HUMAN PRESENCE DETECTION USING RADIO

IRREGULARITY IN WIRELESS NETWORKS

Human Detection in Energy Aware Residential Networks

Bojan Mrazovac

, Milan Z. Bjelica

, Dragan Kukolj

, Branislav M. Todorović

and Saša Vukosavljev

Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000, Novi Sad, Serbia

RT-RK Institute for Computer Based Systems LLC, Narodnog Fronta 23a, Novi Sad, Serbia

Keywords: Energy awareness, Domotics, RSSI, Radio irregularity, Smart outlets, Wireless sensor networks, Zigbee.

Abstract: The paper presents a human detection method applied to the intelligent device-level software platform for

residential energy management. The proposed solution increases user awareness and automates the power

control, with the primary goal to contribute in energy savings. Instead of using conventional presence

sensors as inputs for automated power management, the proposed solution utilizes a network of wireless

power outlets and monitors the variations of the signal strength indicator used for the communication

between them. The radio signals used for the inter-outlets communication can be scattered, absorbed or

reflected by objects in their propagation paths, such as a human body which additionally increases the

variation of the signal strength indicator at the receiver. This phenomenon is known as radio irregularity,

and is often considered as a shortcoming of radio networks. In this paper the idea of using radio irregularity

as efficient presence detection is proposed. With regard to conventional sensors, this solution preserves the

pervasiveness of smart energy and smart home systems, high level of sensorial intelligence and low

installation costs.

1 INTRODUCTION

Weiser’s dream has already begun (Weiser, 1999).

Our homes became temples of various integrated

services and intelligent devices that provide us with

necessary information, communication and

entertainment. With the capabilities built into

today’s gadgets to adapt their behaviour to a

consumer’s habits and manners, the possibilities are

unlimited. The complex infrastructure of smart

devices follows the trend to become ubiquitous,

seamlessly woven into the fabric of everyday life.

More complex infrastructure requires more

energy to be consumed. The required energy sources

have become exhausted and the solution to preserve

them is to increase user awareness. In order to

decrease the energy wasting, several solutions have

been already proposed. The most popular power

saving solutions are based on wireless smart outlets

(Song et al., 2008). By using smart outlets and web-

based power management services, consumers are

able to monitor the power consumption of each

plugged device and to perform a simple set of

commands (Weiss and Guinard, 2010). It is possible

to display the consumption of each appliance in

conjunction with the costs for a specific time period,

to switch it off or on and to protect from so called

“Vampire power loss” on standby outlets (Han et al.,

2009).

Even the consumers, which have insight into the

consumption data, usually forget to take small

corrective actions to improve the efficiency. The

main issue we indicate in existing systems is that the

energy management is based on a user’s instruction,

without the possibility for the system to adapt

automatically. For instance, the system should be

able to turn off or dim the lights in a room if no

people are present for a period of time. To enable the

system to react automatically, it is needed to

establish the interaction with the environment. This

interaction involves a number of sensors, used

mainly for human detection, that help creation of a

smart home ecosystem. Conventional sensors for

human and motion detection (passive infrared, 3D

camera, ultrasonic…) require additional costs and

installation procedures, complex data processing

algorithms and mostly, burdensome wiring

interfaces (Mrazovac et al., 2011). Technologies that

Mrazovac B., Z. Bjelica M., Kukolj D., M. Todorovi

c B. and Vukosavljev S..

HUMAN PRESENCE DETECTION USING RADIO IRREGULARITY IN WIRELESS NETWORKS - Human Detection in Energy Aware Residential

Networks.

DOI: 10.5220/0003803700050014

In Proceedings of the 1st International Conference on Sensor Networks (SENSORNETS-2012), pages 5-14

ISBN: 978-989-8565-01-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

would affect humans in the future should become

transparent in the environment, ubiquitous and

always prepared to interact. By following such

trends, wireless sensor networks (WSN) are

considered as good solutions. However, instead of

using conventional wireless sensors for presence

detection, another approach would be to use the

impact of a person’s presence on wireless signals

nearby.

Some novel localization techniques analyze the

received signal strength indicator (RSSI). It is the

most applicable solution for WSN due to the low

deployment costs and easy integration into wireless

systems. In this paper, the method for indoor human

detection applied to the smart energy residential

wireless network is proposed. The method utilizes

wireless nodes which are part of pre-existing home

electrical installations (smart power outlets and light

switches) and communication signals nearby. In this

research the nodes have two roles: (a) control of the

plugged devices as well as of giving an overview on

energy consumption; (b) human presence detection

enabled by the analysis of the radio irregularities

between radio transceivers embedded in wireless

nodes. Since human body interferes with the radio

signal, irregularities in radio signatures can be used

as an indication of human presence within a room.

