The given scenario falls under the Smart Cities category of IoT. It involves using sensors and data to monitor and improve the quality of life in urban areas. This specific application focuses on environmental monitoring, ensuring that residents are informed about air quality, which is a critical aspect of urban well-being.

The primary driver for the shift from centralized cloud computing to edge computing in IoT is the increased demand for low-latency processing. In many IoT applications, such as autonomous vehicles and industrial automation, real-time or near-real-time data processing is critical, and edge computing helps achieve this by processing data closer to the source.

To achieve the automatic adjustment of lights in a modern smart home based on the time of day and presence in the room, the system would likely involve home automation and occupancy sensors. These sensors detect motion and light levels, enabling the smart home system to control lighting based on occupancy and time, enhancing energy efficiency and convenience.

In a smart city scenario, real-time data processing is crucial for making immediate decisions. Edge computing, which processes data closer to the data source (the sensors in this case), is the most suitable option. It reduces latency and enables quick responses, making it ideal for applications like smart cities.

In the context of IoT device management, ensuring firmware is consistently uniform across all devices is crucial. This uniformity ensures that all devices are running the same firmware version, reducing compatibility issues, vulnerabilities, and enhancing security and functionality.

LoRa (Long Range) technology is ideal for long-range wireless communication. It's designed to provide low-power, long-range communication for IoT devices, making it suitable for applications where devices need to send data over considerable distances.

Hardcoded credentials, such as default usernames and passwords, can lead to serious security vulnerabilities. Attackers can easily guess or find these credentials, potentially compromising the IoT device and the network it's connected to.

Predictive analytics in IoT primarily focuses on analyzing historical data from IoT devices to make informed future predictions. By examining past data, IoT systems can forecast future trends, troubleshoot issues, and optimize operations.

Python is favored for data-intensive IoT applications due to its extensive libraries for data processing and analysis. It's versatile and has a large community, making it suitable for a wide range of data-related tasks in IoT. While C, R, and Swift have their strengths, Python stands out in this context.

The concept of IoT can be traced back to the 1970s, even though it became more popular and widely recognized in the 21st century. The first internet-connected device was a Coke machine at Carnegie Mellon University in the early 1980s, but the foundational ideas began earlier.