ZiLoc: Energy Efficient WiFi Fingerprint-based

Localization with Low-Power Radio

Jianwei Niu∗, Banghui Lu∗†, Long Cheng∗†, Yu Gu†, Lei Shu‡

∗State Key Lab of Software Development Environment, Beihang University, Beijing 100191, China†Singapore University of Technology and Design, Singapore‡Guangdong University of Petrochemical Technology, China

{niujianwei, lu, chenglong}@buaa.edu.cn, jasongu@sutd.edu.sg, lei.shu@ieee.org

Abstract—Indoor localization is essential to enable location-based services in wireless pervasive computing environment. Inrecent years, WiFi fingerprint-based localization has receivedconsiderable attention due to its deployment practicability. Inorder to achieve on-the-fly localization, WiFi receivers (e.g.,mobile phones or laptops) being located need to scan WiFisignals continuously. Since they are normally battery driven,energy efficiency is a very important consideration in WiFifingerprinting localization systems. Motivated by the fact thatIEEE 802.11 (WiFi) and 802.15.4 (ZigBee) channels overlapin the 2.4GHz ISM band, in this work, we develop a WiFifingerprint-based localization system using ZigBee radio, calledZiLoc. We first present a novel RSS-location fingerprint modelto identify the features of surrounding APs. We then proposea simple yet effective method to compute the similarity of twoRSS fingerprints. Experimental results demonstrate that ZiLoccan achieve an average of 85% room-level localization accuracyand reduce more than 60% energy consumption compared withthe method using WiFi interfaces to collect RSS fingerprints.

I. INTRODUCTION

Location-based service (LBS) has a huge market potential,

such as navigation, tracking, and mobile advertising. One of

the key issues for LBSs is the accurate and efficient localiza-

tion. Since people tend to stay most of the time indoors (either

at work or at home), there is a growing demand for indoor

localization systems. While GPS (Global Positioning System)

is the most popular positioning system, it is not suitable for

indoor area due to the poor coverage. For this reason, various

indoor localization techniques have been proposed [1]–[6].

We are witnessing a ubiquitous deployment of 802.11 WiFi

access points (APs), in universities, offices, city parks, restau-

rants, and homes. Meanwhile, WiFi enabled portable devices,

such as laptops, mobile phones and tablets, have become

very common with the extensive coverage of 802.11 WLAN.

Therefore, WiFi fingerprint-based localization is considered

as a practical solution since it allows the design of an easily

deployable infrastructure-less low-cost localization system. A

WiFi-fingerprinting system works in two phases: an offline

training phase and an online localization phase. The first phase

collects fingerprints (RSS measurements of WiFi APs) from

different pre-known locations and stores them to a database

as the training set. The second phase infers the location

based on the observed RSS measurements, through finding the

closest match in the database. However, to achieve realtime

localization, WiFi receivers (e.g., mobile phones or laptops)

being located are required to scan WiFi signals frequently,

which is extremely power-hungry for battery driven mobile

devices.

1 6 11

WiFi Channels

ZigBeeChannels

2 3 4 5 7 8 9 10 12 13 14

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

2405 2410 2415 2420 2425 2430 2435 2440 2445 2450 2455 2460 2465 2475 24802470 (MHz)

Fig. 1. 802.11 and 802.15.4 channels overlap in 2.4GHz ISM band

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0 10 20 30 40 50 60

Cu

rre

nt

dra

w (

mA

)

Time (s)

WiFi NIC

ZigBee radio

Scanning

IdleScanning

Idle

Fig. 2. Our measurement shows WiFi NIC (network interface card) in the idlestate could consume more energy than Zigbee radio when scanning channels

Motivated by the fact that 802.11 and 802.15.4 channels

overlap in the 2.4GHz ISM band (as shown in Fig. 1), and the

low-power feature of 802.15.4-compliant radio transceivers,

this paper is an attempt to explore the 802.15.4 radio to collect

WiFi signals, where the objective is to improve the energy

efficiency of WiFi-fingerprinting localization. Fig. 2 illustrates

the power consumption comparison between ZigBee radio and

WiFi radio. The low-power operation of 802.15.4 radio makes

it attractive for a wide range of instruments, e.g., a plug &

play support for Zigbee radio on mobile phones has been

proposed [7]. We develop a WiFi fingerprint-based localization

system using ZigBee radio, called ZiLoc. Two important issues

are addressed in ZiLoc, the RSS-location fingerprint modeling

and location estimation. We implement a testbed to verify

the effectiveness of ZiLoc design. Three different k-Nearest

Neighbor (KNN) [8] classification methods are compared to

evaluate the location estimation performance. Experimental

results show that ZiLoc can effectively achieve 85% room-

level localization accuracy on average and save around 66%

energy compared with the WiFi NIC-based method.