Presented processing algorithm for human detection

is of negligible complexity compared to the

conventional sensors. The proposed method

increases user awareness by enabling a certain level

of sensorial intelligence for automated operations

with the primary goal to achieve the energy savings.

To the best of our knowledge, there are no available

residential smart energy systems that are able to

monitor the consumption and, at the same time, to

detect human presence by analyzing and quantifying

the irregularities in radio signals used for

communication.

In the following section the method of using

RSSI variations for indoor human presence detection

is presented. The method extends an existing smart

energy infrastructure with the level of intelligence

for automated operations. Detailed measurements of

RSSI variations in different environments confirm

the usability of the proposed solution. At the end of

the paper a conclusion with the idea for the future

improvements is given. Experimental results show

the amount of saved energy achieved by using the

presented energy ecosystem combined with the

proposed human presence detection method.

2 DETECTION METHOD USING

RADIO IRREGULARITY

In this section the phenomenon of radio irregularity,

its causes and impact on signal propagation in

wireless networks are defined. In the subsection 2.1

the related work on the radio irregularity and the

relevance of such approach for human activity and

motion detection are shown. In the subsection 2.2

the method for indoor human presence detection is

proposed. The method is used as an integral part of

the energy aware ecosystem.

2.1 General Analysis of Radio

Irregularity – Related Work

Radio irregularity is a common phenomenon in

wireless networks. It arises from multiple factors,

such as different signal radiated powers caused by

hardware calibration and different path losses in

different directions of transmitted signal. Zhou, He,

Krishnamurthy and Stankovic (2004) set a number

of experiments which show that radio irregularity is

mainly caused by two factors: device properties and

the propagation medium. Device properties include

the antenna gain and type, the transmitter’s radiated

power, the receiver’s sensitivity and threshold and

signal-to-noise ratio (SNR). Medium properties

include the background noise and the environmental

factors like obstacles within the propagation media.

One of the major causes of radio irregularity is

the variation in the signal path loss. When the signal

travels through a medium, it may be scattered,

reflected or diffracted. Scattering occurs when the

signal propagates through a medium which contains

a large number of objects smaller than the signal’s

wavelength. Reflection occurs when the signal,

during its propagation through a medium, encounters

an object which is larger than the signal’s

wavelength. Diffraction occurs when the signal

encounters an irregular surface, such as sharp edges.

The signal path loss can be also affected with

hardware imperfections of transceivers. It is possible

that a transceiver does not have the same antenna

gain in all directions. The power supply (battery)

status change also leads to variations in signal

transmitting power by resulting in different signal

strengths at the receiver’s input

Signal strength variations in indoor environments

due to the radio irregularity are even more expressed

when a human body encounters the signal in its

propagation path. The human body is comprised of

skeleton, flesh and body liquids which are able to

additionally absorb, scatter or reflect the radio

SENSORNETS 2012 - International Conference on Sensor Networks

signal. Since human bodies interfere with the radio

signal, the presence of a subject in the wireless

network results in larger signal strength fluctuations

at the receiver input. One of the earliest researches

(Woyach et al., 2006) reports that the shadowing

effect caused by human subject moving in the line-

of-sight path between transmitter and receiver can be

used for human detection. This approach, mainly

based on RSSI variations monitoring at the receiver,

is extended for outdoor people counting mechanism

(Puccinelli et al., 2011). By analyzing the radio

irregularity phenomenon in WSN, Lee et al., (2010)

investigated the feasibility of intrusion detection

based on the signal strength fluctuations. They

succeeded to characterize the signal strength

fluctuations and translate them into sufficient

information that corresponds to a human activity.

The idea of using RSSI fluctuations is applied on an

indoor automated people counting mechanism (Lin

et al., 2011).

It is important to mention that a human body in

the aforementioned researches neither transmit nor

receive any form of wireless signal unlike the

researches presented by Ahn and Yu (2009) and

Chen et al., (2008) and references therein. These

researches present the techniques of localizing

unknown nodes positions and tracking of mobile

wireless devices within a wireless sensors network.

The presented techniques are also “supported” with

the radio irregularity and the variations of the RSSI

between network nodes.

