"Wake on Demand" functionality in IoT devices means that the device remains in a low-power or sleep state until an external stimulus or event triggers it to wake up. This approach helps save power and resources by ensuring that the device is only active when necessary, making it an energy-efficient strategy for IoT applications.

In smart city applications, traffic sensors play a crucial role in collecting real-time traffic data, which is used to manage and optimize traffic flow, reduce congestion, and enhance overall transportation efficiency.

Sensors are the components in IoT architecture responsible for gathering data from the environment. They capture data from the physical world, such as temperature, humidity, or motion, and transmit it to other IoT components for processing and analysis.

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.

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.

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.

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.

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.

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.

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.