Study of Received Signal Strength Indication in

ZigBee Location Cluster for Indoor Localization Kamol Kaemarungsi#, Rachasak Ranron*, Prasit Pongsoon#

#National Electronics and Computer Technology Center (NECTEC)

Pathumthani, Thailand 1kamol.kaemarungsi@nectec.or.th 3prasit.pongsoon@nectec.or.th

*King Mongkut’s University of Technology North Bangkok (KMUTNB)

Bangkok, Thailand 2ratchasak.ranron@gmail.com

Abstract— This work focuses on the properties of received signal strength indication (RSSI) of ZigBee wireless sensor network, which can be used to implement indoor location system. The study collects a set of measurement samples in a closed room from an implementation of indoor localization application based on ZigBee Cluster Library (ZCL) framework. The measurement data are analyzed for their statistical parameters. The results are compared with the known properties of RSSI of wireless local area network reported in the literature. The insight of RSSI’s properties obtained in this work could be used to improve the localization application using ZigBee wireless sensor network.

Keywords— Indoor localization, location fingerprint, received

signal strength indication, wireless sensor network, ZigBee

I. INTRODUCTION

Wireless sensor network (WSN) such as ZigBee WSN can

be deployed with localization capability, which can be applied

for localization and tracking of object or people in health care

applications [1]. That is the wireless sensor network can be

used to relay location information and radio frequency (RF)

channel parameters in order to estimate a location of mobile

wireless sensor node [2]. The underlying framework for

localization mechanism is outlined by the ZigBee Alliance in

ZigBee Cluster Library Specification [2]. The crucial RF

channel parameter used for locating a mobile node is the

received signal strength indication (RSSI) reported in decibel

milliwatt (dBm), which can be derived from Link Quality

Indication (LQI) defined by the IEEE 802.15.4 standard [3].

Typically, the LQI associates with a received IEEE 802.15.4

packet and is used to provide strength and/or quality of the

packet. It can be obtained from the hardware of IEEE

802.15.4/ZigBee’s RF transceiver such as MC1322x system-

on-chip (SoC) platform from Freescale semiconductor [4].

In the literature, indoor positioning systems using wireless

sensor network and their implementations have been

presented in a number of research works since 2006. For

instance, an implementation of ZigBee indoor location system

was presented in [5] using Chipcon (now Texas Instrument)’s

CC2420 RF transceiver and a ZigBee stack on ATmega128

microcontroller. The user’s position in [5] was estimated

using both ultrasound and time-of-arrival (ToA) of RF signal.

However, the authors did not report any measurement of the

RSSI. Measurements of LQI versus transmitter-receiver

distance using Jennic’s JN5121 and Texas Instrument’s

CC2430 SoC were reported in [6] in which the authors used

the measurement data to create path loss models in two

different environments. The authors in [6] reported that they

implemented a centralized localization algorithm that

combines multidimensional scaling (MDS) and maximum

likelihood estimator (MLE) methods to estimate distance

between transmitter and receiver on a laptop.

A study on variation of received signal strength in WSN

due to various environmental factors using CC2430 was

presented in [7]. The authors investigated on the influence of

the temperature, the height of node’s position, the type of

antenna, and the electromagnetic interference of human body

on the RSSI. An interesting finding was pointed out that the

RSSI could change according to the temperature. The larger

attenuation of RF signal was observed when the surrounding

temperature was higher. The effects of the rest of the factors

on RSSI were similar to common understandings of RF

propagation. Another experimental study on the variation of

RSSI due to the effects of obstacles in indoor environment

was reported in [8]. The measurement experiments were

performed using RF230 and CC2420 RF transceivers. The

authors in [8] investigated the attenuation of RSSI over

different distances in both line-of-sight (LoS) and non line-of-

sight (NLoS) environments. In the NLoS case, there were

three types of obstacles between transmitter and receiver,

which were glass (glazed door), glass and wood (compressed

wood), and partial wall (brick and gypsum board). The

authors concluded that the RSSI alone could not be used to

accurately locate mobile nodes in an indoor environment with

obstacles [8].