2.2 Presence Detection Method

The primary focus of this method is presence

detection, which means that a subject may be static

within the sensing area. The proposed solution

allows the system to be always aware of human

presence or motion even if a person sits or sleeps

inside a room, making the environment more

comfortable for living. Presence detection method is

mainly based on utilizing the shadowing effects

between stationary wireless nodes which line-of-

sight is obstructed by a human body.

RSSI often fluctuates in different environments

with higher or smaller variations around the mean

value. Experimental analysis presented in section 4

confirms that the RSSI variation over a period of

time is even more expressed when a person is

present. In an empty room, the initial RSSI variation

defines the interval of initial signal strength variation

(ISSV). This interval is set during the system

initialization when wireless nodes communicate with

each other by exchanging the messages and values

of RSSI for each communication link, making the

“radio image” of the environment. During the initial

phase, the sensing area is empty and the RSSI is

only obstructed by the environmental and devices

properties. Signal strength variation in initial

conditions is used to define the high and the low

bounds (thresholds) of ISSV. The ISSV bounds are

calculated based on a set of RSSI samples taken for

a minute. This time interval is not strictly defined; it

is chosen to be one minute because of a round

number of samples obtained from nodes during the

interval. The number of nodes in this research is 4

and the polling time of each node is 100ms. After

the set of one minute samples is formed, the

standard deviation and the expression given in (1),

which calculates the ISSV bounds, are executed.

ISSV bounds algorithm is based on a comparison of

the differences between the mean value (

) of the

set and the set’s min and max values (

xmin/max).

100)/(

maxmin/maxmin//

 xxxISSV

lowhigh

(1)

The comparison of the mean value against the

min/max values defines ISSV bounds whereas

standard deviation is the additional control factor.

These three elements together define the detection

condition. Within the ISSV, RSSI can vary without

recognized detection. When a human enters the

sensing area the RSSI starts to vary greatly by

exceeding the ISSV bounds and deviating from

control factors, which results in detected presence.

Two bounds are used, because the signal’s nature is

such that it can vary below and above its mean

value. The signal deviation can vary significantly

across different environments, making the definition

of universal bounds difficult. That is the main

argument why the bounds definition is necessary

during the initial phase. Bounds are also recalculated

periodically for the case of room layout change

(positioning the furniture). The one minute sliding

window of RSSI samples periodically calculates the

standard deviation based on the received samples.

During the initial phase, the signal deviation is very

small. When a subject enters the room and obstructs

one of the propagation paths the RSSI deviation on

that radio link is increased. After the subject exits

the room, the recalculated signal deviation value is

restored to the initial. If the subject changes the

furniture layout before leaving a room, the new

layout also increases signal deviation. The additional

software mechanism monitors the signal deviation

over period of environment changes (PEC). If the

signal deviation is constant during the PEC interval,

regardless of the initial value, the system detects

room layout change and new ISSV bounds are

HUMAN PRESENCE DETECTION USING RADIO IRREGULARITY IN WIRELESS NETWORKS - Human Detection

in Energy Aware Residential Networks

recalculated based on the last minute of samples.

Realization of the system includes at least two

wireless nodes (outlets or in combination with light

switches). The communication control and the ISSV

analysis are implemented in the associated control

unit (Home Controller - HC) which is made in the

form of an embedded PC. These basic system units

communicate with each other and monitor the ISSV.

At the same time, these components provide the

automated energy management. As the final result

the system controls e.g. lighting in a room, but in

addition to the lighting control, the system can be

preconfigured to control any other device which is

connected to the smart outlets.

The contribution of the presented solution is the

reduction of a number of physical devices and the

elimination of additional sensors (presence sensors

and RFID tags). To the best of our knowledge, this

is the first RSSI variation monitoring algorithm

applied solely to the smart power outlets extended to

detect human presence. Experimental confirmation

of the proposed presence detection method extends

the related researches mentioned in section 2.1

which analyse the techniques for motion detection.

3 THE SMART ENERGY

RESIDENTIAL ECOSYSTEM

The proposed method for human detection is applied

to the smart energy residential ecosystem. The smart

energy ecosystem consists of a home controller

device and a number or smart power outlets and

light switches mounted in existing home electrical

installations. The system analyzes RSSI fluctuations

between wireless nodes (outlets and light switches)

conditioned by human entering or leaving a room (as

illustrated on Figure 1).

