Internet of Things Patterns
This is an excerpt of the pattern that was first published in [1].

Normally-Sleeping-Device

Icon of the Normally-Sleeping Device pattern

Devices with a limited supply of energy have to use it wisely. Disable most of the components of the device for long periods of time and only enable them when required.

Aliases:

Sleepy, Deep Sleep, Hibernate, Duty-cycled, Normally-Off

Context:

You have a use case which comes with size, weight, cost, or energy restrictions. For example, this is the case when the use case needs mobility or wearability. You use devices optimized to fit these restrictions. These devices are Lifetime Energy-Limited Devices, Period Energy-Limited Devices, or Energy-Harvesting Devices.

Problem:

You have a device with a limited energy supply. You want to minimize the power used by the device.

Forces:

  • Limited Energy: Having an Always-On Device is not an option since the device has a limited power source.

  • Energy Saving: Saving energy decreases costs and is good for the environment but leads to constraints.

  • Component Use: The device does not use every component continuously. Turning them off when not needed saves energy. But if these components have long startup times, the responsiveness of the device suffers.

  • Communication: Turning of the communication module when not needed saves energy. But doing this manually takes too much effort, especially for remotely placed or large amounts of devices.

Solution:

Program the device to disable its main components when they are not needed. Leave a small circuit powered which reactivates the components after a predefined amount of time has passed or when an event occurs.

Solution sketch of the Factory Bootstrap pattern

Solution Details:

This is an excerpt of a previously published pattern. The full pattern can be found in [1].


Benefits:
Drawbacks:

Variants:

Related Patterns:

Known Uses:

  1. L. Reinfurt, U. Breitenbücher, M. Falkenthal, F. Leymann, and A. Riegg, “Internet of Things Patterns for Devices,” in Proceedings of the Ninth International Conferences on Pervasive Patterns and Applications (PATTERNS) 2017, 2017, pp. 117–126. Available at https://www.thinkmind.org/index.php?view=article&articleid=patterns_2017_9_10_70019