Wearable Badge for Indoor Location Estimation of

Mobile Users

MAS 961 Developing Applications for Sensor Networks

Daniel Olguin Olguin

MIT Media Lab

Description of the problem

• People in large companies and manufacturing facilities are constantly moving from place to place.

• In this scenario it becomes difficult to find a person in a fast and efficient way.

• If the person is not carrying a cell phone or pager and somebody needs to contact her, it can take some time before the person is reached.

Proposed solution

• I propose the use of a wearable badge and the plugs as base stations to estimate the location of a person in real time.

• Using RSSI we can estimate distance, proximity and location of a mobile user.

• The location can then be displayed using an LCD/LED or an interactive map.

RSSI and location estimation

• GPS is the most used technique for outdoor location, but its use is limited in indoor applications.

• Radio Signal Strength Indicator (RSSI) is a standard feature in most radios, however it has not been effectively used in existing localization algorithms.

• Indoor location estimation is a challenging task.

Building the list of neighbors

• Each plug and lug builds a list of neighbors as they receive packets.

• The list of neighbors contains the following information:

Neighbor[1]

Local address Dynamically assigned

Link quality indicator Appended to the end of each packet after RSSI value. LQI is used as a relative measurement of the link quality since its value is dependent on the modulation format

RSSI Appended to the end of each packet after the payload. The RSSI value read from the status register is a 2’s complement number.

Number of packets received Up to 256

Time of last packet Time stamp

Global address Physical address of each device

Neighbor[2]

Finding the nearest neighbor

• Each plug broadcasts a packet containing the address of a mobile node (lug).

• The lug with this address receives packets from all its surrounding plugs.

• The lug builds its list of neighbors and determines its nearest neighbor by looking at the RSSI values in the list.

• The lug broadcasts a packet containing the physical address of its nearest neighbor.

• Each plug receiving this packet compares the address of the lug’s nearest neighbor with its own address. If they are the same the green LED turns on.

payload_

length

to_address from_address type payload[0] = find request

payload[1] = physical address byte 1

payload[2] = physical address byte 2

Plugs and Lug Tracking a Mobile User

Conclusions and future work

• This is the first step towards solving the location estimation problem in a wireless sensor network using RSSI as a measure of distance/proximity.

• Location estimation algorithms could be implemented to improve the accuracy of the estimation.– Triangulation– ML estimation of a target’s position by using MSE– SELFLOC (Selective Fusion Location Estimation). This algorithm

combines multiple information sources (heterogeneous RF sensors) and selectively weights them to minimize the error contribution from each branch.

• Working towards an intelligent badge that will allow finding mobile users by using voice commands, retrieving information from experts, and facilitating communication and interaction of work groups.