## 11 Current Studies and Future Directions
### Research Directions
* Deploying distributed edge computing for smart city applications This includes novel structures of edge units and novel interfaces with other network parts. Moreover, the development of associated algorithms with the deployment of edge computing is an important direction. These algorithms should meet the demands of future smart cities. The main directions regarding distributed edge deployment for smart cities can be summarized as follows:
* Implementing a microservices-based architecture for edge nodes, breaking smart city applications into modular, independently deployable services. This enhances scalability, maintainability, and flexibility. * Utilizing containerization technologies, e.g., docker, to encapsulate each microservice, ensuring consistency across edge units and easing deployment and scaling processes. * Employing orchestration solutions, e.g., Kubernetes, to manage and scale the deployment of containers across distributed edge units seamlessly. * Establishing standardized APIs for communication between edge units and other network components. * Design edge computing infrastructure that can easily scale to accommodate the growing demands of smart city applications. * Using 5G technologies to connect edge units and central data centers at high speeds and low latency. * Implementing load-balancing algorithms to distribute computational workloads efficiently between edge units, preventing resource bottlenecks and optimizing overall system performance. * Developing algorithms for intelligent data offloading between edge units and centralized cloud resources, optimizing data processing based on different parameters, including network congestion and application requirements. * Introducing the paradigm of green edge computing by integrating energy-efficient computing practices into the design, utilizing low-power hardware, and implementing algorithms that optimize energy consumption. * Managing the massive network traffic via intelligent core This includes developing SDN networks efficiently for handling massive traffic of different smart city applications. Also, this direction includes developing associated network algorithms to facilitate the operation of SDN controllers. Moreover, developing API for network operators to facilitate managing smart city networks. An SDN has many remaining issues that can be considered for massive deployment networks. The research on this part of smart city networks can involve the following:
* Creating a distributed control plane architecture that enables effective resource management and coordination in response to the changing needs of different
applications for smart cities. A multi-controller scheme should be deployed for such core networks. * Developing novel ways for communication between SDN controllers and avoiding communication overhead. * Developing intelligent schemes, i.e., AI-based schemes, for load balancing between SDN controllers. * Using network slicing to make virtualized, separate parts of the communication infrastructure. This will allow different smart city applications personalized and the best communication paths. This includes creating methods for dynamic network slicing, enabling the effective allocation and deallocation of resources according to the evolving demands of various applications over time. * Developing adaptive routing protocols that dynamically adjust to network topology change and traffic patterns. This includes integrating machine learning algorithms for anomaly detection in network behavior, allowing the SDN infrastructure to adapt to emerging patterns. * Developing intelligent algorithms for proactively detecting, predicting, and diagnosing network faults and failures to ensure ultra-high network availability. * Developing novel reliable APIs for SDN networks using existing solutions, e.g., OpenAPI and Swagger. * Developing novel NFV approaches for future smart cities able to meet the required network scalability and cost efficiencies.