The outline of the paper is as follows. Section II surveys

the related work. Section III elaborates the ZiLoc design in

details. Section IV provides the experimental results. Finally,

conclusions are drawn in Section V.

II. RELATED WORK

Indoor localization is a critical enabler for LBS application-

s. Traditional indoor localization solutions normally require

dedicated infrastructures based on infrared [9], RFID [10],

or acoustic sensors [11]. Recently, there has been growing

interest in utilizing in-building communication infrastructures

(e.g., WiFi, WiMAX or FM) for indoor localization, especially

the WiFi fingerprint-based indoor localization techniques. The

idea of fingerprint-based localization is to utilize the received

signal strength (RSS) to estimate the location of a receiver. The

main challenging issue for accurate fingerprint-based indoor

localization is the design of robust and discriminative RSS

signatures. In [12], the authors report a comparative survey of

WLAN location fingerprinting methods.

With the proliferation of wireless technologies, several ap-

proaches have been proposed to promote the WiFi and ZigBee

coexistence [13], [14]. In [15], the authors present an analysis

of the IEEE 802.15.4 and 802.11 interference patterns at 2.4

GHz ISM band, and then design a MAC layer solution that

enables 802.15.4 nodes to coexist with WiFi networks. Li et al.

present WiBee, a method to build a WiFi radio map with Zig-

Bee sensor nodes [16]. Through empirical study, the authors

reveal that the difference of the ZigBee RSS and the WiFi RSS

is almost a constant. Our work differs from WiBee in that, we

present a complete design and implementation of a localization

system with ZigBee radio, including the training set collection,

RSS-location fingerprint modeling, RSS fingerprint similarity

measurement, and location determination. While WiBee only

focuses on how to match the RSS detected by ZigBee radio

with RSS detected by WiFi NIC, in which a linear model

between them is proposed.

III. ZILOC DESIGN

A. Motivation

−100

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−40

0 500 1000 1500 2000 2500 3000

RS

S V

alu

e (

db

m)

Sequence Number of RSS Samples

Fig. 3. WiFi signals detected by a ZigBee radio

There exists frequency overlap across 802.11 based WiFi

and 802.15.4 based ZigBee on the 2.4GHz ISM band. How-

ever, the transmission power of WiFi senders typically are

10 to 100 times higher than that of ZigBee senders [15],

leading to much higher energy consumption when collecting

WiFi signals. This motivates us to design a WiFi-fingerprinting

localization system using the 802.15.4 radio to collect WiFi

signals for the energy saving. Since the channel frequencies

of off-the-shelf 802.15.4 radios (e.g., CC2420 [17]) can be

programmed, it’s practical for ZigBee radios to sense the

spectrum usage of WiFi network when WiFi and ZigBee radios

operating at the same or adjacent frequencies. Besides, beacon

frames are transmitted periodically to announce the presence

of an AP in 802.11 infrastructure networks. Therefore, we can

use ZigBee radio to detect the beacon signal strength of WiFi

channels and find periodic features of beacon frames through

folding of the RSS samples [14]. Fig. 3 shows an example

that WiFi signals can be easily detected by a ZigBee radio.

−10.00

−5.00

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Devia

tion (

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Time (hour)

(a) The relative deviation between themeasurements and the standard bea-con interval 102.4ms

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CD

F

Deviation (ms)

First 8 HoursMiddle 8 Hours

Last 8 Hours24 Hours

(b) CDF of relative deviation at threedifferent periods in a day

Fig. 4. Stability of 802.11 beacon frame period

Fig. 4 illustrates the relatively stable beacon frame period

of an AP. In this experiment, we use the sniffier tool wireshark

[18] and WiFi equipped laptop to measure the timing accuracy

of the beacon transmissions of a specific AP. During a period

of 24 hours, the laptop records all overheard beacon frames

with reception time stamps. To evaluate the impact of WiFi

traffic load on the beacon period, we divide the beacon

frame flows into three sections and each section is analyzed

separately.

