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.

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 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.

A company's IoT devices participating in a DDoS attack could be due to a lack of proper security measures. IoT devices, if not adequately secured, can be vulnerable to malicious actors who compromise their operation and use them as bots in a DDoS attack. Ensuring security measures like encryption, strong authentication, and regular security updates is crucial in such cases.

Data breaches are a common security threat in IoT, as unauthorized access to IoT data can have serious consequences, such as privacy violations and misuse of sensitive information.

In the context of IoT, AI algorithms that enable devices to learn from data and enhance their performance over time are referred to as "Reinforcement Learning" algorithms. These algorithms learn by trial and error, which is valuable in IoT applications.

Wearable devices primarily use the "Bluetooth" communication model to send health metrics and data to the user's smartphone or other devices. Bluetooth is a short-range wireless communication technology commonly used in wearables for connectivity.

A temperature sensor would be the most appropriate choice for monitoring the shipment of perishable goods. Temperature sensors, often referred to as thermometers or thermocouples, measure the temperature of their surroundings. In this context, they are crucial for ensuring that perishable items remain within a safe temperature range during transportation, such as in refrigerated trucks or containers.