CEILING SENSOR NETWORK FOR SOFT AUTHENTICATION

AND PERSON TRACKING USING EQUILIBRIUM LINE

Hidetoshi Nonaka, Shuai Tao, Jun Toyama and Mineichi Kudo

Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan

Keywords: Soft authentication, Person tracking, Ceiling sensor network, Equilibrium line, Electromagnetic interference.

Abstract: In the previous stage of our research, we have developed a soft authentication system using a ceiling sensor

network. Our aim has been to exclude psychological and physical load caused by using strict biometrics,

video camera, and so on. We introduced a notion of distributed personality for authentication and tracking

of several persons. Through experimental results, we confirmed that the system could keep track of up to 5

persons. However, it has been found the performance is not enough for practical use, and then we have

reconstructed and improved the system. In this position paper, we present the design policy, overview of the

network system, and the obtained performance.

1 INTRODUCTION

Soft authentication is one of the important elements

of pervasive computing. Identifying and tracking of

the user are indispensable for appropriate context-

aware services. In general, there is a trade-off

between the accuracy (and security) and the comfort

in the user’s authentication. In the case of context-

aware services, the latter is more important,

considering the user’s privacy and psychological

load.

In our project, we have developed a soft

authentication and tracking system using ceiling

sensor network (Hosokawa, 2009). Our aim has been

to exclude psychological and physical load caused

by using strict biometrics, video camera, and so on.

Through experimental results, we confirmed that the

system could keep track of up to 5 users with a high

probability of correct identification. Now we are

examining the combination of various techniques,

such as individual ambient temperature (Kanda,

2009), and chairs with pressure sensor array

(Yamada, 2009). Complementary integration of

weak evidences is suitable for our purpose

(Koumoto, 2009).

In the previous ceiling sensor network

(Hosokawa, 2009), the sampling rate for 50 sensors

has been relatively slow for effective tracking and

practical use. We decided to reconstruct and

improve the system, especially the network protocol.

In this position paper, we present the design policy,

overview of the network system, and the obtained

performance.

2 SOFT AUTHENTICATION

Recently, various authentication systems have come

into practical use. The authentication factors are

classified into three types: the ownership factors, the

knowledge factors, and the inference factors. The

ownership ones include ID card, cell phone with RF-

ID, USB token, etc. The knowledge ones include

password, pass phrase, PIN number, birthday, etc.

The inference ones are mainly based on biometrics

such as fingerprint, DNA, retinal pattern, iris, face,

finger vein, speech, and so on. Such systems are

effective for maintaining a high level of security and

accuracy. On the other hand, for context-aware

services in daily life at home or at the office, such a

high level of security is not needed. The important

feature required for such purpose is that there is no

mental pressure, physical load, or invasions of

privacy.

We call the traditional authentication for security

“hard authentication,” and we call the authentication

for personalized services “soft authentication.” The

differences between them are summarized in Table 1.

218

Nonaka H., Tao S., Toyama J. and Kudo M..

CEILING SENSOR NETWORK FOR SOFT AUTHENTICATION AND PERSON TRACKING USING EQUILIBRIUM LINE.

DOI: 10.5220/0003398202180223

In Proceedings of the 1st International Conference on Pervasive and Embedded Computing and Communication Systems (PECCS-2011), pages

218-223

ISBN: 978-989-8425-48-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: Difference between “soft authentication” and “hard authentication” (Hosokawa, 2009).

Item Soft authentication Hard authentication

Goal Personalized service Security

Process/Device Unnoticeable Noticeable

Population size Small (2-20) Large (10-100,000)

Place to use Home or office, anywhere in a room Building or security-controlled room, fixed place

Level of accuracy Low (Very) High

Strength of evidence Weak (behavioural evidence, gait, etc.) Strong (iris, DNA, finger vein, etc.)

