In Edge computing, intermediary nodes (often located closer to the end-devices) process data before it's sent to the cloud. This reduces latency and can be crucial for real-time or time-sensitive applications, making it a suitable choice for IoT scenarios.

Cloud computing is the technology that primarily supports the vast amount of data generated by IoT devices. IoT devices generate enormous quantities of data, and cloud platforms provide scalable and flexible infrastructure for storing, processing, and analyzing this data. Cloud computing also enables remote access and real-time monitoring of IoT devices.

One of the primary reasons IoT devices employ low-power modes is to conserve battery life. IoT devices are often deployed in remote or inaccessible locations where changing batteries can be challenging, so optimizing power consumption is crucial.

In a typical WSN, sensor nodes collect and process data. These nodes are responsible for sensing the environment, processing the collected data, and transmitting relevant information to the base station or other nodes in the network.

In IoT healthcare applications, remote patient monitoring primarily employs Cellular (3G/4G/5G) Communication. Cellular networks offer widespread coverage and reliable connectivity for transmitting patient data to healthcare providers, ensuring that critical health information can be monitored in real-time.

In IoT projects, the phase where potential risks and challenges are identified is known as "Risk Identification." This process involves recognizing and documenting possible issues that could impact the project's success.

The Raspberry Pi supports programming in Python, which is one of the most commonly used languages for developing applications on this platform. While other languages like C# and Ruby can be used on the Raspberry Pi, Python is particularly popular due to its extensive support and libraries.

After defining objectives in an IoT project like this, the next crucial step is data collection and sensor deployment, as this is essential to gather the necessary data for predicting maintenance needs accurately.

A wearable IoT device is a device that can be worn on the body and is connected to the internet to collect and transmit data. A fitness tracker is a prime example of a wearable IoT device, as it's worn on the wrist to monitor health and activity data like steps, heart rate, and sleep patterns.

Edge Computing is a critical component for seamless IoT and AI integration in edge devices. It allows processing data closer to the source, reducing latency and enhancing efficiency.

In the context of IoT (Internet of Things), edge computing involves processing data closer to the data source, often at or near the IoT devices themselves. This approach reduces latency, conserves bandwidth, and allows for real-time data analysis. Edge computing is especially useful in scenarios where immediate data processing and decision-making are critical.

Energy efficiency is a fundamental aspect of IoT device power management. IoT devices are often battery-powered or have limited power resources, so optimizing energy consumption is critical to prolong battery life and ensure continuous operation.