# Machine Learning for Blockchain and IoT Systems in Smart Cities: A Survey
## 5 Challenges and Future Directions  
The integration of ML, blockchain, and IoT in smart cities, while promising, encounters a myriad of sophisticated challenges that demand careful consideration and innovative solutions. One of the foremost challenges lies in the computational demands of ML algorithms, particularly in the context of large-scale IoT networks. These networks generate immense numbers of data that must be processed in real time to maintain the efficacy of smart city operations. The complexity of deploying ML models on resource-constrained devices at the edge of the network exacerbates this issue. Current advancements in edge computing, while notable, remain insufficient for the seamless execution of ML algorithms that require significant processing power and memory. Consequently, there is a pressing need for the development of more efficient, lightweight ML models that can operate under the stringent constraints of IoT environments without compromising performance [107; 108; 109]. Scalability poses another critical challenge, particularly in the context of blockchain technology. As the proliferation of IoT devices in smart cities accelerates, blockchain networks must contend with an ever-increasing number of transactions. Traditional blockchain architectures, reliant on resource-intensive consensus mechanisms like proof of work, struggle to maintain performance and throughput at scale. This scalability bottleneck not only hampers the real-time processing of transactions but also raises concerns about energy consumption and latency. Emerging solutions such as sharding and layer-2 protocols, and alternative consensus mechanisms like proof of stake offer potential pathways to enhanced scalability. However, these innovations must be rigorously tested and optimized for integration \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Topic** & **References** & **Description** \\ \hline ML, Blockchain \& IoT & [79; 80; 81; 82] & Discussion on the synergy and integration of ML, blockchain, and IoT in smart cities. \\ \hline ML in IoT & [83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106] & Application of ML within IoT systems for smart city services like traffic management, energy optimization, and anomaly detection. \\ \hline Blockchain for Automation \& Integrity & [87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106] & Use of blockchain and smart contracts to automate processes and ensure data integrity in smart cities. \\ \hline Interoperability \& Data Privacy & [95; 96; 97; 98] & Challenges and solutions related to interoperability among systems and maintaining data privacy in smart city integrations. \\ \hline FL \& Security & [99; 100; 101; 102] & Use of FL in IoT environments to enhance security and privacy with blockchain integration. \\ \hline Privacy-Preserving Frameworks & [103; 104; 105; 106] & Focuses on privacy-preserving frameworks using blockchain and ML to ensure secure and private data management in IoT-driven smart cities. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The synergy of ML, blockchain, and IoT in smart cities. within the complex, multi-layered ecosystems of smart cities, where the interplay between various subsystems demands seamless, high-throughput communication [110; 111; 112]. Interoperability represents yet another significant hurdle. The diversity of IoT devices, blockchain platforms, and ML frameworks within a smart city ecosystem introduces challenges related to standardization and compatibility. The lack of standardized protocols for data exchange and integration can lead to fragmented systems that fail to communicate effectively, undermining the overall coherence and efficiency of smart city operations. Addressing this issue requires the development of universal standards and protocols that facilitate interoperability across heterogeneous systems.