User’s cooperation Unnecessary Necessary

Psychological barrier Weak/None String

Sensors Infrared, pressure, etc. Camera, special devices

Necessary environment No special condition, day and night, movable obstacles Controlled condition, no obstacles

Establishment cost Low and flexible High and fixed

Stolen damage Low Crucial

In order to combine authentication and person

tracking, video camera has been used in many

studies. It is used for abnormality detection of single

residents (Aviv, 1997), (Sawai, 2004). Multiple

cameras and multiple omnidirectional cameras are

also utilized for authentication and person tracking

systems (Sogo, 2004), (Zhao, 2004), (Hightower,

2001), (Khan, 2001), (Yam, 2003). However, using

a camera causes psychological load and invasion of

privacy.

Pressure sensors have been used for the same

purpose (Murakita, 2004), or auxiliary data for other

methods (Ito, 2004), (Want, 1992), (Okuda, 2005).

However, in general, large number of sensors is

necessary for person tracking (more than 25 sensors

per 1 m

Pyroelectric infrared sensors have been also

adopted for soft authentication and person tracking.

In some of them, wireless ID badges are used for the

person identification (Want, 1992), (Schulz, 2003),

but some people would hesitate to wear such sensing

devices in a room. Others are the systems for single

person (Okuda, 2005). There is a series of studies

using pyroelectric infrared sensors and Fresnel lens

array (Hao, 2006), (Fang, 2006), (Fang, 2007),

(Shanker, 2006), but they are the tracking systems

for single person, and the resolution is not sufficient

to specify the person’s position in a room.

In the early stage of our study, we constructed a

ceiling sensor network using pyroelectric infrared

sensors (Hosokawa, 2005). We tried to use a

Bayesian network for prediction of the user’s current

position. However, we noticed that it was difficult to

embed into the algorithm several kinds of

knowledge about the difference in trajectories and

the difference in layout of obstacles. To learn the

probability of possible movements depending on the

locations, many training data are necessary.

Therefore we decided to use a simpler method

(Hosokawa, 2009).

We introduced a notion of distributed personality

(Hosokawa, 2009). In principle, two persons cannot

be distinguished when they meet at one place. When

this situation occurs, we divide their personalities.

For example, when persons A and B get together at

the same place, we replace them with two virtual

persons who have multiple personalities consisting

of a half of A and a half of B. By this division of

personality, the total number of persons is kept as

two. In this way, we have ambiguity but can keep

tracking them. The probability of each personality is

updated incrementally according to various weak

evidences, such as walking speed, trajectory of

movement, sitting down specific chair, and so on.

Through experimental results, we confirmed that

the system could keep track of up to five persons

with a high probability, however, precise

identification was difficult and more than six

persons could not be authenticated. We found the

main defect of our system was the sampling rate

from the sensor nodes, which was 2 Hz when all of

the 50 sensors are activated. Therefore, we decided

to reconstruct and improve the system, especially the

network protocol.

3 REQUIREMENTS

In our system, up to 128 pyroelectric infrared

sensors are attached on the ceiling. They are

interconnected by a sensor network. By gathering

the data from sensors, authentication and tracking of

users are achieved. The scope of our system extends

to not only home, office, laboratory, but also nursing

home, elder-care facility, assisted-living

condominium, welfare hospital, and child-care

CEILING SENSOR NETWORK FOR SOFT AUTHENTICATION AND PERSON TRACKING USING EQUILIBRIUM

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institution. Consequently, following conditions are

required:

1. Low noise. Medical electronic instruments

such as artificial pacemakers might be used in

the room. Influence of EMI (electromagnetic

interference) should be avoided. Wireless

network is not preferable, and the wire should

be shielded-cable or at least balanced-line.

2. Simple and easy installation and wiring. The

positions of sensor units should be changed

along with the rearrangement of the furniture.

Light-weight wire is preferable.

According to the requirements mentioned above,

we adopted a wired network with balanced

equilibrium line (ANSI EIA-422).

4 SPECIFICATIONS

4.1 Sensor Network Topology

Figure 1 shows the physical topology of the sensor

network. There are 8 segments, and each segment

composes a bus network. In the figure, “T” stands

for the terminator, which can be set by jumper plug.