Figure 1: Presence detection in smart energy ecosystem.

3.1 The Home Controller

The home controller (illustrated in Figure 2) presents

a software platform based on open standards

(POSIX/C) which provide scalability. The controller

is platform independent, and currently it can be

easily installed on both Windows and Linux OS

platforms. The design of the platform’s modules

ensures that new devices (additional outlets or

switches) can be added seamlessly, without re-

architecture of the platform’s design.

Figure 2: The Home Controller design overview.

The device handler is connected to device drivers

units providing a communication mechanism with

the nodes. This module is in charge of sending the

control messages as the responses on detection

events. The RSSI analyzer module performs periodic

polling of outlets and light switches to retrieve the

current values of RSSI. The RSSI analyzer has

access to the list of nodes’ addresses, polls them in

turn on every 100ms and saves the received values

in the local storage database.

After a node receives the polling command from

the controller it sends its RSSI table as broadcast

message. The message contains a table of RSSI

values toward all links (other nodes) within a room.

During the period of one node polling the other

nodes are in the “listening” mode, so there is no

interference or superposition between them. The

broadcast message is received on the

controller side

as well as by other nodes which then update their

RSSI tables with the values of signal strength from

that link. The nodes are able to receive the message

from the home controller as well as from other

nodes. After the message is received, the RSSI

analyzer saves the received samples into the local

database and waits for the next 100ms, to poll

another node. The procedure is repeated until all

nodes send their RSSI values toward all links. After

the RSSI table is completed, the processing module

calculates standard deviation and ISSV bounds.

SENSORNETS 2012 - International Conference on Sensor Networks

After the bounds are defined, the polling procedure

is repeated for obtaining new samples which are

compared with the predefined bounds. If the samples

exceed the ISSV, the presence is detected.

The concept of behavioural patterns, explained

in details by Bjelica et al., (2011), enables the

energy ecosystem to be used for various setups of an

environment. Behavioural patterns are presented in

the form of XML (Extensible Markup Language)

scripts which define timely actions and respond to

external events, such as human detections, with the

primary goal to achieve desired energy consumption

scheme. The scripting language is similar to high-

level programming languages by having a support

for declaration and usage of variables, loops, if-then-

else constructs, delays and sleep instruction and the

commands for control of smart outlets and light

switches. The script interpreter module (shown as a

block in Figure 2) executes the script and interprets

the behaviour. Mrazovac et al., (2011) described the

design of the smart energy residential ecosystem

which is used as the power management platform,

including its comparison with commercially

available solutions for smart power metering such as

Plogg, Plugwise and digitalStrom. The proposed

algorithm for presence detection can be integrated

into various wireless power outlets and other similar

solutions, by extending their metering purpose with

the detection capability.

The home controller software runs on a PC based

on CPU Intel Atom Z530 1.6GHz with 2GB DDR2

RAM under Linux OS. The communication protocol

is ZigBee (IEEE 802.15.4) which is established by

using CC2531 USB dongle.

3.2 Smart Outlets and Light Switches

Smart outlets and smart light switches, presented by

Mrazovac et al., (2011), fit into existing electrical

installations, standard sockets on the wall. Smart

outlet provides power to electrical devices with

standard flat, two-pole AC power plug, called

Europlug (CEE 7/16) which is designed for voltages

up to 250V and currents up to 2.5A. Besides simple

on/off switching it is able to pass any percentage of

power to the consuming electric devices (e.g. a light

dimmer).

TI CC2530 ZigBee RF transceiver

(2.4GHz) is used as the communication module. It

has an RSSI status register which value represents

the signal strength in 8bit basis. Smart outlets are

powered from 220-240Vac (±10%) 50Hz current

electric power supply. It is the cheapest and the

safest way which provides full compatibility with

the regulatory requirements. With an average current

of 35mA and operational voltage of 3.3V for an

outlet and 2.4V for a switch, the power consumption

is around 0.12W per outlet and 0.08W per switch.

4 EXPERIMENTAL RESULTS

In this section the analysis of RSSI variations caused

by a human presence in two different environments

is presented. The mean value, the standard deviation

and ISSV bounds calculated for a testing time period

are presented. The system described in section 3 has

been installed in two buildings whose walls were

made of different materials: (a) gypsum with

fibreglass isolation, (b) aluminium with plastic

covers and fibreglass isolation. The RSSI variations

in such environments and processing results are

shown in the following subsections. Four nodes have

been used for each experiment, three outlets and one

light switch, which is often room situation.

