using edgebased analysis to enhance traffic flow or autonomous cars making quick sensorbased choices. Data at the network edge needs security. AI algorithms must work on low-resource edge devices. Edge intelligence can solve these issues and enable near-instantaneous network decision-making, changing numerous industries. Edge computing (EC) solved this problem by moving processing closer to data sources, but edge intelligence introduced AI at the network edge. This unique solution uses AI to increase edge device capabilities beyond data processing. A network edge device can learn, decide, and infer in real-time with EI. Integration reduces latency, boosts efficiency, and empowers intelligent devices. This chapter covers the need for solid EI infrastructure and cutting-edge AI models to optimize the edge device (ED). AI's astounding results in numerous fields have inspired EC researchers to investigate its potential, particularly in machine learning (ML), a fast-growing discipline of AI. This chapter defines EC and explains its popularity. EC's fundamental issues and flaws in traditional methods are then examined. This chapter shows research on optimizing EC and applying AI within the EC framework for different areas to stimulate new research lines that exploit AI and EC's synergy. © 2024 by IGI Global. All rights reserved. |
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