Based on our literature survey, none of the existing

research works has implemented the ZigBee’s RSSI location

cluster in their systems. Moreover, none of them has

considered the statistical properties of the RSSI such as its

probability distribution and skewness. Therefore, in this work

we implemented a ZigBee localization system using the

framework of ZigBee Cluster Library (ZCL) and performed a

data collection of RSSI over a small and closed laboratory

room. The main objective of this work is to compare the

978-1-4799-0545-4/13/$31.00 ©2013 IEEE

statistical properties of RSSI in ZigBee WSN with the known

properties of RSSI in wireless local area network (WLAN) or

Wi-Fi network reported in the literature.

The organization of this paper is as follows. Section II

describes the architecture of the indoor localization system,

which consists of both hardware and software components.

Section III explains the experimental design. Section IV

reports on the measurement results. Section V discusses our

findings on the properties of the RSSI from the ZigBee

localization system. Finally, Section VI concludes our work

and outlines the future work.

II. SYSTEM ARCHITECTURE

An indoor localization system used in this study is

implemented on a set of ZigBee wireless sensor nodes

developed in our research laboratory. The network comprises

of five ZigBee nodes in which there are four fixed nodes and

one mobile node. One of the fixed nodes is connected to a

laptop computer via a RS232-to-USB serial interface and the

node is the ZigBee coordinator (ZC). The rest of the nodes in

the network are implemented as ZigBee routers (ZR). The

implemented localization system can be categorized as a

centralized architecture as described in ZigBee Telecom

Application Profile Specification [9]. The two-dimensional

locations in this system are identified using Cartesian

coordinate system or pair of numerical coordinates.

Generally, ZigBee localization system can be implemented

using four different techniques as outlined by ZigBee Cluster

Library Specification [2], which are the lateration, the

proximity or the signposting, the RF fingerprinting, and out-

of-band localization device. First three of these techniques

rely on the measurement of RSSI, while the last technique

depends on non-ZigBee based device connected to each node

to provide location information such as infrared or ultrasound

localization device. In this work, the implemented system

utilizes the RF fingerprinting technique that can be deployed

in two phases: the offline phase and the online phase [10].

During the offline phase, the system must collect the RSSI

patterns at each location and create a database of RF

fingerprints, which will be used during the online phase to

estimate a location based on the closest matching of RF

fingerprint with the current RSSI pattern at a location.

The rest of this section described three important parts of

our ZigBee localization system. First, a brief description of

ZigBee Location Cluster, which is implemented in a

commercial ZigBee protocol stack, is explained. Second, the

hardware and its characteristics are summarized. Third, a

personal computer (PC) based localization application

developed for controlling and visualizing the localization

system is presented.

A. ZigBee Location Cluster

ZigBee specification defines a cluster as standardized

communication interface between ZigBee client and server

devices. Inside each cluster, there is a collection of attributes

and commands used by specific ZigBee application. For

instance, ZigBee’s RSSI Location Cluster is defined in

ZigBee Cluster Library (ZCL) and given a unique Cluster ID

of 0x000b. The location cluster enables ZigBee devices to

exchange relevant location information and channel

parameters for localization application and optionally report

collected data such as RSSI from networked devices to a

centralized device which estimates locations of mobile nodes

[2]. In this work, we utilize all these mechanisms inside the

ZigBee protocol stack.

Examples of related attributes, which are arranged into

different sets, in the RSSI Location Cluster are location type,

location method, location age, quality measure, number of

devices, coordinates, power, path loss exponent, reporting

period, calculation period, and number of RSSI measurements

[2]. Some of these attributes are mandatory. Examples of

related commands in this cluster are Set Absolute Location,

Set Device Configuration, Get Device Configuration, Get

Location Data, RSSI response, Send Pings, and Anchor Node

Announce [2]. The cluster also defines payload format of each

command and action upon receiving a command such as

setting relevant attributes. Some of these attributes and

commands are implemented in this work.

Fig. 1 illustrates the ZigBee localization system deployed in

this study. The nomenclature of each ZigBee node is defined

in ZigBee Telecom Application Profile Specification [9]. The

fixed node with a known location is called the anchor node,

while the mobile node is called the location node. The

centralized node that collects the RSSI data from all other

nodes and relays the data to a computer is called the location

gateway.

Fig. 1 ZigBee Localization System Architecture.

The usage of ZigBee’s RSSI Location Cluster is

summarized in Fig. 2 and can be explained as follows [2].

