Edge AI in IoT Systems: Reducing Latency and Power Usage

Abstract

The fast proliferation of the Internet of Things (IoT) systems has increased the need to process real-time data, make low-latency decisions, and design systems with low energy costs. Traditional cloud-based architectures do not usually satisfy these demands because of overburdening of the network, high transmission delays, and power consumption. In this regard, Edge Artificial Intelligence (Edge AI) has been implemented as a revolutionary solution and allows processing data and making intelligent decisions that are closer to the source of data. The role of Edge AI in the IoT systems is considered in this research paper with particular reference to its properties of latency reduction and power efficiency optimization.

The paper will examine the architectural change experienced as centralized cloud computing to distributed edge-based intelligence, the importance of on-device and near-device inference in eliminating the need to transmit data continuously. Edge AI can greatly reduce the response time because it processes data on the device, and is therefore well suited to applications with strong requirements on latency, including smart healthcare monitoring, autonomous systems, industrial automation, and smart cities. In addition, the paper examines the role of lightweight AI models, model compression methods, and hardware accelerators as an opportunity to decrease the energy consumption and increase the battery life of devices.

Incorporating both the literature analysis of the field and practical implementation, this paper defines the major challenges related to Edge AI application, such as a lack of computational resources, scalability of models, security, and interoperability of systems. The developing solutions, including federated learning, adaptive model optimization, and energy-aware scheduling strategies are also highlighted in the discussion. Altogether, the results indicate that Edge AI is a possible and sustainable solution when it comes to improving the performance of IoT through the simultaneous mitigation of the latency and the efficiency of power usage. Finally, the paper ends with future directions of research that are aimed at advancing the concept of integrating Edge AI in future IoT systems.