The bus line can be branched as is shown in the

figure (the lower left segment). The number of

sensor nodes is up to 16 for each segment, and then

the total number of sensor nodes is 128.

Figure 2 illustrates the logical topology of each

segment. Sensor nodes and the controller node are

interconnected with balanced equilibrium line (EIA-

422). The controller node plays the role of bus

master, because only one node is permitted to

transmit packets at each moment.

Figure 1: Physical topology of the sensor network.

Figure 2: Logical topology of the sensor network of each

segment.

4.2 Sensor Node

Passive infrared sensor is also called “pyroelectric

infrared sensor” or “infrared motion sensor”. It

detects change in the motion of a person or an object

with a different temperature from the ambient

temperature. It has been used for many applications,

such as automatic security lights, burglar alarms,

visitor acknowledgement, light switch control and

door opener.

We have used NaPiOn (AMN11111, Panasonic

Denko Co. Ltd.) as the sensor module. It includes 16

lenses for gathering infrared radiation to 4 quadrants

on the surface of the pyroelectric infrared detector.

Then, 64 detection zones are formed in front of the

sensor module. The detection area is up to 7.42 m ×

5.66 m on a plane at a distance of 2.5 m from the

sensor. In our system, the detection area of each

sensor was narrowed by a hand-made cylindrical

lens hood. Specifications of the sensor module are

shown in Table 2.

Figure 3 shows the circuit diagram of sensor

node attached on the ceiling.

Figure 3: Circuit diagram of sensor node.

Table 2: Detection performance of the sensor module.

Rated detection distance 5 m (max)

Horizontal detection range 100°

Vertical detection range 82°

Detection zone 64 zones

Movement speed 0.5 m/s (min) 1.5 m/s (max)

Object = human body H 700 mm × W 250 mm

Controller

USB

TTT

MCU

Sensor

MCU

Sensor

MCU

Sensor

MCU

Sensor

MCU

USB

Controller

Balanced Equilibrium Bus

Controller

REN

MAX489ECPD

6,7

+12V

RS422-P

RS422-N

Vcc Vcc Vcc

jumper for

terminator

A0A3

OSCI

OSCO

Vdd

MCLR

Vss

22p

20MHz

22p

910

LED

Vcc

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4.3 Controller Node

The controller consists of MCU (PIC16F873), USB

controller (FT232BM), and eight RS-422 (EIA-422)

transceivers (MAX489), which correspond to 8

segments. Some of these devices are slightly

obsolete, but obtainable by low prices at such as

junk shop. Figure 4 illustrates the circuit diagram of

the controller node. In the diagram, only the signal

lines are indicated.

Figure 4: Circuit diagram of controller node.

Figure 5: The format of packet framing.

Figure 6: Interconnection of sensor nodes with cables.

4.4 Cables for Equilibrium-lines

Figure 6 shows the physical connection of sensor

nodes. We adopted general ready-made phone cable

with modular connectors 6P4C (RJ14) without

shield. The reason is easiness of installation and

wiring. This type of cable is easily obtainable and

light enough to wiring only by connecting to the

plugs and leaving the cable drooping. It is expected

that EMI noise is suppressed by using equilibrium-

line, but further test is necessary.

4.5 Packet

The request from the controller node and the answer

from the sensor nodes are transmitted by packets.

The packet consists of only 8 bits (Figure 5). The

least significant 4 bits contain the address of the

sensor node, which is set using the dip switch

beforehand. If a node detects human motion, Bit 4 is

set when the node transmits the answer packet to the

controller node. Bit 5 toggles the flag of LED on the

sensor node, which blinks along with the human

motion. Bit 7 is usually unset. If time-out occurs, the

controller sets this bit internally.

4.6 Timing of Transmission

Data rate is suppressed to 250 kbps because of the

EMI matter (Figure 7). It takes 36 us to transfer each

packet with a start bit. In order to switch the tri-state

buffer of the equilibrium line, additional 8 us is

required. Consequently, time required to collect the

sensor data is within 90 us per node. If the number

of nodes is 128, the total time is about 12 ms, and

the sampling rate is up to 80 Hz.