Initially, the location gateway, which is the centralized device,

receives commands called “Anchor Node Announce” from all

anchor nodes after they joined the network. If the localization

system wants to locate the mobile location node, the location

gateway will issue a “Send Pings” command to the location

node. Upon receiving the “Send Pings” command, the location

node will periodically send multiple “RSSI Ping” commands

to all its single-hop anchor nodes in our system. Each anchor

node that received “RSSI Ping” command will measure the

RSSI of each “RSSI Ping” command and calculate an average

value of multiple RSSIs for the sending location node. Next,

the location node will issue the “RSSI Request” command to

all single-hop anchor nodes to ask for its averaged RSSI

values. Each anchor node will reply to the requesting location

node with “RSSI Response” command that contains

coordinate data of the anchor node, the averaged RSSI value,

and the number of RSSI measurements. Finally, the location

node will send “Report RSSI Measurements” command to the

location gateway to provide its measurement for localization.

Fig. 2 RSSI Location Cluster Usage.

B. ZigBee Wireless Sensor Nodes

Fig. 3 shows the set of actual ZigBee wireless sensor nodes

used in our experiment. There are two types of printed circuit

boards (PCBs): the large board and the smaller board which is

the one in the middle of the figure. The different between the

two types of the boards are the number of peripheral

input/output (I/O) interfaces in which the smaller board has no

I/O connectors and is suitable for equipping as a mobile

device and operating on small battery. The larger board has

two RS232 serial interfaces, which can be used to connect to a

laptop computer.

Fig. 3 Actual ZigBee Wireless Sensor Nodes.

The main component of each node is the ZFSM-201-1

module from California Eastern Laboratories (CEL) [11]. This

is an integrated transceiver module for ZigBee/IEEE 802.15.4

that is based on Freescale’s MC13224V SoC platform [4] and

an additional RF power amplifier front end. The MC13224V

itself is actually contained both IEEE 802.15.4 RF transceiver

and a low power 32-bit ARM7TDMI-S microcontroller. The

MC13224V possesses the ability to report LQI for a received

packet. The LQI is an 8-bit hexadecimal value from 0x00 to

0xFF defined in IEEE 802.15.4 standard [3]. The value 0xFF

is approximately equal to -15dBm, while the value 0x00 is

approximately equal to -100dBm [4]. The reference manual of

the MC13224V defines the conversion expression between the

RSSI or the power in dBm and the LQI in decimal as [4]:

1003

)()( −=

decimalLQIdBmRSSI (1)

The embedded software of each ZigBee node is developed

based on proprietary software templates and a proprietary

ZigBee Stack called Freescale BeeStack [12], which is written

in C programming language. Note that the ZigBee

coordinator’s and ZigBee router’s codes are different and have

to be modified independently. The provided software

templates did not implement the RSSI Location Cluster and

we implemented additional codes to enable the localization

ability in the system. The communication interface between

the location gateway and the laptop computer in Fig. 1 is

based on the Freescale’s BeeStack BlackBox ZigBee Test

Client (ZTC) application programming interface (API) [13].

The API defines the frame format of message exchanging

between the location gateway and the laptop computer.

The ZigBee network parameters are set as followings. The

ZigBee stack profile is selected as Stack Profile 0x02, which

is the ZigBee PRO 2007 specification. The network topology

is formed as a mesh network without enabling security

mechanism. The maximum transmit power of each node is set

to 20dBm. The RF channel is chosen as channel number 14 as

defined by IEEE 802.15.4 specification in 2.4GHz ISM band.

The personal area network identification (PANID) is set to

0x1aff.

C. ZigBee Location Application

A PC software application is developed in this work using

JAVA programming language to provide graphical user

interface (GUI) for both ZigBee localization system

management and visualization. After connecting the gateway

node through the USB port of the laptop, the user can start the

connection to begin a communication with the gateway node

via a play command icon. The application can automatically

detect all nodes that have joined the ZigBee network. Then, it

allows the user to perform each detected node’s placement on

a map and to collect RF fingerprint of the location node on

different locations during the offline phase. The application

utilizes proprietary Freescale’s ZTC API to communicate with

the location gateway node in which it allows the user to send

commands to and gather the RSSI data from the ZigBee

network. Moreover, the application maintains a database of

RF fingerprints, which are collected through the location

gateway node. It also performs location estimation algorithm

and shows the estimated location of mobile node on the map

during the online phase. Initially, we implemented a simple

algorithm using the single nearest neighbour pattern

classification based on the Euclidean distance between the RF

fingerprints in the database and the new RSSI pattern

collected during the online phase.

