advancements in convergence of ai and iot.pptx

convergence of ai and iot

1. Advancements, Insights, and Future Directions in AI-IoT Convergence HARSH BATAVIYA 12202110501017

2. Edge Computing and AI Integration Edge Computing allows data generated by IoT to be processed near its source rather than sending the data to a great distance to data centers or cloud. It's connected to dealing with persistent data near the data source, which is considered the 'edge' of the association. Edge AI is the combination of Edge computing and Artificial Intelligence. With Edge AI, AI algorithms are executed locally on a hardware device using the data collected from Edge computing. As the data is collected and processed in real-time, it reduces power consumption as well as data costs since the device doesn't need to be connected to the internet at all times.

3. Interoperability and Standardization Efforts Collaboration among industry stakeholders to establish interoperability standards and protocols for seamless integration and communication between AI and IoT devices. Standardization initiatives to address compatibility issues, promote data exchange formats, and facilitate interoperability across heterogeneous IoT ecosystems.

4. Emerging trends and applications of AI and IoT convergence include 1. Edge AI 2. Autonomous IoT systems 3. Predictive maintenance 4. Smart cities 5. Healthcare monitoring 6. Industrial automation 7. Intelligent transportation 8. Environmental monitoring 9. Agriculture optimization 10. Energy management

5. Research Challenges and Opportunities Tackling research obstacles in AI-IoT convergence, such as scalability, energy efficiency, reliability, and the credibility of AI-powered IoT systems. Exploring interdisciplinary research chances where AI, IoT, cybersecurity, privacy, and human-computer interaction intersect, to unleash the complete capabilities of smart IoT solutions.