One of the main challenges in deploying WSN in IoT is the limited battery life of sensor nodes. This is because many sensor nodes are often deployed in remote or inaccessible locations, making it challenging to replace or recharge their batteries.

In a typical IoT system, the Data layer is responsible for processing and analyzing the collected data. This layer is crucial for handling, storing, and making sense of the data gathered from various sensors and devices in the IoT ecosystem.

Data visualization in IoT is essential for monitoring and understanding data. It allows stakeholders to interpret complex data easily, identify trends, and make informed decisions. Effective data visualization is crucial for deriving actionable insights from IoT data.

The main difference is that a general-purpose OS is not optimized for resource-constrained IoT devices. IoT OS is designed to run efficiently on devices with limited processing power and memory.

Early IoT was primarily focused on industrial automation and monitoring. It was used to improve processes, gather data from sensors in manufacturing and logistics, and increase efficiency in various industries. IoT's roots can be traced back to applications like remote monitoring and control of industrial equipment.

Blockchain technology offers a decentralized ledger for secure and transparent transactions, making it a potential solution for enhancing IoT security. It provides immutable and auditable records of transactions, improving trust and security.

The integration of IoT with cloud services is often facilitated by AWS IoT Core, which is a managed service for IoT that provides secure and scalable connectivity to the cloud. MQTT, Docker, and Apache Kafka are relevant technologies but do not provide the same level of cloud integration as AWS IoT Core.

URLLC in 5G is essential for applications that require extremely low latency, such as autonomous vehicles, where split-second decisions are critical for safety.

One advantage of using predictive analytics in IoT for energy management is reduced energy consumption. By analyzing data from various sensors and devices, predictive analytics can optimize energy usage, helping to reduce energy waste and lower operational costs.

Machine Learning in IoT is primarily used for predictive analytics. It involves using historical data to make predictions and decisions, a key aspect of IoT applications.