We measure the interval between the reception timestamps

of two consecutive beacons. The relative deviation between the

measurements and the standard beacon interval (102.4 ms) are

reported in Fig. 4. We observe the period of beacon frame sent

by an AP is fairly stable. Thus the relative phases of APs can

be regarded as a feature of fingerprint. In other words, any two

different APs have the same relative phase when they appear

in different fingerprints. ZiLoc utilizes the stability feature

of relative phases for the similarity measurement of different

fingerprints.

B. System Architecture

RSS measurements with

unknown location

RS

S s

ampl

ing

RS

S fo

ldin

g

Fingerprintcollector

RS

S s

hapi

ng

Zig

Bee

rad

io

Locationdetermination

KNN based classification

Marked location

fingerprintTraining set

Offline

Online

Fin

gerp

rint m

odel

ing

Fig. 5. ZiLoc Architecture Overview

The ZiLoc architecture overview is shown in Fig. 5. As

a conventional fingerprint-based localization system, ZiLoc is

based on a client-server architecture. It is composed of offline

and online phrases. The client-side, e.g., a mobile device with

built-in ZigBee interface, serves as the fingerprint collector in

the offline phase. The fingerprint collector first samples the

RSS of a specific WiFi channel, leveraging the overlapped

frequency between ZigBee and WiFi networks. Then, the

client-side adopts the same approach in [14], called Common

Multiple Folding (CMF), to identify an AP and capture its

signal strength. Finally, a fingerprint is extracted from fuzzy

RSS samples obtained by ZigBee radio.

For each individual interested indoor location, a certain

number of location fingerprints marked with position labels

are collected and reported to the server. After collecting

enough fingerprints, the RSS fingerprint training set (also

called radio map or fingerprint database) is built. During the

online phase, the observed RSS measurements associated with

an unknown position is transmitted to the backend server-side.

In the location determination module, ZiLoc utilizes KNN-

based classification method [8] to assign an estimated position

to the unmarked fingerprint. Then, the server-side sends back

the determined location to the ZiLoc client to achieve real-time

indoor localization and tracking.

A key challenge in ZiLoc design is the similarity mea-

surement of raw fingerprints. Although we can identify the

existence and signal strength of an AP beacon with ZigBee

radio, the identification, e.g., BSSID, of each AP cannot be

obtained. We address this issue by leveraging the relative phase

between two AP beacons, which will be introduced in detail

in Section III-D.

C. RSS Sampling, Shaping and Folding

We use TelosB [19] mote equipped with ZigBee-compliant

CC2420 radio to sample WiFi signals. From [14], the sampling

period is set as 122µs. Based on our testbed experiment,

we find that the default WiFi beacon transmission period is

102.4ms1, during which around 839 RSS samples can be

collected.

We briefly introduce the shaping and folding methods of

CMF, full details can be found in [14]. Since each WiFi AP

broadcasts beacon frames periodically, a ZigBee radio may

scan multiple APs’ beacon signals in a certain period (e.g.,

3s). Unlike conventional approach to collect the AP signals

using WiFi NICs, ZigBee radios can neither decode any WiFi

beacon, nor determine if a signal is associated with a specific

AP. Consequently, it is non-trivial to distinguish multiple AP’s

signals. In our design, to obtain RSS-location fingerprint, we

keep scanning 802.15.4 channel 17 for N (e.g., 30) beacon

periods2. It will take about N ∗ 102.4ms to complete this

process, and around N ∗K samples can be collected, where

K is the total number of RSS samples collected in a beacon

period. We partition all the samples into N groups, and denote

each group as an RSS vector Ri[k] (i ∈ [1, N ], k ∈ [1,K]),where i is the group number and k is the sample index in a

group.

To identify the presence of periodic AP beacon frames, RSS

shaping for each vector is performed. An RSS sample is set

to 0 if it is below −90dBm, otherwise, its value is set to 1

1The measurement is consistent with the results reported in [14]. Actually,the WiFi beacon transmission period can also be configured.

2The corresponding WiFi channel number is 6 as shown in Fig. 1, andchannels 1, 6, and 11 are the most common channels in WiFi networks.

1 0 1 ... 0 1 0

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1 1 0 ... 1 1 0

1 0 1 ... 0 1 1

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+

...

