Rademics Research Institute

Abstract

This chapter explores the optimization of transfer learning techniques tailored for resource-constrained IoT devices, focusing on enhancing model efficiency and performance while minimizing energy consumption. As the proliferation of IoT applications increases, the necessity for intelligent and adaptive analytics becomes paramount, particularly in low-power environments. Key strategies discussed include lightweight model architectures, energy profiling, and edge-aware optimizations that collectively address the unique challenges posed by IoT constraints. Additionally, the integration of hardware-specific design considerations, such as energy-efficient processors and memory management, plays a critical role in enabling effective deployment of transfer learning models. By leveraging federated learning and adaptive inference strategies, this chapter highlights innovative approaches to ensure sustainable and scalable IoT solutions. The insights provided herein contribute to the ongoing development of robust IoT analytics, bridging the gap between advanced machine learning techniques and practical implementation in low-power scenarios.

Introduction

The rapid expansion of the Internet of Things (IoT) has ushered in a new era of interconnected devices, generating vast amounts of data that require advanced analytical techniques for meaningful insights [1-3]. As IoT applications proliferate across various sectors, including healthcare, smart cities, and industrial automation, the need for efficient and adaptive analytics becomes increasingly critical [4]. Traditional machine learning methods often fall short in addressing the unique challenges posed by IoT environments, particularly in terms of resource constraints [5,6]. Therefore, transfer learning has emerged as a promising approach, enabling models to leverage pre-existing knowledge to improve performance in specific tasks with limited data [7].

Implementing transfer learning in resource-constrained IoT devices presents significant challenges [8]. These devices typically have limited computational power, memory, and energy resources, which can hinder the deployment of complex machine learning models [9]. The integration of transfer learning techniques must be carefully designed to optimize resource usage while ensuring that the models remain effective [10]. This necessitates the development of lightweight architectures that can efficiently utilize available resources without compromising accuracy [11,12]. Exploring model compression methods, such as pruning and quantization, was essential in this context, as facilitate the adaptation of large pre-trained models for deployment on low-power devices [13].