# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML
#### 2.2.1 TinyML as a Novel Facilitator in IoT Big Data Management  
Within this challenging landscape, TinyML emerges as an innovative intersection between machine learning and embedded systems. Specifically tailored for resource-constrained devices, it presents several avenues for mitigating Big Data challenges: \begin{table}
\begin{tabular}{l l} \hline \hline
**Challenge** & **Description** \\ \hline \multirow{3}{*}{Data Volume} & The increase in interconnected devices leads to unprecedented \\  & data generation, surpassing the capacity of conventional storage \\  & and processing systems. \\ \multirow{3}{*}{Data Velocity} & Continuous data generation in IoT necessitates real-time analysis \\  & and response, stressing the need for prompt processing solutions. \\ \multirow{3}{*}{Data Variety} & Diverse data sources in IoT range from structured to unstructured \\  & formats, posing integration and analytical complexities. \\ \multirow{3}{*}{Data Vercacity} & The accuracy, authenticity, and reliability of data from varied \\  & devices present significant challenges in data verification. \\ \multirow{3}{*}{Data Integration} & Consolidating data from heterogeneous sources while preserving \\  & integrity and context remains complex. \\ \multirow{3}{*}{Security} & Increased interconnectivity broadens the risk of cyber attacks, \\  & necessitating robust security protocols. \\ \multirow{3}{*}{Privacy} & Balancing the protection of sensitive data within extensive \\  & datasets, while maintaining utility, is crucial. \\ \multirow{3}{*}{Latency} & Processing or transmission delays can affect the timeliness and \\  & relevance of insights, impacting decisionmaking. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of Big Data challenges in IoT. * **Localized On-Device Processing**: TinyML facilitates local data processing, markedly reducing the need for continuous data transfers, thus optimizing network bandwidth and improving system responsiveness. * **Intelligent Data Streamlining**: With the ability to perform preliminary on-device analysis, TinyML enables IoT systems to discern and selectively transmit pivotal data, ensuring efficient utilization of storage resources. * **Adaptive Learning Mechanisms**: IoT devices embedded with TinyML can continuously refine their data processing algorithms, fostering adaptability to dynamic data patterns and environmental changes. * **Reinforced Security Protocols**: By integrating real-time anomaly detection at the device level, TinyML significantly enhances the security framework, providing an early detection system for potential data breaches or threats. The complex challenges and problems associated with Big Data in the Internet of Things (IoT) paradigm are diverse and complex, encompassing numerous aspects such as data management, processing, unstructured data analytics, visualization, interoperability, data semantics, scalability, data fusion, integration, quality, and discovery [58]. These issues are closely related to the growing trend of \"big data\" within cloud computing environments and the progressive development of IoT technologies, exerting a significant impact on various industries, including but not limited to the power sector, smart cities, and large-scale petrochemical plants [58; 59; 60]. Additionally, the realm of IoT architectures is not immune to pressing security and privacy threats, making them salient challenges that require immediate and effective addressal [61]. The efficiency and completeness of IoT Big Data, coupled with security concerns, have emerged as critical areas of focus in the realm of research and development [62]. Furthermore, the potential integration of blockchain technology is being explored as a solution to ensure continued integrity in IoT Big Data management, particularly in addressing concerns related to data correctness, resource sharing, and the generation and verification of service-level agreements (SLA) [50].