R'1

R'2

R'N-1

R'N

R*

1 2 3 ... K-2 K-1 K

+Folding

Shaping

Fig. 6. Example of the RSS shaping and folding

indicating the busy channel. Let R′

i[k] denote the shaped RSS

vector. Then, we fold R′

i[k], calculated as Eq. 1, and get the

normalized folded RSS vector R∗[k].

R∗[k] =1

N

N∑

i=1

R′

i[k], k ∈ [1,K] (1)

As shown in Fig. 6, R∗[k] can be considered as the probability

that an AP beacon frame is captured in the RSS series. For

obtaining the signal strength of AP beacon frames, we fold

the raw RSS vectors Ri[k] and get R[k], calculated as Eq. 2.

Each element of R[k] denotes the average signal strength of

a sampling point.

R[k] =1

N

N∑

i=1

Ri[k], k ∈ [1,K] (2)

D. RSS-location Fingerprint Modeling

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Avg

. N

orm

aliz

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SS

Phase

Phase:100 Scan 1Scan 2

(a) We can easily find the relative phase of each AP withthe RSS folding results

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. R

aw

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AP1

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Scan 1Scan 2

(b) RSS-location fingerprints are extracted

Fig. 7. Example of fingerprint modeling

R∗ are the normalized (also magnified) WiFi RSS series

and each periodic AP beacon signal is expected to generate

a peak in R∗. We consider that a peak in R∗ represents the

presence of an AP if the strength is larger than a threshold

α (we set α an empirical value as 0.5). Fig. 7(a) illustrates

the folded RSS series of two scans (each scan lasts 3072msin our experiment). As shown in the figure, after filtering out

the noises, 6 different AP beacons can be identified in each

scan. We observe that the RSS series in different scans show

similar signal envelopes, and the time difference between two

Sa

Sb

Match

Sa

Sb

Match

(b)(a)

APa1 APa2 APa3 APa4 APa5

APa1 APa2 APa3 APa4 APa6APa5

APa1 APa2 APa3 APa4 APa5

APa1 APa2 APa3 APa4 APa5APa6

Fig. 8. Example of matching two fingerprints

AP beacons within one scan is relatively stable. Let us define

the relative offset of a peak against the sampling start point as

the phase (f ) of an AP beacon. f is calculated by averaging

those adjacent samples whose strength are larger than α around

a peak. After obtaining the phase of an AP beacon, we can

easily get its signal strength h from R[k].

h = R[f ′] (3)

where f ′ is the closest sampling point to f in the RSS series.

Fig. 7(b) shows the corresponding signal strength of different

AP beacons in Fig. 7(a).

Since the AP beacon period and the time difference between

two AP beacons are stable, so the relative phase, i.e., the

relative distance of two peaks, also holds the stable feature.

We find that any AP can be uniquely identified by using a two-

tuple (phase, strength) in an RSS series. With this observation,

we use phase fi and strength hi to identify APi in one scan.

An RSS-location fingerprint is modeled as a set of two-tuples,

denoted by S : {(fi, hi)|i ∈ [1, n]}, where n is the number of

identified peaks in one scan.

E. Location Estimation

Given two fingerprints Sa and Sb, we now describe the

similarity measurement method between them. The distance,

which is the output of similarity measurement, will be used in

k-Nearest Neighbor (KNN) classification algorithm in ZiLoc.

The similarity measurement is composed of two steps. First,

we perform vector conversion of Sa and Sb. Then, we compute

the distance (similarity) of Sa and Sb.

1) Vector Conversion: Let |Sa| = na and |Sb| = nb denote

the number of identified APs in Sa and Sb, respectively.

|Sa

⋂

Sb| = nab represents the number of common APs that

both Sa and Sb contain. Since the relative phases among

APs within a fingerprint are stable, if nab ≥ 2, we can find

the best match between Sa and Sb in terms of the phase

(not considering the signal strength) through cyclic shift. As

showed in Fig.8, we cyclically shift the phase sequence of Sb

and align it with Sa. Then, we can get the number of matched

APs. Fig.8(a) only has 2 matched APs, while Fig.8(b) shows

the best matching case. After na∗nb attempts, we can find the

best match with the largest number of matched APs. If there

exist more than one best matchings with the same number of

matched APs, we randomly select one as the best match.

