This chapter explores a unified framework that leverages both edge computing and cloud computing for AI inference in big data environments. Edge computing enables near-source, low-latency processing and decision-making, reducing dependency on centralized infrastructure, while cloud platforms...

The 21 st century has increased the rate of urbanization, thus making the problems of traffic snarl up, pollution, energy crisis, improper waste disposal and heavy load on the social safety and health system more complex. The idea of the smart city became one of the new technologies that...

The proliferation of IoT devices creates massive real-time data, which creates challenges for centralized AI in terms of latency, scalability and privacy. Federated AI inference supports distributed model training and inference on the edge devices as well as keeping data confidential while using...

This chapter explores the strategic implementation of AI inference in the edge-cloud continuum, focusing on the downsides of cloud-based processing and the latency, bandwidth, and scalability advantages of edge-based processing. It describes foundational architectures—edge, fog, and hybrid...

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has offered the legality of big data analysis that could completely transform sectors such as healthcare, transportation, smart cities/industrial automation. However, the integration of AI raises important security...

The integration of Artificial Intelligence (AI) inference into the Internet of Things (IoT) ecosystem is reshaping the landscape of intelligent systems by enabling real-time analytics, predictive modeling, and autonomous decision-making directly at the device and network levels. Unlike...

The paper will explore how AI inference on IoT and Big Data is transformative with respect to the health care sector, manufacturing, smart cities, energy, and agriculture. AI inference transforms sensor data into actionable intelligence through the combination of real-time data, predictive...

This chapter addresses the challenge of transforming heterogeneous, noisy, and incomplete dataflows into actionable and trustworthy decisions. While machine learning has advanced predictive accuracy, it struggles with interpretability, semantic integration, and generalizability in high-stakes...

Cities are becoming smarter by leveraging technology to tackle traffic congestion, pollution, energy use, and healthcare. Central to this shift is the integration of Artificial Intelligence (AI) inference, Internet of Things (IoT) devices, and Big Data analytics. These technologies allow massive...

The integration of Artificial Intelligence, Big Data analytics and the Internet of Things is reshaping real-time decision-making, creating the need for strong ethical and regulatory safeguards. This chapter explores equitable AI deployment, privacy protection in continuous data flows, and bias...