Connected internet of things devices are becoming more powerful, yet the chal-lenge of effectively managing them to prevent failures remains ongoing. In somescenarios, such as with devices produced by a single company, it is possibleto use the same fault detection provided by the manufacturer and follow theinstruction for resolving the threat. In other cases, such as with devices producedby different companies, the heterogeneity of devices necessitates a more detailedand complex approach to fault detection. Log files, which are records of eventsor processes generated by a device’s software or hardware, are crucial for moni-toring device behavior. It is important to consider the diversity of log files anddata to detect threats, identify their root causes, and provide effective solutions.In the realm of troubleshooting interconnected internet of things devices, currentsolutions predominantly address homogeneous device environments, whichlimits their scalability and adaptability to diverse device types and configura-tions. Instead, a more flexible approach is needed; one that can accommodatea variety of connected devices while minimizing reliance on specific companyinstructions. One such method is Smart-troubleshooting which involves a 4-stepcycle which include prevention, detection and diagnosis, recovery, and evolutionof threats. Given these premises, the ultimate goal of this research is to definea smart-troubleshooting approach based on log files and product information.By leveraging a generalized methodology, this approach seeks to enhance themanagement of internet of things systems in complex, multi-manufacturer en-vironments. This thesis focuses on a systematic review of log files and thestate of the art in troubleshooting methodologies. During the research, thescarcity of publicly available log files for troubleshooting purposes was identified. Consequently, a method was proposed for generating synthetic log filesusing generative adversarial networks. The proposed methodology leveragesthese log files along with product information to enhance smart-troubleshooting.To validate the approach and gather industry feedback, questionnaires and in-terviews was conducted. Following this, machine learning algorithms will beemployed to implement and refine the proposed method. By leveraging a gener-alized methodology, this approach seeks to improve the management and faultdetection of internet of things systems in complex, heterogeneous environments.