# A Comparative Analysis for Optimizing Machine Learning Model Deployment in IoT Devices
## 1 Introduction  
In the rapidly evolving landscape of technology, the Internet of Things (IoT) has emerged as a foundational element, transforming various sectors by enabling the inter-connectivity of physical devices through the Internet. These devices, ranging from simple sensors to complex systems, collect and exchange data, facilitating a smarter and more responsive environment. Concurrently, Machine Learning (ML) has seen remarkable advancements, enhancing the capability of systems to learn from data, predict outcomes, and make informed decisions without explicit programming. The integration of ML with IoT has unlocked a vast array of opportunities, automating tasks traditionally requiring human intervention and offering more insightful and predictive analytics. The fusion of IoT with ML presents the transformative potential for developing intelligent solutions across diverse domains, including predictive maintenance, anomaly detection, and smart transportation. This integration, however, introduces a critical challenge for developers: the selection of the appropriate development approach for ML-based features within IoT solutions. The dilemma stems from a choice between leveraging traditional, high-level programming languages and libraries that simplify ML model development and opting for more specialized, low-level programming for nuanced control and customization. This decision is pivotal, impacting not only the feasibility and performance of the solution on the intended hardware but also its energy efficiency, execution time, and overall carbon footprint. Despite the clear benefits of integrating ML into IoT-based solutions, developers face significant hurdles in selecting the most suitable development approach for ML models. This challenge is compounded by the need to balance ease of development, performance requirements, hardware compatibility, and sustainability concerns. There is a conspicuous gap in guidance for developers navigating these complex considerations, leading to potential inefficiencies and suboptimal solutions. To address this challenge, we have proposed the development of a decision support system designed to aid developers in selecting the most appropriate approach for developing ML models within IoT-based solutions. This system will be grounded in an exhaustive comparison of available solutions, taking into account various problem-specific and environmental factors. Through a comprehensive experimental framework, we have assessed different ML development libraries across multiple dimensions, including performance metrics, execution time, memory utilization, power consumption, CPU temperature, and carbon footprint. This approach will enable the identification of trade-offs and optimal paths for different IoT-ML application scenarios. Our research contributes significantly to the field of IoT and Machine Learning integration via the following:  
* A Comparative Analysis of ML Development Libraries: We have provided an extensive comparative analysis between high-level and low-level programming libraries for developing ML models, catering to a range of IoT machine learning tasks. This analysis leverages two distinct datasets, each aligned with a common IoT ML image classification objective. * The Carbon Footprint Calculation Algorithm: A novel algorithm has been proposed, coined the green carbon footprint (GCF), to calculate the carbon footprint associated with the execution of ML training codes. This algorithm incorporates a comprehensive set of parameters, including execution time, memory utilization, power consumption, and CPU temperature. * The Decision Support System: Based on the exhaustive experimental findings, a decision support system has been synthesized, presenting key insights and recommendations for developers. This system aids in navigating the complex landscape of IoT-ML integration, highlighting optimal development approaches that balance performance, hardware compatibility, and environmental sustainability. The remainder of the paper is organized as follows.