Fig. 4 is a screen capture of the developed PC application.

There is a map on the right hand side of the screen that shows

all location fingerprints (denoted by fingerprint icons) and the

positions of the gateway (denoted by small computer icon)

and anchor nodes (denoted by base station icons). On the left

hand side of the screen, there is a device list that itemizes all

nodes with their IEEE 802.15.4 medium access control (MAC)

addresses under their corresponding type of node. Note that

the list of all RF fingerprints and their corresponding three

dimensional coordinates (denoted by X, Y, Z) are also

displayed. Note that all Z-axis values are zeros because we

only use two-dimensional localization system in this work.

Fig. 4 Screen Capture of ZigBee Localization Application.

III. EXPERIMENTAL SETUP

This section describes the preparation of the ZigBee

wireless sensor network inside a small laboratory room for our

measurement experiment. Fig. 5 illustrates a localization area

which is defined as a square grid of locations with grid

spacing of 1.4m×1.4m inside a small laboratory room. The

positions of all ZigBee nodes are also labelled as L1 to L24 in

the figure. There were 24 locations inside this room which

were used to collect RF fingerprints. The anchor nodes are

labelled with A1 to A4. There is no large obstacle that block in

between any transmitters and receivers. This can be

considered as an environment with clear line-of-sight (LoS).

To form the ZigBee wireless sensor network, the location

gateway was powered on first at the lower right corner of the

Fig. 4 and connected to the laptop that ran our localization

application as shown in Fig. 6. Then, we placed the rest of the

anchor nodes at the other three corners as shown in the figure.

After all nodes were detected, we sent a command via PC

application to set their Cartesian coordinates.

Fig. 5 Grid of locations with the positions of all nodes.

Fig. 6 A photograph of the gateway node in the test room.

The user collected the RF fingerprint during the offline

phase at each location through the application’s command

after placing the actual location node at that location. The

process was repeated until RF fingerprints were collected for

all locations. In this work, each sample of RF fingerprint

consists of four different RSSI samples from the location

gateway and the three anchor nodes. Note that the location

gateway node also acted as an anchor node. We collected 30

RSSI samples from each anchor node at each location and

averaged them out to obtain one of the components in the RF

fingerprint. The sampling rate was one sample every nine

seconds. Thus the measurement period per location was 9×30

= 270 seconds or 4.5 minutes. Note that only the integer value

of the averaged data is kept in the database. There were a total

of 30×24 = 720 samples. This set of RSSI samples will be

used in our statistical data analysis in Section V.

To measure the localization performance of the system, the

software can be switched to operate in the online phase in

which the user can locate or track the current location of the

mobile location node. Note that we only test the system by

placing the mobile location node on the same grid locations as

those locations that were collected the RSSI. The localization

application will use the location estimation algorithm to

provide an estimated location. We compared the estimated

location with the actual location and calculated the error

distance for each user’s request as illustrated in Fig. 5. The

online experiments were performed for 50 times at each

location. Consequently, there are a total of 50×24 = 1,200 test

results. The measurement results will be reported in the next

section.

IV. PERFORMANCE MEASUREMENT RESULTS

The localization performance measurement results are

reported in Table I. The first column is the error distance

which is the coordinate’s difference between the correct

location and the estimated location returned by our application.

The second column, which is labelled as probability density,

is counted and normalized from the frequency of test results

with corresponding error distance. The last column is the

cumulative value of the probability density given in the

second column. Note that these results were reported

previously as a plot of cumulative distribution function in our

two-page short paper [14]. The numerical data are included

here for completeness of our study. In the literature, the

localization performance is usually reported in terms of

location accuracy and location precision. The location

accuracy is defined as an error distance (in meters) that the

estimated location is deviated from the actual location [15].

On the other hand, the location precision is defined as a

percentage or a cumulative probability density value that the

system returns all correct locations within a given distance of

accuracy [15]. Based on the results in the table, our system

achieved 2.8 meters of accuracy with approximately 92

percent of precision. The mean accuracy of the system can be

calculated by summing all products of error distances and

their corresponding probability densities, which is

approximately 0.77m.

TABLE I

LOCALIZATION PERFORMANCE RESULTS

Error Distance (m.)

