Independent Researcher.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(02), 1680-1691
Article DOI: 10.30574/wjarr.2023.19.2.1685
Received on 13 July 2023; revised on 22 August 2023; accepted on 24 August 2023
Revolutionizing data processing, security and real-time decision making, the move to IoT edge intelligence is advancing the state of the art in how we approach these and all challenges of modern business. Latency, bandwidth constraints, security vulnerability are the traditional pain points of traditional cloud-based service models, edge computing is a critical solution. The IoT systems can be made more responsive, better able to utilize resources more effectively, and more secure by way of integrating ML driven visualization and edge AI strategies. Nevertheless, there are still some challenges about this such as scaling, data privacy, and computational efficiency. These risks can be mitigated with the solutions like federated learning, blockchain integration and then the anomaly detection, and all that data can actually flow seamlessly and securely. Edge AI takes the best of centralized cloud along with cost efficiency of distributed systems and results in reducing dependence on centralized cloud infrastructure, and optimizing data processing by doing the computation locally to lower latency and save bandwidth. Furthermore, ML based visualization tools help in making IoT applications efficient for smart cities, health-care and industrial automation domains. Though the technology was developed years ago, security continues to be a key consideration as blockchain technology ensures secure, tamper proof data management, while federated learning ensures that data is private because it is decentralized during training. It is expected that later IoT edge intelligence can be advanced further from emerging technology such as quantum computing and AI driven automation. Such advancements will enable more scalable, secure and efficient processing frameworks that would lead to making intelligent, autonomous decisioning in the real time environment. As organizations adopt the edge AI solutions, it is important to address their current limitations and exploit the future innovation for the further growth and efficiency of IoT ecosystems.
Iot Edge Intelligence; Machine Learning; Edge AI; Federated Learning; Blockchain Security; Real-Time Data Processing; Quantum Computing; ML-Driven Visualization; Scalability; Anomaly Detection; Smart Cities; Cloud Computing Alternatives
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Natarajan Sankaran. Enhancing IoT edge intelligence: Machine learning-driven visualization for smart cities decision-making. World Journal of Advanced Research and Reviews, 2023, 19(2), 1680-1691. Article DOI: https://doi.org/10.30574/wjarr.2023.19.2.1685