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.

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.

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.

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.

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.

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.

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.

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.

Proper device provisioning ensures efficient device operation. When an IoT device is correctly provisioned, it is set up with the necessary configurations and credentials, allowing it to function optimally. This includes proper authentication, network settings, and security measures to ensure the device operates as intended.

In its early stages, the Internet of Things (IoT) was primarily focused on industrial automation. It aimed to improve efficiency and monitoring in various industries by connecting machines and sensors to the internet, enabling remote monitoring and control.

One of the earliest implementations of IoT can be traced back to Carnegie Mellon University, where a "wireless sensor network" was developed. Wireless sensor networks are a crucial component of IoT, as they enable the collection of data from various sensors and devices. These networks are a fundamental building block of IoT technology.

Monitoring equipment health and predicting machine failures is a prime example of predictive analytics. Predictive analytics uses historical data and statistical algorithms to make predictions about future events, helping companies prevent costly machine failures.