4.1 Gypsum Wall

The first set of tests was performed in a building

which walls were made of gypsum attached to the

construction elements (steel) and isolated with

fibreglass. The wall thickness was 15 cm. The layout

of a testing room is shown on Figure 3. Red points

illustrate the nodes positions, blue the subject’s

positions and yellow point with green spot illustrates

the central (x=0, y=0) position. Nodes were placed

at an elevation of 40 cm (switch was at 1.20cm)

above the floor.

Figure 3: Room with gypsum walls isolated with

fibreglass.

The room dimensions were 536×530cm. The

distance (in cm) of each node from the central

position is shown in the first two columns of Table

1. The last two columns show the positions of a

subject within the room.

HUMAN PRESENCE DETECTION USING RADIO IRREGULARITY IN WIRELESS NETWORKS - Human Detection

in Energy Aware Residential Networks

Table 1: Nodes’ and subjects positions – gypsum walls

environment.

Node name Distance (cm) Position name Distance (cm)

N1 (73, 211) P1 (0, 78)

N2 (54, 477) P2 (270, 75)

N3 (474, 428) P3 (424, 254)

N4 (519, 66) P4 (306, 420)

- - P5 (120, 255)

The test scenario was the following: the room

was empty for a period of two minutes, and no

detection was reported. Once a subject entered the

room, he performed walking within the room by

passing the positions P shown in the Figure 3. After

one minute of walking, the subject was standing in

each position P for one minute without movements.

The scenario tried to confirm the hypothesis that is

possible to distinguish motion, presence or an empty

room. The raw samples of RSSI variations retrieved

from each node are shown in Figure 4.

N1 links

185

190

195

200

205

210

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI samples

8bit value

N1- >N2

N1- >N3

N1- >N4

Init P1 P2 P3 P4 P5 No humansWalk

N2 links

180

185

190

195

200

205

210

215

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI samples

8bit value

N2-> N1

N2-> N3

N2-> N4

Init P1 P2 P3 P4 P5 No humansWalk

N3 links

180

185

190

195

200

205

210

215

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI sam

les

8bit value

N3-> N1

N3-> N2

N3-> N4

Init P1 P2 P3 P4 P5 No humansWalk

N4 links

180

185

190

195

200

205

210

215

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI sam

les

8bit value

N4-> N1

N4-> N2

N4-> N3

Init P1 P2 P3 P4 P5 No humansWalk

Figure 4: Experimental results for gypsum walls.

From the Figure 4 it can be observed that in the

position P1 the RSSI variation was the highest for

the link N1→N4 in both directions. It is explained as

a result of signal reflection by the human body

which was very close to the line-of-sight between

outlets N1 and N4. In the position P2, the human

body shadowed the links N1→N4 and N4→N1, so

the most of the radio signal was absorbed by the

human body which was the main reason for lower

RSSI values. In the position P2, the links N2→N4

and N4→N2 were distorted with the reflection by

the human body, so the high RSSI variation in the

position P2 for links between outlets N2 and N4 can

be noticed. The position P2 had slight influence on

the links N1→N3 and N3→N1, which were also

distorted by the vicinity of human body which

slightly reflected the signal. The human position P3

mostly absorbed the signal from the links N2→N4

and N4→N2, and also reflected the signals from the

links N3→N4, N4→N3 and N1→N4, N4→N1.

Position P4 shadowed the links N1→N3 and

N3→N1 and absorbed the signal. The position P5

also shadowed the links N1→N3 and N3→N1 and

reflected the signals from the rest of links, except for

N3→N4 and N4→N3 which were far from the

human. At the end of the experiment the room was

empty again for two minutes.

In the Table 2, the standard deviation (Std.

Deviation), the mean value (Mean value) and the

ISSV bounds calculated by (1), are shown.

Calculations are performed over a set of initial two-

minute samples retrieved during the time when the

room was empty.

Table 2: Initial state - gypsum walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 201.99 0.11 0.49 0.01

N1→N3 198.22 0.48 1.13 0.89

N1→N4 194.38 0.52 0.72 0.82

N2→N1 201.74 0.44 0.37 0.13

N2→N3 207 0.07 0 0.48

N2→N4 200.03 0.16 0.01 0.48

N3→N1 198.10 0.31 0.05 0.95

N3→N2 207 0 0 0

N3→N4 208 0.07 0.48 0

N4→N1 194.75 0.4 0.91 1.64

N4→N2 200.13 0.41 1.07 1.41

N4→N3 208 0 0 0

The Table 3 shows ISSV bounds and the signal

strength deviation when the human was standing in

the position P1. The calculations are performed over

a set of one minute samples.