Since some APs in Sa may not have corresponding APs in

Sb, as shown in Fig. 9, we add virtual APs with fixed signal

strength mR in order to let Sa and Sb have the exactly same

AP sequences. In our experiment, we set mR as −110dBm.

Given the best match between Sa and Sb, we build the signal

Sa

Sb

APa1

(fa1,ha1)APa2

(fa2,ha2)APa3

(fa3,ha3)APa4

(fa4,ha4)APa5

(fa5,ha5)

APb1

(fb1,hb1)APb2

(fb2,hb2)APb3

(fb3,hb3)APb4

(fb4,hb4)APb5

(fb5,hb5)

Va ha1 ha2 mR ha3 ha4 ha5 mR

hb1 hb3 hb4Vb mR hb2 mR hb5

Virtual AP

Fig. 9. Example of fingerprint vector conversion

strength vectors Va and Vb after inserting virtual APs into the

fingerprints. For example, as shown in Fig. 9, both Sa and Sb

contain 5 APs, but their relative phases are different. In this

case, after getting the best match and performing the vector

conversion, we have Va = (ha1, ha2,mR, ha3, ha4, ha5,mR),and Vb = (mR,hb1, hb2, hb3,mR, hb4, hb5).

2) Similarity Computing: From the first step, we get the

signal strength sequences Va and Vb ordered by the phase,

respectively. Now, different methods can be applied to measure

their distance, which is actually the similarity of Sa and Sb. We

investigate two methods in ZiLoc, one is the Euclidian distance

as Eq. 4, and the other one is the Manhattan distance as Eq.

5. We mark the similarity of any two arbitrary fingerprints Sa

and Sb as Distance(Sa, Sb) = d(Va, Vb).

d(Va, Vb) =

√

n∑

i=1

(ha,i − hb,i)2

(4)

d(Va, Vb) =n∑

i=1

(|ha,i − hb,i|) (5)

Manhattan distance is also called city-block distance be-

cause it is the distance in blocks between any two points in

a city. An influential hypothesis is that Euclidian distance is

valid when the dimensions are perceptually integral, whereas

city-block distance is appropriate when dimensions are percep-

tually separable [20]. In this work, the dimensions are actually

the signal strength of surrounding APs, hence they are not

integral dimensions to some extent.

IV. EXPERIMENTATION

In this section, we evaluate ZiLoc performance in room level

and compare different KNN classification algorithms. KNN

classification [8] is a method for classifying objects based on

majority vote, with the object being assigned to the class most

common amongst its k nearest neighbors. Three different KNN

algorithms that are used in this work are shown as following.

• Euclidian-KNN: The conventional Euclidian distance is

used to measure the similarity of fingerprints.

• Euclidian-DW-KNN: This scheme also uses Euclidian

distance for similarity measurement. However, we as-

sign different fingerprint distances with different weights,

which is to mitigate the noisy fingerprints. Given the

distance Distance(Sa, Sb) between two fingerprints Sa

and Sb, its weight is calculated as 1/Distance(Sa, Sb)2.

• Manhattan-DW-KNN: It differs from Euclidian-DW-KNN

in that the Manhattan distance is used for fingerprint

similarity measurement.

We also report the performance of fingerprinting-based

localization using WiFi NIC to scan the MAC address and AP

RSS. In this test, we collect the same number of fingerprints

as ZiLoc experiment, and use the Manhattan-DW-KNN as the

classification algorithm.

A. Experimental Setup

1048

1049

1028

1030

1038

1039

1045 1044 1043 1042

Fig. 10. Floor plan of the experimental environment

Our experiment was conducted on the 10th floor of the New

Main Building at Beihang University, Beijing, China. The floor

plan is showed in Fig. 10 and the area of each room is about 30

square meters. There are 10 rooms involved in our experiment.

In our experiment, we didn’t deploy any extra AP and only

scanned the existing WiFi infrastructures. The devices used

to collect location fingerprints include a ThinkPad laptop and

a TelosB mote connected to the USB port of laptop. Based

on our field survey, a considerable number of APs work on

channel 6, so we set the ZigBee radio to scan channel 17,

which has a full overlap with WiFi channel 6. There are at

least 6 APs and 11 APs on average can be detected within

a room. For each room we collect 18000 fingerprints and we

randomly select 3000 as test samples, while the rest 15000 are

used to build training set. Each fingerprint is extracted from

the RSS samples of 30 beacon periods, so it costs about 3s to

collect one fingerprint.

