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.

Microsoft Azure IoT provides a fully managed service for connecting, processing, and storing data from IoT devices. While other cloud platforms offer IoT services, this is a distinctive feature of Azure IoT.

A zero-day vulnerability is a security flaw in a system or device that is not yet known to the vendor or the public. It's called "zero-day" because there are zero days of protection against it. Hackers can exploit such vulnerabilities before the vendor has a chance to fix them, making them high-priority issues for security.

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.