Mites: A Ubiquitous Sensing System and Stack

Description:

2018-018 Mites: A Ubiquitous Sensing System and Stack

Abstract

https://mites.io/
Smart spaces and the internet of things rely on robust sensing of the environment, achieved by upgrading a space with expensive smart devices that do not talk to one another or tagging devices/rooms with after-market sensors. The latter is more flexible and intelligent but can be socially and aesthetically intrusive. Mites.io create an alternate, general purpose sensing approach that uses a single, highly capable sensor board to indirectly monitor an entire room.

Benefit

Key Benefits

Single sensor board can capture a wide variety of events in a room/confined space (given the extensive sensor dimensions), as opposed to multiple sensors or smart devices.

Reduced network overhead

Plug & play sensor
- Uses wall power
- WiFi enabled -ubiquity, ease of set up, range & high bandwidth.

Use of machine learning to automatically recognize patterns of sensor activation/events
Nth order sensors

Scaleable- effective and easy if you’re using 3 sensors within a home context or 300 within an enterprise context.

Ensures user privacy by eliminating the use of camera sensors. End to end encryption support & denaturing data.

Secure back end for effortless monitoring & maintenance

Remote sensor control
- o Including ability to adjust the sampling frequency/gain of a sensor, updating the firmware across the network

Market Application

There are many applications in IoT sensing- some highlighted applications:
Building/maintenance IoT

Mechanical health and maintenance of equipment based on usage/cycles data

Identifying & notifying users of malfunctioning equipment based on irregular patterns of data/activity

Safety & security within facility based on motion, temperature among other sensor dimensions. E.g. theft, fire detection.

Access control

Climate control

Human activity across facility

Remote activity monitoring

Integration with workplace tools to create operational efficiencies by feeding virtual sensors into second or Nth order sensors and capturing higher level inferences.· Resource conservation e.g. electrical, energy or water usage

Stock/restock resource scheduling

Relevant industries: offices/commercial buildings, government, education, construction, manufacturing, hospitality, oil/gas, property management

Publications

Laput, G., Zhang, Y. and Harrison, C. 2017. Synthetic Sensors: Towards General-Purpose Sensing. In Proceedings of the 35th Annual SIGCHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA, May 6 - 11, 2017). CHI '17. ACM, New York, NY. 3986-3999.
https://www.chrisharrison.net/index.php/Research/SyntheticSensors