B. ZiLoc vs. WiFi NIC-based method

In this comparison, the k value is fixed to 10 in KNN

classification. We report the ZiLoc performance in terms of

localization accuracy and energy consumption in Fig. 11 and

Fig. 12, respectively.

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Acc

ura

cy

Room NumbersEuclidian-KNN

Euclidian-DW-KNN

Manhattan-DW-KNN

Manhattan-DW-KNN (WiFi NIC)

Fig. 11. Localization accuracy of room level evaluation

1) Localization Accuracy: Fig. 11 shows the localization

accuracy of different methods. Overall, ZiLoc under three

classification algorithms can provide comparable performances

to the WiFi NIC-based method. DW-KNN algorithm outper-

forms the one without distance-weighted KNN. It is interesting

that Manhattan-DW-KNN shows a higher accuracy compared

to the other two classification algorithms. WiFi NIC-based

method shows around 88% accuracy on average, while ZiLoc

with DW-KNN algorithm can achieve an average of 85%

accuracy, which is very close to the accuracy of WiFi NIC-

based method.

2) Energy Consumption: To measure the practical respec-

tive energy consumption of ZigBee radio and WiFi radio, we

use an external WiFi NIC. Fig. 12(a) shows the measurement

equipments in our experiment. Fig. 12(b) plots the energy

consumption as the time elapsed. As seen in the figure, ZiLoc

costs only about 34% energy of the WiFi NIC-based method.

(a) Measurement equipments

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y C

on

su

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tio

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J)

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ZigBee Radio

(b) Energy consumption

Fig. 12. Comparison of energy consumption

C. Impact of k in KNN

Fig. 13 shows the localization accuracy of each individual

room with the change of k values. As shown in Fig. 13(b),

room 1030 has the highest and most stable classification

accuracy. This is because, this room is relatively isolated

and far from the other interested rooms as seen in Fig. 10.

Intuitively, the fingerprints in room 1030 would show more

differences from those fingerprints in other rooms, hence it

suffers fewer interference when performing the classification.

Surprisingly, we find that those rooms, whose two neighboring

rooms are also involved into our localization experiment, such

as 1043, 1044 and 1049, can achieve a relatively higher

accuracy. In contrast, the other rooms, that have only one

neighboring room involved, such as 1028, 1038, 1039 and

1042, show a comparatively lower accuracy.

The average accuracy of the three KNN algorithms with

different k values are showed in Fig. 14. We can see that all

the three algorithms are descending along with the increase of

k values. It is interesting that when k is small, e.g., less than

5, there is a small increase of the localization accuracy. This

indicates our methods can get the best performance when k is

small. While when k is larger than 6, the accuracy of all the

three algorithms are descending with the increase of k. Thisis because the sample spaces of different rooms are highly

correlated with each other. Therefore, when more neighbors

are involved into the classification, it also brings noises.

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0.2

0.4

0.6

0.8

1

1 200 400 600 800 1000

Accura

cy

k

Euclidian-KNNEuclidian-DW-KNN

Manhattan-DW-KNN

(j) 1049

Fig. 13. Accuracy of each room with different k values

0

0.2

0.4

0.6

0.8

1

1 200 400 600 800 1000

Accura

cy

k

Euclidian-KNN

Euclidian-DW-KNN

Manhattan-DW-KNN

0.75

0.8

0.85

0.9

2 4 6 8 10 12 14 16 18 20

Fig. 14. Average accuracy with different k values

Remarkably, for any k values, Manhattan-DW-KNN algorithm

always shows the highest accuracy. With these observations,

we conclude that Manhattan-DW-KNN algorithm is a simple

yet effective algorithm in ZiLoc.

V. CONCLUSION

In this work, we have developed ZiLoc system, an energy

efficient WiFi fingerprint-based localization with ZigBee radio.

We present a novel RSS-location fingerprint model to uniquely

identify APs’ beacon signal features. We then propose a simple

yet effective method to compute the distances between the

testing and training RSS fingerprints. We have implemented

ZiLoc, and conducted extensive testbed experiments to study

the performances. Interestingly, through experimental results,

we find that Manhattan distance is more suitable for RSS

fingerprint similarity measurement than the Euclidian distance.

Experimental results also demonstrate that ZiLoc can achieve

an average of about 85% room-level localization accuracy and

reduce around 66% energy consumption compared with WiFi

NIC-based method when collecting RSS fingerprints.

