Sarcouncil Journal of Engineering and Computer Sciences

Abstract: This article presents a comprehensive architectural framework for implementing robust logging systems in heterogeneous embedded environments, with particular focus on automotive and IoT applications. The article addresses the unique challenges posed by diverse operating systems, hardware configurations, and communication protocols found in modern embedded systems, proposing innovative approaches to data capture, consolidation, and analysis. The architecture employs protocol-specific collectors and domain-specific adapters to gather diagnostic information across system boundaries, while implementing unified format transformation and cross-domain synchronization to enable holistic analysis. Implementation strategies balance the competing demands of performance impact, diagnostic completeness, and resource utilization through adaptive logging techniques and fault-tolerant design patterns. Advanced diagnostic capabilities, including secure extraction methodologies and sophisticated visualization frameworks, transform raw log data into actionable insights that significantly accelerate issue resolution. Through detailed case studies in automotive and industrial IoT contexts, the research validates the practical applicability of the proposed architecture, demonstrating substantial improvements in diagnostic efficiency and system reliability. The article concludes by examining emerging research directions in machine learning integration, edge computing coordination, and privacy-preserving logging techniques, establishing a roadmap for future advancements in this critical domain of embedded systems engineering.