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.

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.

Azure IoT Hub is a service that facilitates bi-directional communication between IoT applications and the devices it manages. Azure Functions, Azure IoT Suite, and Azure Data Factory serve different purposes within the Azure ecosystem.

ARPANET, the precursor to the modern internet, can be considered a precursor to the Internet of Things (IoT). ARPANET was one of the first wide-area packet-switching networks and laid the foundation for the interconnected networks that led to the development of IoT.

The scope and objectives of an IoT project are defined in the planning phase. During this phase, project goals, deliverables, and overall strategy are determined.

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.

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.