Position P1 was very close to nodes N1 and N4,

so the RSSI variation for this link in both directions

was the highest, because of the body which reflected

the signal, resulting in a higher standard deviation.

ISSV bounds also show the highest values for these

SENSORNETS 2012 - International Conference on Sensor Networks

two links. Comparing with the initial measurements

it can be seen that these values exceed the initial

ISSV bounds and the signal strength deviation. It is

enough that the values exceed the ISSV interval at

only one link and the presence would be reported.

Table 3: Position P1 – gypsum walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 201.05 0.09 0 0.49

N1→N3 198.95 0.13 0.5 0.53

N1→N4 194.29 0.78 1.19 0.36

N2→N1 201 0 0 0

N2→N3 206.99 0.09 0.28 0

N2→N4 200.98 0.13 0.49 0.01

N3→N1 199 0 0 0

N3→N2 207 0 0 0

N3→N4 207.82 0.18 0.4 0.08

N4→N1 194.82 0.59 0.94 0.63

N4→N2 201 0 0 0

N4→N3 208 0 0 0

The position P2 (shown in Table 4) is in the line-

of-sight between nodes N1 and N4, so the most of

the signal was absorbed by the human body, and

also no high deviations were observed. But

reflection by the body detected on the links N2→N4

and N4→N2 has reported detection.

Table 4: Position P2 - gypsum walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 201.47 0.5 0.24 0.26

N1→N3 198.66 0.55 1.36 0.17

N1→N4 193 0 0 0

N2→N1 201.4 0.49 0.2 0.3

N2→N3 207.02 0.12 0.01 0.47

N2→N4 199.09 1.28 1.06 0.95

N3→N1 198.6 0.49 0.3 0.2

N3→N2 207.02 0.12 0.01 0.47

N3→N4 207.91 0.28 0.44 0.04

N4→N1 192.01 0.09 0 0.51

N4→N2 199.13 1.25 1.6 1.42

N4→N3 207.98 0.12 0.48 0.01

Because the paper size is limited, it is not

possible to show the rest of the results for all the

positions, but the conclusion is the same: RSSI for

all nodes that communicate far from the human’s

position vary slightly or has a constant value. When

a human is close to a node, but not in the line-of-

sight, the RSSI varies greatly because of the signal

reflection which is shown to be the most powerful

radio irregularity feature that can report presence in

this environment. When a human shadows the line-

of-sight, the RSSI deviation is very low, and the

signal strength does not exceed the ISSV. But, the

other links which line-of-sight is near are distorted

with the reflection. It is enough that RSSI exceeds

the ISSV only on one link and the detection would

be reported.

4.2 Aluminium Wall with Combination

of Plastic

The second set of measurements was performed in a

building which walls were made of aluminium and

plastic slices with fibreglass isolation. The room

dimensions were 960×580 cm and the wall thickness

was 30 cm. Red points illustrate the nodes positions,

blue the subject’s positions and yellow point with

green spot illustrates the central position. The room

layout is shown in Figure 5.

Figure 5: Room with aluminium and plastic walls.

The distance (in cm) of each node from the

central position is shown in the first two columns of

Table 5. The last two columns show the positions of

a subject within the room.

Table 5: Nodes’ and subjects positions – alu/plastic walls

environment.

Node

name

Distance (cm) Position

name

Distance (cm)

N1 (410,530) P1 (220,305))

N2 (600,530) P2 (410,305)

N3 (795,40) P3 (500,150)

N4 (410,40) P4 (690,150)

- - P5 (945,305)

- - P6 (955, 500)

This environment is interesting because of the

wall structure, which forms a Faraday’s cage so the

signal is strongly reflected by the walls. From the

initial measurements shown on Figure 6 it can be

noticed that the signal strength varies even in an

empty room, so the initial values for Min var and

Max var, including signal strength deviation are

higher than the initial values in previous building.

The test scenario was slightly different from the

scenario explained in the previous subsection. The

HUMAN PRESENCE DETECTION USING RADIO IRREGULARITY IN WIRELESS NETWORKS - Human Detection

in Energy Aware Residential Networks

room was empty for a period of two minutes, and no

detection was reported. Once a subject entered the

room, he was standing in each position P from

Figure 5 for one minute without movements. After

samples from all P positions were collected, the

subject performed one minute walking within the

room by passing the positions P. The raw samples of

RSSI variations retrieved from each node in this

environment are shown in Figure 6.

