Smart thermostat
In 2011, Nest Labs developed the Nest Learning Thermostat. The Nest Thermostat attempted to reduce home energy consumption by addressing the problems with programmable thermostats through the use of better technology. This new technology included the implementation of sensors, algorithms, machine learning, and cloud computing. These technologies learn the behaviors and preferences of the occupants, and adjust the temperature up or down to make the occupant comfortable when they are home and to save energy when they are away. Additionally, the Nest Thermostat connects to the home Wi-Fi. This allows users to change the temperature, adjust the schedule, and check energy usage from a smartphone or laptop. All of these features were part of Nest's goal to create an easy to use thermostat that saves users energy and money.
Wireless ad hoc network
Sensors are useful devices that collect information related to a specific parameter, such as noise, temperature, humidity, pressure, etc. Sensors are increasingly connected via wireless to allow large scale collection of sensor data. With a large sample of sensor data, analytics processing can be used to make sense out of these data. The connectivity of wireless sensor networks rely on the principles behind wireless ad hoc networks, since sensors can now be deploy without any fixed radio towers, and they can now form networks on-the-fly. "Smart Dust" was one of the early projects done at U C Berkeley, where tiny radios were used to interconnect smart dust. More recently, mobile wireless sensor networks (MWSNs) have also become an area of academic interest.