# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 6 Fog Computing and Data Management Use Cases in Smart Cities
### Fog Computing in Smart Grid Architectures  
The transformation of cities into smart cities depends mostly on their ability to modernize their power grids through the deployment of smart grids and their efficient management, which will allow for the integration of renewable energies and the production of clean energy close to needs. The smart grid represents a promising technology for integrating green energy resources into the power distribution system, controlling energy use and balancing the energy load. Smart grids rely on smart meters and devices to ensure bi-directional information flows in the power grid to manage and monitor electricity consumption. These devices produce massive amounts of data that smart grids need to harness to enable utilities to offer new pricing schemes that can increase energy efficiency and generate a more reliable power supply through improved power failure management. Improvements to the city power grid would also enable better integration of new technologies such as electric vehicles, which could serve as energy storage in emergencies, creating many opportunities for urban areas. Future prospects would include zero-emission transportation throughout the city. Many scholars investigated the use of cloud computing to support the computational needs, information management, storage, and integration of smart grids [61; 62; 63; 64; 65; 66; 67]. [PERSON] et al. [61] surveyed existing smart grids' cloud-based applications, dealing with the three issues of energy management, information management, and security. They discussed the benefits of cloud computing applications to cope with these issues, and they provided insights into future opportunities for smart grid development. They also highlighted challenges that conventional power grids face that cloud-based solutions can solve. Furthermore, they overviewed the current state of research on smart grids and identified current challenges in cloud-based energy management, smart grid security, and information management. [PERSON] et al. [62] described the benefits and opportunities of smart grid data management in the cloud. Moreover, they proposed a model linking the smart grid domain to the cloud computing domain and presented motivating applications. Figure 5: Fog-cloud-based architecture for healthcare. Real-time data management and processing is one of the pressing issues for smart grids. A number of efforts investigated this concern in the context of the cloud [66; 67; 68]. [PERSON] et al. [67] studied the requirements for deploying smart grid applications on cloud servers. They found that many promising energy management applications require scalability of the kind only cloud computing can provide. However, these applications also include additional requirements such as support for scalable real-time services, support for consistent and fault-tolerant services, and privacy protection. Cloud computing would not currently support these requirements. The authors in [66] proposed a cloud-based model for smart grid data management to meet the near real-time information retrieval needs of various energy market players. The distributed data management and parallel processing schemes are highly specialized in time series, which is the typical type of data generated by a smart grid. Therefore, a smart grid DaaS provider would provide its data management and processing services to any party with a legitimate interest in providing reusable services such as data collection, validation and cleansing, analysis, and data archiving. [PERSON] et al. [68] investigated the issue of smart grid big data analytics in the cloud. They described a cloud-based platform for data-driven analytics to respond to dynamic consumer demand (D2R optimization), detecting the supply-demand mismatch, and preemptively correcting it. The platform ingests real-time data and uses a semantic data integration pipeline and scalable machine-learning models trained over massive historical datasets to predict the energy demand.