## 11 Current Studies and Future Directions
### Current Research  
There are many existing works and related frameworks for smart city applications. We present the recent relevant studies that consider the previously mentioned key technologies for enabling heterogeneous smart city applications. In [118], the authors developed a blockchain scheme for outdoor health applications in smart cities. The users' wearable sensors generated health data (HD). The work considered only healthcare applications among all smart city use cases. Also, the proposed approach used a blockchain and MEC as smart city enablers. In [119], the authors developed an offloading method on a fog computing system and formulated an optimization solution for energy consumption in the fog computing process. The authors proposed a fairness algorithm (FCA) to obtain fog nodes' optimal fairness cooperation policy. FCA was an effective solution that reduced energy consumption and overhead time. In [120], the authors used a machine learning (ML) approach to improve MEC performance. The results validated the proposed approach for large-scale networks with good stability but could not recognize fast algorithm convergence. The proposed algorithm for distributed coordination is close to the optimal MEC performance and could achieve fast convergence. The authors of [121] studied the integration of MEC in automotive contexts by dividing the desired applications in the smart city into four main groups. They depended on three layers of MEC architecture to propose a data distribution mechanism. The results demonstrated that the performance of the suggested protocol could be greatly improved regarding data transmission, communication overhead, and latency. In [122], the authors proposed a hierarchical structure for smart city applications. The suggested design aimed to solve the drawbacks of previous methods. The suggested technique improved the scalability and dependability of user access. The data processing cost was reduced by spreading processing tasks over edge devices. The proposed model outperformed other models regarding delay and cost. In [123], the authors offered a paradigm that enabled autonomous orchestration capabilities for smart cities. The work's contribution was integrating fog node orchestration \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Challenge** & **Discussion** \\ \hline Interoperability & Integrating different existing systems with a blockchain might be challenging due to compatibility issues and the need to establish common standards and protocols for seamless communication. \\ \hline Regulatory & Smart cities must navigate complex legal and regulatory frameworks to ensure compliance with existing laws. Blockchain-specific regulations and standards are still evolving in many jurisdictions. \\ \hline Privacy concerns & While a blockchain provides transparency, it may pose challenges in terms of privacy, as personal data on a blockchain might be accessible to all participants. Striking the right balance is crucial to protect personal information. \\ \hline Energy consumption & Blockchain networks can be energy-intensive due to consensus mechanisms like proof-of-work. This can be a concern regarding energy consumption and environmental sustainability. \\ \hline User adoption & Encouraging widespread adoption of blockchain technology may be challenging due to citizens’ lack of awareness and understanding. Promoting digital literacy, conducting public awareness campaigns, and demonstrating the tangible benefits of a blockchain in areas like transparent governance and efficient services can aid in overcoming this challenge. \\ \hline \hline \end{tabular}
\end{table} 
Table 16: _Cont._with the application layer for transmitting service provisioning data between fog units. The developed approach reduced the bandwidth and latency. The authors of [124] created a basic gateway access node based on an adaptable and efficient NFVI. The results further demonstrated that the prototype offered security for IoT devices, recognizing fraudulent traffic with 99.8% accuracy.