ACKNOWLEDGEMENTS

This work was supported in part by the Research Fund of the StateKey Laboratory of Software Development Environment under GrantNo. BUAA SKLSDE-2012ZX-17, National Natural Science Foun-dation of China under Grant No. 61170296, 61190120, Program forNew Century Excellent Talents in University under Grant No. NECT-09-0028, Singapore-MIT IDC IDD61000102a, IDG31100106a, andNRF2012EWT-EIRP002-045.

REFERENCES

[1] R. Want, A. Hopper, V. Falcao, and J. Gibbons, “The active badgelocation system,” ACM Trans. Inf. Syst., vol. 10, no. 1, pp. 91–102,Jan. 1992.

[2] T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher,“Range-free localization schemes for large scale sensor networks,” inProc. MobiCom ’03, 2003.

[3] K. Whitehouse and D. Culler, “A robustness analysis of multi-hopranging-based localization approximations,” in Proc. IPSN ’06, 2006.

[4] M. Youssef, M. Mah, and A. Agrawala, “Challenges: device-free passivelocalization for wireless environments,” in Proc. ACM MobiCom ’07,2007, pp. 222–229.

[5] D. H. Kim, Y. Kim, D. Estrin, and M. B. Srivastava, “Sensloc: sensingeveryday places and paths using less energy,” in Proc. ACM SenSys ’10,2010, pp. 43–56.

[6] S. P. Tarzia, P. A. Dinda, R. P. Dick, and G. Memik, “Indoor localizationwithout infrastructure using the acoustic background spectrum,” in Proc.

MobiSys ’11, 2011, pp. 155–168.[7] C. Chen, X. Zhang, J. Zhang, and Y. Tang, “usd card: a plug play

solution for mobile device to access wireless sensor networks,” in Proc.

WASA’11, 2011, pp. 354–365.[8] V. Nitin Bhatia, “Survey of nearest neighbor techniques,” Journal of

Computer Science and Information Security, vol. 8, no. 2, 2010.[9] D. Hauschildt and N. Kirchhof, “Advances in thermal infrared localiza-

tion: Challenges and solutions,” in Proc. IPIN ’10, 2010, pp. 1–8.[10] A. Jimenez Ruiz, F. Seco Granja, J. Prieto Honorato, and J. Gue-

vara Rosas, “Accurate pedestrian indoor navigation by tightly couplingfoot-mounted imu and rfid measurements,” IEEE Transactions on In-

strumentation and Measurement, vol. 61, no. 1, pp. 178–189, jan. 2012.[11] E. Mangas and A. Bilas, “Flash: Fine-grained localization in wireless

sensor networks using acoustic sound transmissions and high precisionclock synchronization,” in Proc. ICDCS ’09, 2009.

[12] V. Honkavirta, T. Perala, S. Ali-Loytty, and R. Piche, “A comparativesurvey of wlan location fingerprinting methods,” in Proc. WPNC ’09.

[13] J. Huang, G. Xing, G. Zhou, and R. Zhou, “Beyond co-existence:Exploiting wifi white space for zigbee performance assurance,” in Proc.

ICNP ’10, 2010, pp. 305–314.[14] R. Zhou, Y. Xiong, G. Xing, L. Sun, and J. Ma, “Zifi: wireless lan

discovery via zigbee interference signatures,” in Proc. MobiCom ’10.[15] C.-J. M. Liang, N. B. Priyantha, J. Liu, and A. Terzis, “Surviving wi-fi

interference in low power zigbee networks,” in Proc. SenSys ’10, 2010.[16] W. Li, Y. Zhu, and T. He, “Wibee: Building wifi radio map with zigbee

sensor networks,” in Proc. INFOCOM ’12, 2012.[17] “Cc2420: 2.4 ghz ieee 802.15.4 zigbee-ready rf transceiver.” [Online].

Available: http://www.ti.com.cn/cn/lit/ds/symlink/cc2420.pdf[18] “wireshark.” [Online]. Available: www.wireshark.org[19] “Telosb datasheet.crossbow technology inc.” [Online]. Available:

http://www.xbow.com.cn/wsn/pdf/TelosB Datasheet.pdf[20] D. M. Ennis, F. Gregory Ashby, “Similarity measures,” Scholarpedia,

vol. 2, no. 12, 2007.