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@article{198032,
        author = {Dr. Asmita Namjoshi and Sagar Balu Sangale},
        title = {AI in Edge Computing and IoT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11011-11014},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198032},
        abstract = {The integration of Artificial Intelligence (AI) with Edge Computing and the Internet of Things (IoT) is revolutionizing real-time data processing by reducing latency, optimizing resource utilization, and enhancing security. Traditional cloud-based AI systems often suffer from high bandwidth consumption, increased latency, and potential privacy risks. By shifting AI-driven computations to edge devices, data is processed locally, enabling faster decision-making and reducing the need for continuous cloud connectivity. This paradigm is critical for applications requiring immediate responses, such as smart surveillance, autonomous vehicles, industrial automation, healthcare monitoring, and smart cities. AI at the edge enhances predictive analytics, anomaly detection, and adaptive learning, making IoT systems more autonomous and efficient. Additionally, edge AI reduces network congestion and operational costs while ensuring data privacy and security. As AI models become more optimized for low-power devices, the synergy between AI, edge computing, and IoT will drive the development of intelligent, scalable, and responsive systems across various industries. Moreover, the integration of AI with edge computing and IoT fosters a decentralized intelligence framework, where devices can collaboratively learn and adapt to changing environments without relying on constant cloud updates. This enables federated learning techniques, where AI models are trained locally on multiple-edged devices, ensuring improved personalization while maintaining data privacy. Furthermore, advancements in hardware acceleration, such as AI-specific edge processors and neuromorphic computing, are enhancing the efficiency of on-device machine learning. These innovations pave the way for new possibilities in real-time automation, self- healing networks, and energy-efficient smart systems, making AI-driven edge computing a cornerstone of future technological ecosystems.},
        keywords = {Artificial Intelligence (AI), Edge Computing, Internet of Things (IoT), Real-time Data Processing, Latency Reduction, Resource Optimization},
        month = {April},
        }