While increased connectivity, decreased cost of sensors, and improved data analytics are key drivers of IoT growth, reduced security concerns are not.

Edge computing aims to process data closer to the source, enabling real-time analysis and reducing latency.

Mesh networking allows devices to communicate directly with each other, forming a self-healing and resilient network.

AIoT refers to the combination of IoT and AI, where AI algorithms are applied to IoT data to enhance decision-making and automation.

Industrial IoT involves using sensors and actuators to monitor and control industrial equipment and processes remotely.

LPWANs are designed for long-range communication, enabling IoT devices to operate in remote areas with limited infrastructure.

Bluetooth is widely used for short-range wireless communication between IoT devices due to its low power consumption and ease of use.

Digital twins are virtual representations of physical assets, enabling real-time monitoring, predictive maintenance, and optimization.

Asset tracking involves using IoT devices to monitor the location and condition of valuable assets in real-time.

Machine learning algorithms enable IoT devices to learn from data, identify patterns, and make predictions without explicit programming.

Cellular networks are widely used for long-range wireless communication in IoT, providing reliable connectivity over large areas.

Blockchain technology provides increased data security in IoT by ensuring the integrity and immutability of data transactions.

Smart home automation involves using IoT devices to control lighting, heating, cooling, and other home appliances remotely.

Machine-to-machine (M2M) communication refers to the ability of IoT devices to exchange information and data with each other without human intervention.

Wi-Fi is commonly used for wireless communication between IoT devices and the cloud due to its high bandwidth and ease of deployment.