honours

Shortlisting Hardware

I’m learning rapidly that the area of occupancy is booming at the moment, and the set of possible options for sensors and host hardware is of considerable size.

To help trim down my list to a set of viable and non-viable candidates, I’ve needed to come up with a more stringent set of criteria, and measure possible solutions against them.

Criteria

The set of criteria I am attempting to optimise for are:

  • Cost; the set of components required should aim to minimise cost, as these devices are intended to be deployed in situations where the user may not have much money.

  • Invasiveness; the sensors used in the system should gather as little information as necessary to achieve the detection goal; there are privacy concerns with the use of high-definition sensors.

  • Energy Efficiency; the system may be placed in a situation where there is no access to mains power, so ability to survive for long periods on only battery power is advantageous.

  • Reliability; the system should be able to operate without user intervention or frequent maintenance, and should be able to perform its multi-occupant detection goal with a high degree of accuracy.

Host options

There are are a variety of options for configurations that can host the sensor:

  • Raspberry Pi (battery): A Raspberry Pi with a set of sensors connected to it operates on a rechargeable battery pack.

  • Raspberry Pi & Sleepy Pi (battery): A Raspberry Pi with the Sleepy Pi addon operates on a wake-sleep cycle with a set of sensors connected to it.

  • Arduino (battery): An Arduino board with a set of sensors connected operates on a rechargeable battery pack, with on board data processing.

  • Raspberry Pi (mains), Arduino (battery): An Arduino board with a set of sensors connected operates on a rechargeable battery pack. A Raspberry Pi communicates with it and handles the data processing aspect.

  • TMote Sky (battery): A MoteIV Tmote Sky, the same as that used in the ThermoSense paper. [1]

Sensor options

There are similarly a variety of sensors that can either be used directly, or act as proxies for occupancy in a given area.

  • Thermal Array: A thermal array is used to measure temperatures in different sections of a room. The use of them to detect occupancy is covered in multiple papers. [2], [3]

  • Tagging: By assuming occupants have smartphones, it is possible to use them as a proxy for occupancy. [4]

  • Ultrasonic: Through the use of ultrasonic measurement of distance, passage through doorways can be directionally measured. [5]

  • Power Consumption: By assuming occupants will switch on electronic devices when they enter a room, broad power consumption can act as a proxy for occupancy. [6]

  • Network traffic: By assuming occupants will use computers when they enter a room, network traffic can act as a proxy for occupancy. [7]

  • Cameras: Computer Vision algorithms can be used on video and images to determine the number of people present in the scene. [8]

  • Fusion: Multiple different solutions can be combined to help optimise over multiple criteria. [9]

Comparison

Host Cost Energy Efficiency
RPi $50 1000mA
RPi & SleepyPi $105 dependent
Arduino Yun $70 500MA
Arduino Uno $125 50mA
TMote Sky $105 25mA

Cost includes that of compatible 802.11 adaptor if it doesn’t have built in wireless capabilities.

Sensor Type Cost Invasiveness Reliability
Grid-Eye Thermal $30 Low High
802.11 card Tagging $15 Low Medium
HC-SR04 Ultrasonic $5 Low Low
OV7670 Camera (Arduino) $10 High Low
OV5647 Camera (RPi) $40 High High

I’ll be evaluating these options over the next week.

References

  1. A. Beltran, V. L. Erickson, and A. E. Cerpa, “ThermoSense: Occupancy Thermal Based Sensing for HVAC Control,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013, pp. 1–8.
  2. V. L. Erickson, A. Beltran, D. A. Winkler, N. P. Esfahani, J. R. Lusby, and A. E. Cerpa, “ThermoSense: thermal array sensor networks in building management,” in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013, p. 87.
  3. V. L. Erickson, A. Beltran, D. A. Winkler, N. P. Esfahani, J. R. Lusby, and A. E. Cerpa, “TOSS: Thermal Occupancy Sensing System,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013, pp. 1–2.
  4. W. Kleiminger, C. Beckel, A. Dey, and S. Santini, “Using unlabeled Wi-Fi scan data to discover occupancy patterns of private households,” in Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, 2013, p. 47.
  5. T. W. Hnat, E. Griffiths, R. Dawson, and K. Whitehouse, “Doorjamb: unobtrusive room-level tracking of people in homes using doorway sensors,” in Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 2012, pp. 309–322.
  6. W. Kleiminger, C. Beckel, T. Staake, and S. Santini, “Occupancy detection from electricity consumption data,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013, pp. 1–8.
  7. K. Ting, R. Yu, and M. Srivastava, “Occupancy inferencing from non-intrusive data sources,” in Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, 2013, pp. 1–2.
  8. V. L. Erickson, S. Achleitner, and A. E. Cerpa, “POEM: Power-efficient occupancy-based energy management system,” in Proceedings of the 12th international conference on Information processing in sensor networks, 2013, pp. 203–216.
  9. Z. Yang, N. Li, B. Becerik-Gerber, and M. Orosz, “A multi-sensor based occupancy estimation model for supporting demand driven HVAC operations,” in Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, 2012, p. 2.

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