Explain applications of Fog computing.

Fog computing, often referred to as "edge computing," is an architecture designed to process data closer to the source rather than relying solely on cloud infrastructure. This decentralized approach reduces latency, enhances real-time data processing, and optimizes network bandwidth. The applications of fog computing are widespread and diverse, particularly in fields where real-time decision-making, low latency, and high reliability are critical. Below are some prominent applications of fog computing:

### 1. Smart Cities
Fog computing plays a pivotal role in the development of smart cities. With the increasing adoption of IoT devices such as traffic sensors, smart meters, and surveillance cameras, fog computing offers a way to process data at the edge of the network, closer to where it is generated. This is especially beneficial for traffic management systems, where real-time data from sensors embedded in roads and vehicles can be processed immediately, enabling the dynamic control of traffic lights, efficient routing, and improved safety. Additionally, fog computing can manage the smart grid systems, where local data processing helps optimize energy distribution and consumption, reducing energy loss and improving the efficiency of power systems.

### 2. Autonomous Vehicles
Autonomous vehicles rely heavily on IoT devices such as cameras, LIDAR sensors, and radar to navigate and make real-time decisions. The massive amount of data generated by these devices needs to be ______ ________ ______ ________ ________ _____ ________ _____ ________.
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