N1 links

180

185

190

195

200

205

210

215

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI samples

8bit value

N1- >N2

N1- >N3

N1- >N4

Init

P2 P3 P4 P5 Walk No humansP1 P6

N2 links

185

190

195

200

205

210

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI sam

les

8bit value

N2- >N1

N2- >N3

N2- >N4

Init

P2 P3 P4 P5 Walk No humansP1 P6

N3 links

180

185

190

195

200

205

210

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI samples

8bit value

N3- >N1

N3- >N2

N3- >N4

Init

P2 P3 P4 P5 Walk No humansP1 P6

N4 links

190

195

200

205

210

1 104 207 310 413 516 619 722 825 928 1031 1134 1237 1340

RSSI samples

8bit value

N4-> N1

N4-> N2

N4-> N3

Init

P2 P3 P4 P5 Walk No humansP1 P6

Figure 6: Experimental results for alu/plastic walls.

From the Figure 6 it can be observed that in the

position P1 the RSSI variation was low or similar to

the initial state (empty room) for all the links. This is

explained as a result of strong signal reflection by

the walls combined with the human body which was

close to the N1 and N4. In the position P2, the

human body shadowed the links N1→N4 and

N4→N1, so the most of the radio signal was

absorbed by the human body which was the main

reason for lower RSSI values. The position P2 had

slight influence on the links N1→N3, N3→N1,

N2→N4 and N4→N2, which were distorted by the

vicinity of human body which slightly reflected the

signal. The human position P3 reflected the signal

from the links between nodes N2 and N4 and also

reflected the signals from links N2→N3, and

N3→N4 in both directions. Position P4 strongly

reflected the signals on links between nodes N2 and

N4, and also N3 and N4. The position P4 slightly

absorbed the signals between nodes N2 and N3. The

position P5 showed RSSI variations on links

between nodes N1 and N3, which is the most

probably because of the signal reflection by the wall

and human body which were close to each other.

The strongest impact on the signal strength in the

position P5 was noticed for the links between nodes

N2 and N3. The position P5 reflected the signal, and

together with the wall reflection increased the RSSI

variation. The position P6 which was the furthest

position from all nodes, showed very low signal

strength variations. The system did not detect a

subject standing in the position P6 - “blind position”.

Since the room was large, during the nodes

positioning installers did not keep in mind to cover

all “blind positions”. So for larger rooms the

installers should consider installing more outlets for

better radio coverage, which is usually the case for

larger rooms. Otherwise, the system will report false

detections. At the end of the experiment the subject

was walking around the room, trying to move closer

to nodes N2, N3 and N4, and radio links therein,

without obstructing the line-of-sight between nodes

N1 and N2. After the one minute of walking, the

room was empty.

From the Figure 6 it can be observed that the

wall reflection and reflection by the human body

mostly affected signals in this environment. The

Table 6 shows the measurement results for the initial

state. Tables 7 shows the measurement results for

the position P5 which mostly interfered with the

radio signals in this environment. The table 8 shows

the results in position P6 which is the furthest

position from all wireless nodes.

Table 6: Initial state – alu/plastic walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 207.86 0.35 0.41 0.07

N1→N3 192.22 0.41 0.11 0.40

N1→N4 204.03 0.16 0.01 0.47

N2→N1 207.78 0.41 0.38 0.11

N2→N3 204.26 0.45 0.62 0.36

N2→N4 204.82 0.39 0.4 0.09

N3→N1 192.74 0.46 0.38 1.16

N3→N2 204.06 0.53 1.02 0.46

N3→N4 202.47 0.51 0.23 0.75

N4→N1 204.01 0.08 0 0.48

N4→N2 204.81 0.39 0.40 0.09

N4→N3 202.96 0.2 0.47 0.02

SENSORNETS 2012 - International Conference on Sensor Networks

Table 7: Position P5 – alu/plastic walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 207.87 0.34 0.42 0.06

N1→N3 193.04 1.16 0.54 2.5

N1→N4 204.08 0.28 0.04 0.45

N2→N1 207.64 0.48 0.31 0.17

N2→N3 203.74 1.35 2.9 1.57

N2→N4 204.88 0.51 1.42 0.55

N3→N1 193.31 1.07 0.68 0.87

N3→N2 203.68 1.29 1.84 1.6

N3→N4 203.5 0.63 1.25 0.73

N4→N1 204.12 0.38 0.55 0.43

N4→N2 204.84 0.74 1.41 1.04

N4→N3 203.71 0.78 1.35 0.63

Table 8: Position P6 – alu/plastic walls.

