Internet of Things (IoT) systems often consist of heterogeneous, resource-constrained devices that generate massive amounts of data. This data is important for assessments, behaviour analysis, and decision-making. However, IoT devices are also susceptible to cyber-attacks, such as information theft, personal device intervention, and privacy invasion. In case of an incident, these devices are subject to digital forensic investigation to identify and analyze crimes and misuse. Over the years, several forensic frameworks and techniques have been proposed to facilitate the investigation of IoT networks and devices, but finding a perfect solution that covers the diversity of IoT devices and networks is still a research challenge. In this study, we present a comparative analysis of existing forensic investigation frameworks and identify their strengths and weaknesses in handling forensic challenges of IoT devices. The study uses evaluation metrics of ten important parameters, including heterogeneity, scalability, and chain of custody, to thoroughly audit the effectiveness of these models. Our analysis concludes that the existing investigation frameworks do not cater to all requirements and aspects of IoT forensics. It further highlights the need for standard mechanisms to acquire and analyze digital artifacts in IoT devices.