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.

In this scenario, the primary requirement is real-time analysis of immense data generated by thousands of sensors. Edge computing is the best technology for this purpose, as it allows data processing to occur closer to the data source, reducing latency and enabling real-time analytics, which is crucial in monitoring traffic patterns in a smart city.

The primary purpose of IoT data collection is data analysis and decision-making. IoT devices collect vast amounts of data, which is then processed and analyzed to derive insights and make informed decisions. This data can be used for various applications, including predictive maintenance, improving efficiency, and enhancing user experiences.

By the year 2015, the number of connected devices had already surpassed the global human population. This milestone highlights the rapid growth of IoT, where everyday objects and devices are becoming smart and interconnected, contributing to the data-driven world we live in today.

Data analytics in IoT is instrumental in gaining insights from the vast amount of data generated by IoT devices. It helps in detecting anomalies, predicting trends, and making informed decisions, which are essential for optimizing operations and achieving the goals of IoT applications.

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.

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.

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.

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.