Link Mean

value

Std.

Deviation

Min var

[%]

Max var

[%]

N1→N2 207.86 0.35 0.42 0.07

N1→N3 192.26 0.44 0.14 0.38

N1→N4 204.07 0.26 0.03 0.45

N2→N1 207.79 0.41 0.38 0.1

N2→N3 204.25 0.43 0.12 0.37

N2→N4 204.86 0.35 0.42 0.07

N3→N1 192.77 0.42 0.4 0.12

N3→N2 203.84 0.67 0.41 0.56

N3→N4 202.46 0.50 0.23 0.27

N4→N1 204.02 0.23 0.5 0.48

N4→N2 204.84 0.52 1.41 0.56

N4→N3 202.96 0.18 0.48 0.02

The results in this room are different from those

in the first building. The reflection by the human

body interfered with the walls reflection mostly

affected the radio signals. But even in such

environment, the presence and motion detection can

be easily recognised.

5 POWER MANAGEMENT

EXPERIMENT

The presented human detection method implemented

for residential smart energy management was

analyzed in the experiment of controlling 7 bulbs of

100 Watts. The regular control included the worst

case when a user leaves the light on, after leaving a

room. The presented system for energy consumption

control switched off the light automatically after 5

seconds if no humans were detected within a room.

In the Figure 7 the achieved power savings in the

testing house (110m²) applied to the lighting control

are shown. The test has been performed during one

working day with four-member family (two adults

and two kids). The house contained three bedrooms,

one kitchen and dining room, one bathroom, one

foyer and one living room. Test subjects performed

normal behaviour at home, trying to manually save

the electric energy by switching off the lights in all

empty rooms (shown as a blue line in Figure 7). In

each room one lamp was plugged to a smart power

outlet and one to a standard power outlet. Smart

power outlets with plugged lamps were under

automatic switch control, whereas standard power

outlets were under users’ manual control. Supported

with the proposed presence detection algorithm the

energy consumption used for lights was lower from

1260 Watts to 780 Watts at the end of the day.

100

150

200

250

300

350

Hour (t)

Watts (W)

Without energy saving

With energy saving

Figure 7: The energy saving experiment supported with

the proposed presence detection method.

6 CONCLUSIONS

In this paper the novel method for human detection

applied to an energy aware residential network is

presented. The method utilizes radio irregularity

phenomenon to detect human presence. To the best

of our knowledge this is the first paper presenting

the human presence detection achieved by using a

network of smart power outlets and light switches

within a room. The presented analysis confirms the

hypothesis that the presence detection is possible by

monitoring the radio signal variations between

wireless power outlets. The proposed solution

defines the RSSI variations bounds during the

system initialization and periodically if the static

signal deviation is noticed. When a human enters the

sensing area, RSSI values exceed the interval’s

thresholds resulting in reported detection. It can be

concluded that for larger rooms, the level of false

detections increases. It is shown that some positions

in a large room were out of the detection scope. For

that case the detection accuracy was 86%. For the

standard room dimensions, the detection accuracy

was 100%. Correct detection requires good radio

coverage within a room which depends on the

nodes’ positions. As the future improvement the

authors will try to replace the ISSV definition

algorithm with another metric which will recognize

a presence in the system by using a single parameter.

The benefit of such an approach would facilitate the

installation of a larger number of additional smart

HUMAN PRESENCE DETECTION USING RADIO IRREGULARITY IN WIRELESS NETWORKS - Human Detection

in Energy Aware Residential Networks

outlets without changing the core of the processing

algorithm. The energy savings achieved by using the

smart energy ecosystem supported with the proposed

detection method are significant. Authors believe

that this idea will encourage other manufacturers to

apply the presented approach to their smart meters

and help the global awareness for energy saving.

ACKNOWLEDGEMENTS

This work was partially supported by the Ministry of

Education and Science of the Republic of Serbia

under the Grant projects TR-36029, TR-32034 and

TR-32041, year 2011.

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SENSORNETS 2012 - International Conference on Sensor Networks