# Research on Digital Forensics Analyzing Heterogeneous Internet of Things Incident Investigations
## 2 Related Work
### Challenges of Internet of Things Digital Forensics  
Regarding IoT services and their forensic implications, the challenges arising from the diversity of devices and the intricacy of data retrieval are of the utmost importance. Figure 1 provides an illustrative framework of a smart-home ecosystem, encompassing the multitiered IoT architecture. The framework commences with sensor devices that gather environmental data, progressing through the network layer, which employs protocols (e.g., Zigbee, Bluetooth, Z-Wave, and Wi-Fi) to establish broader connectivity [17, 18]. The diagram culminates at the smart hub, operating as the central node facilitating communication between physical devices and the user-interface application. Furthermore, the hub extends into the cloud to enhance data processing and storage capabilities. This layered architecture mirrors the operational dynamics of IoT systems, underscoring the potential forensic challenges that arise due to the decentralized and diverse nature of IoT environments. These challenges are explored in the subsequent sections of this research. Consequently, the schematic in Figure 1 is crucial for comprehending the interconnected IoT landscape and the ensuing digital forensic hurdles within smart-home networks. The process of obtaining data from IoT devices presents distinctive challenges, due to the limited resources and diverse operational environments of these devices [19]. Often, IoT devices use different proprietary data storage mechanisms as a result of their constrained resources. This diversity, along with limitations in memory, battery life, and network bandwidth, makes data access and retrieval more complicated [20]. Moreover, IoT devices typically have limited memory and processing capabilities, despite having various sensors and functionalities. This limitation becomes problematic as these systems continuously operate and generate substantial volumes of data from numerous sensors, leading to rapid data overwriting. Transferring data to external storage devices as a workaround is hindered by the risk of data integrity loss during transmission. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sector** & **Service Type** & **Application** & **Technology** \\ \hline \multirow{4}{*}{Private} & Home & Home automation & Smart devices \\  & Health care & Remote patient monitoring & Wearable devices \\  & Agriculture & Precision farming & Sensor networks \\ \hline \multirow{4}{*}{Public} & Safety & Smart city & Emergency response \\  & Environmental & Pollution monitoring & Data analytics \\  & Energy & Energy management & Smart grids \\  & Transportation & Connected car & V2X communication \\ \hline \multirow{2}{*}{Industrial} & Manufacturing & Predictive maintenance & Sensors \\  & Defense & Mobile wearable networks & Secure networks \\ \hline \hline \end{tabular} Note: V2X: vehicle to everything.  
\end{table} 
Table 1: Classification of Internet of Things services and their applications and technology elements. The heterogeneity of IoT devices encompasses a wide range of products, including web cameras, digital locks, routers, and thermostats, and adds another layer of complexity. The variety of data formats and types of evidence necessitates unique methodologies for collecting and analyzing data for each device type [21]. Specialized tools and techniques for extracting and interpreting data from these diverse systems have been developed. However, questions regarding their reliability and the potential risk of compromising data integrity during the collection process persist. Last, the distributed nature of IoT data across multiple platforms, extending beyond the devices to cloud network areas, introduces another level of complexity. The data stored in the cloud and their restricted usage within the network and IoT devices present formidable challenges in integrating data across these distributed ecosystems [22; 23].