Probability Density

Cumulative Probability Density (CDF)

0 0.7025 0.7025

1.4 0.1050 0.8075

1.98 0.0592 0.8667

2.8 0.0517 0.9184

3.13 0.0108 0.9292

4.43 0.0633 0.9925

5.77 0.0075 1

V. STATISTICAL DATA ANALYSIS

In the literature, there is a comprehensive research work in

[16] that analyses statistical parameters of RSSI measured

from a number of Wi-Fi interfaces. The authors of that work

pointed out that there is a need to understand characteristics of

RSSI beyond typical RF propagation in order to better design

and implement an indoor localization system. An interesting

finding in that work is that most of the RSSI data collected

from various indoor environments, if it is considered as a

random variable, possesses a unique probability distribution

function. Most of RSSI’s distributions were not Gaussian but

rather had left-skew distributions [16]. Minority of the

distributions was found to have symmetry shape which is

similar to Gaussian distribution. The skewness parameters

were calculated for various RSSI data sets from a number of

WLAN access points and most of them were found to be

negative. The standard deviation of the distribution was

observed to be larger when the RSSI samples were collected

at a location that was closer to the WLAN access points.

In this work, we performed similar statistical data analysis

in term of the shape of RSSI distribution, the standard

deviation, and the skewness. Fig. 7 compares the normalized

histograms of RSSI from all locations where each sub-graph

represents data from different anchor nodes. We observe that

most of the histograms are symmetry which could be

modelled by Gaussian distribution. This is different from the

results reported in LoS environment of Wi-Fi in [16].

Fig. 7 Normalized Histograms of RSSI from All Locations

Fig. 8 plots the mean RSSI values of RSSI collected at each

anchor node (from anchor 1 to 4) against each location (from

location 1 to 24). The combination of mean RSSI values from

all four anchor nodes forms a RF fingerprint at a location. A

visual inspection of the pattern of mean RSSI values at each

location is quite different from other locations. This enables

the localization algorithm to separate one location from

another through the unique pattern of RSSIs.

Fig. 8 Mean of RSSI from All Locations

Fig. 9 considers standard deviations of the RSSI samples

collected by all anchor nodes at each location. We found that

most of the standard deviations were below 2 and were not

larger than 4. This is different from the results reported by

small area scenario with LoS propagation of [16] where Wi-Fi

tends to have larger standard deviation than 4. We observed

that when mobile location node is at the same location as

anchor node, the standard deviation of RSSI is zero. For

example, when mobile node is at location 1, the standard

deviation of RSSI measured at Anchor 1 is 0. In other words,

Wi-Fi signal is more fluctuate than ZigBee signal under the

LoS environment.

Fig. 9 Standard Deviation of RSSI from All Locations.

Fig. 10 compares the skewness values of all RSSI

distributions from different locations and anchor nodes.

Skewness is a measure of symmetry of distribution. A

probability density function (PDF) is said to be left-skewed

(has a long tail on the left) when it has its mean less than its

median which is less than its mode [17]. Note that the left-

skewed distribution will have a large negative skewness value.

However, in this figure we observed that most of the

distributions were not left-skewed. This is also different from

the Wi-Fi results reported in [16].

Fig. 10 Skewness of RSSI from All Locations.

VI. CONCLUSIONS AND FUTURE WORK

An implementation of ZigBee localization system, which

consists of both hardware and software components, is

described in this work. Preliminary localization performance

in term of location accuracy and location precision is also

summarized where the average accuracy was 0.77m. At

approximately 92% of precision, the system can achieve the

accuracy of 2.8m. The main contribution of this paper is the

study of statistical data analysis on the RSSI in RF fingerprint

database. We found that for small area with LoS RF

propagation most of the distributions of RSSI in ZigBee WSN

are symmetry which is different from Wi-Fi results in [16].

Moreover, the standard deviation was also different in

which ZigBee RSSI signal is less fluctuate in this work. The

majority left-skew distributions were not observed in this

work, while it is prevalent in the Wi-Fi system. However, in

term of statistics the total number of measured samples in this

work is still small. Our preliminary findings reported in this

work may not be valid in all environments and scenarios

because we only collected the data from a small room

environment. We are planning to deploy the ZigBee

localization system in a larger area and perform more

extensive measurement experiments, which will include NLoS

environment and low signal strength condition.

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