# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 2 Background
### Characteristics of Fog Computing  
The research efforts described in Section 6 as well as other works in other fields demonstrate that reduced latency, reduced traffic and bandwidth optimization, and enhanced privacy and security represent the essential advantages that edge and fog computing have over cloud computing.  
Reduced latency: With cloud-based IoT solutions, whenever an application initiates an action, for example, to query the status of a medical device over a period of time, the system must understand the command, send a request to a cloud server for processing, then wait for the response before presenting it graphically. With solutions based on edge computing, IoT data streams are partially stored and processed locally, allowing businesses to get faster responses and reduce network traffic. Reduced latency is essential for many verticals, such as supply chain management and logistics, smart transportation, and digital healthcare.  
Reduced traffic and bandwidth optimization: According to several forecasts, the number of IoT devices will exceed 30 billion over the next few years. These devices would generate nearly 2.5 billion bytes of data per day. To reduce the volume of data crossing the network and optimize bandwidth usage, organizations need to analyze IoT data at the edge, while state data can be aggregated, compiled, and sent daily to a central cloud repository. Data analysis at the edge can be supported by machine learning and artificial intelligence algorithms. Many modern devices, such as consumer electronics, delivery drones with computer vision capabilities, and autonomous industrial robots, already support artificial intelligence algorithms.  
Enhanced privacy and security: Edge and fog computing allow for the analysis of sensitive data at an on-premises gateway or fog node instead of a data center outside of the organization's control, which dramatically improves data privacy. Vulnerabilities in the Internet of Things come from the limited computing power of IoT devices, as well as hardware design flaws and firmware update constraints. These inconsistencies can provide hackers with an easy entry point into a network. Data transfer between devices in an IoT solution and the cloud infrastructure can also pose security issues. Therefore, the security of IoT solutions should be enhanced at the network level. The network edge might be the place to insert security patches between vulnerable devices and the other components of the network.  
For many organizations, data overload and the high cost of cloud analytics is the biggest barrier to capturing and analyzing sensor data. The above benefits of edge and fog computing can lead many companies to change their minds by adopting smart IoT solutions in which edge and fog computing could help combat the data deluge by making it easier to collect, aggregate, and process their IoT data. A balance between processing and storing data at the edge/ fog level and its processing and storage at the cloud level is necessary because the cloud will always have more storage and processing power than edge devices. Fog computing is a substitute for pure edge computing in cases where edge devices cannot perform data analysis due to their limited resources.