Table 3: Command set of the controller.

Command Function

0x01

The controller is reset and all the bits of 8 mask registers

corresponding to the 8 segments are set. One byte is

returned only as acknowledge whose value is 0x00.

0x02

The controller is reset and all the bits of 8 mask registers

corresponding to the 8 segments are set. One byte is

returned only as acknowledge whose value is 0x00.

0x03

The controller sends the “LED ON” commands to all the

sensor nodes. One byte is returned only for acknowledge

whose value is 0x00.

0x04

The controller sends the “LED OFF” commands to all

the sensor nodes. One byte is returned only for

acknowledge whose value is 0x00.

0x05

The controller scans the sensor nodes without

acknowledge.

0x06

8 words (16 bytes) of mask registers are returned.

0x07

8 words (16 bytes) of sensor data are returned.

to PC via USB controller

26 252728

PIC16F873-20/SPRC5

RB4

RB0 RB1 RB2 RB3

RB7 RB5RB6

21 22 23

6P4C-Modular

+12V

6P4C-Modular

+12V

6P4C-Modular

+12V

6P4C-Modular

+12V

6P4C-Modular 6P4C-Modular

+12V

6P4C-Modular 6P4C-Modular

+12V +12V +12V

0: Detect: OFF

1: Detect: ON

0: LED: OFF

1: LED: ON

Address

01234567

Reserved

0: Node is active

1: Node is inactive or not exist

CEILING SENSOR NETWORK FOR SOFT AUTHENTICATION AND PERSON TRACKING USING EQUILIBRIUM

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Figure 7: Waveforms of packet transmission.

4.7 Gathering the Sensor Data

All the data of 128 nodes in the network can be

gathered within 12 ms, but all the addresses are not

necessarily used. The number of nodes may be less

than 128, and there are possibilities that some nodes

get out of order or a node is detached while

operating. We adopted a time-out checking. The

time-out period is set to 50 us. When a time-out

occurs, the corresponding bit of the 16 bit mask

that, the request to the node is skipped. On the other

hand, each mask bit is set compulsorily once every

16 times. It allows the hot-plugging of sensor nodes

without shutting down the network. A newly

plugged node can be detected within 200 ms. Table

3 shows the command set of the controller node.

5 DISCUSSION

If electromagnetic interference can be disregarded,

the wireless sensor network seems to be applicable.

However, usually it is used for discrete data

acquisition, for example in several samples per

second with power saving mode, because of the

limitation of power supply. There have been many

researches, for example, (Bandyopadhyay, 2004),

(Liu, 2006), (Jafari, 2009), and so on. To our

purpose, much higher rate sampling of 80 Hz from

up to 128 nodes are necessary. It means

80×128=10240 samples per second, thus it is

difficult to make use of wireless network.

As is mentioned in the introduction, we are now

examining the integration of various kinds of weak

evidence of soft authentication and person tracking.

Wireless sensor network will be effectively used for

gathering and processing such heterogeneous sensor

data. Also, it will be suitable for tracking inter-room

or ambulant sensor nodes in nursing home, child-

care institution, and so on.

There have been following items to be studied

empirically in current solution of our system:

1. The performance of the authentication and the

tracking increase with the data rate.

2. EMI increases with the data rate.

3. Using shielded cable reduces EMI.

4. Using shielded cable diminishes the easiness

of wiring and installation.

It is necessary to examine above items

experimentally and quantitatively as the near future

work.

6 CONCLUSIONS

We have presented an improved ceiling sensor

network for soft authentication and person tracking.

Sampling rate of 80 Hz for up to 128 nodes using

250 kbps equilibrium line have been realized. The

total sampling rate is up to 10k samples per sec.

Now, the development and improvement of the soft

authentication system using our network system is in

progress. The experimental evaluation of the

performance of tracking persons is also needed.

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