A Multi-Layered Computational Framework for Secure Edge-AI Integration in Distributed IoT Networks
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V1I2P101Keywords:
Edge-AI, Iot Networks, Distributed Computing, Data Security, Multi-Layered Framework, Real-Time Analytics, Edge Computing, Machine Learning, Secure Data Aggregation, Latency OptimizationAbstract
Internet of Things (IoT) devices are growing exponentially, which has amplified the demand of effective, secure, and smart systems of data processing. The legacy cloud-based solutions cannot significantly manage the big influx of data because of latency, bandwidth limits, and security risks. The proposed paper will suggest a multi-layered framework of computation that incorporates Edge-AI in distributed IoT networks, to overcome these issues. The suggested architecture will make use of hierarchical edge computing, secure data aggregation, and AI-based analytics to guarantee real-time decision making, improved data privacy, and maximum resource usage. We give an in-depth analysis of the construction of the framework, its parts, and prove their effectiveness in the form of experimental simulations. Comparative analyses with traditional models paint the best picture of the superiority of the framework in the case of the latency minimization, throughput efficiency and security resistance
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