Crowdedness Estimation Using RSSI on
Already-deployed Wireless Sensor Networks
Naoya Matsumoto† , Jiei Kawasaki† , Makoto Suzuki‡ , Shunsuke Saruwatari† , Takashi Watanabe†† Graduate School of Information Science and Technology, Osaka University, Japan‡ Sonas Corporation, Japan

Abstract—This paper proposes a system that estimates crowdedness, such as the numbers of people and wireless communication devices, from the received signal strength indication (RSSI),
which is measured using sensor nodes already-deployed for adifferent purpose. For example, the system estimates crowdedness
using sensor nodes of structural health monitoring. Our system
consists of two parts: an RSSI synchronous measurement anda crowdedness estimation algorithm. The RSSI synchronous
measurement enables us to measure and collect two kinds of RSSI
by synchronized RSSI sampling: the inter-node RSSI and surrounding RSSI. The crowdedness estimation algorithm estimates
the number of people from the collected inter-node RSSI and thenumber of wireless communication devices from the collectedsurrounding RSSI in a location. We evaluated the proposedsystem in the laboratory at Osaka University. The evaluation
results demonstrate that the system estimates the presence orabsence of people in the laboratory with approximately 92%
accuracy and the number of people with approximately 79%
accuracy, with errors of up to two people.
Index Terms—Crowdedness Estimation, RSSI, SynchronizedSampling, Sensor Networks, Wireless Sensing

I. I NTRODUCTION
With the advent of smartphones and the Internet of Things(IoT) devices, traffic using radio waves continues to increasesignificantly. The authors are working on a wireless sensing
approach that senses a location and user information fromradio waves that are transmitted by wireless communication
devices. This wireless sensing approach enables us to integrate
a communication system and sensing system.
This paper considers a crowdedness estimation using radio
waves. We define the crowdedness estimation as estimating
the number of people and wireless communication devices
in a location. The estimated crowdedness facilitates situationdependent services, such as energy consumption reduction ina smart building, navigation in a large shopping mall, observation of elderly people, and intrusion detection in facilities.
The estimated crowdedness can also be utilized to improve
wireless communication performance according to the numberof devices in a wireless communication protocol.
Considering usage in daily life, the crowdedness estimation system needs to satisfy two requirements: a devicefree approach and low deployment cost. Various crowdedness
estimation methods using radio waves or cameras have beenstudied [1]–[12]. However, there is currently no approachsatisfying the two abovementioned requirements. The detailsof these requirements and related work will be discussed in
Section II.

In view of this, this paper proposes a crowdedness estimation using a synchronized RSSI measured by IEEE 802.15.4.
First, we propose a mechanism to measure two kinds of
RSSI: the inter-node RSSI and surrounding RSSI. Both ofthese RSSIs are strictly synchronized using “Choco” which
is a wireless sensor network platform utilizing simultaneoustransmission [13]–[15]. The proposed method enables us toestimate the crowdedness just by adding RSSI acquirement
features to IEEE 802.15.4 wireless sensor networks alreadydeployed for a different purpose. Next, we propose crowdedness estimation algorithms that estimate the number of people
from the inter-node RSSI and the number of devices from thesurrounding RSSI. An experiment conducted in the laboratory
confirms that the algorithm estimates the number of people
with approximately 79% accuracy, with errors up to two
people.
The remainder of this paper is organized as follows. Section II describes the requirements for crowdedness estimationand existing methods. Section III presents an overview ofthe proposed system. Section IV presents the mechanism forcollecting data. We describe how to process the collected data
in Section V. We evaluate the data collection rate and the
proposed system in Section VI. Finally, our conclusions are
given in Section VII.
II. C ROWDEDNESS ESTIMATION
A. Requirements for crowdedness estimation
In this paper, crowdedness estimation is defined as a technique to estimate how many users, or how many wirelesscommunication devices exist in a location. The crowdedness
estimation will enable us to utilize various applications indaily life. For example, according to the crowdedness, a smart
building can reduce energy consumption by adjusting the air
conditioning [16], a large shopping mall can reduce waitingtimes and optimize the arrangement of shops, and a communication protocol can improve the communication performanceby adjusting the initial backoff time. Crowdedness estimation
can also be applied to observation services for elderly people
living alone and for intrusion detection in a house.
For such applications, a crowdedness estimation system
must satisfy the following two requirements:
1) device-free estimation,2) a low deployment cost.
Device-free estimation is desirable to reduce the user’s burden.
If a user requires a specific device, then problems suchas battery exhaustion and device failure may arise. A low
development cost includes the financial and mental costs when
deploying a crowdedness estimation system. It is desirable todeploy the system as cheaply and easily as possible to a target
location. A low invasion of privacy is also desirable for users.

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B. Existing crowdedness estimations
Two kinds of crowdedness estimation exist: estimation using
the position information of all users and estimation from direct
spatial information. Position estimation methods include GPS,
Wi-Fi, and Bluetooth [1]–[5]. For example, [3] estimates user
positions using the flight times of transmitted radio waves.
However, estimations using GPS, Wi-Fi, or Bluetooth do not
represent device-free approaches.
A crowdedness estimation from direct spatial information
employs cameras or infrared ray sensors, and these are categorized as device-free approaches [6]–[8]. For example, [7]estimates the crowdedness by counting the number of people
based on the visual features from images captured by cameras.
However, such camera-based approaches have some drawbacks, including the limitations of the deployment location,
privacy, price, and computational cost of image processing.
For example, a limitation of the deployment location resultsfrom the strong directivity of light. The directivity of lightaffects the acquisition range of a camera, as well as shadowing
caused by obstacles.
Estimation using radio waves has also been studied as
device-free approach [9]–[12]. For example, [11] estimatesthe crowdedness from radio waves acquired between two
terminals deployed in a location. However, in that approach,
it is necessary to conduct prior calibrations in the location,
where all people walk at the same speed.
III. OVERVIEW OF THE PROPOSED SYSTEM
To resolve the problems mentioned in Section II, we integrate a crowdedness estimation mechanism with alreadydeployed wireless sensor networks. The proposed mechanism
senses the state of a location using only the RSSI acquiredby already-deployed IEEE 802.15.4 sensor nodes [17]. Our
approach is device-free and easy to deploy, because alreadydeployed IEEE 802.15.4 sensor nodes are available, such as forstructural health monitoring, environmental monitoring, andsmart meters [18]–[20].
Fig. 1 presents an overview of the proposed crowdedness
estimation system. Sensor nodes estimate the crowdedness by
evaluating the variance of the synchronized inter-node RSSI.
The synchronized sampling maintains the time consistencyof the RSSI acquired by different sensor nodes. The time
consistency of the RSSI enables us to capture the radio wave
environments of a location at a certain moment. The detailsof the synchronized sampling are presented in Section IV.
All sensor nodes send the RSSI to a sink node, and the sinknode processes the RSSI to estimate the crowdedness. Users do
not require a specific device for the crowdedness estimation.
Our RSSI-based system has lower privacy issues comparedto camera-based systems, because the sizes of terminals are&URZGQHVVHVWLPDWLRQ

Fig. 1. Overview of the proposed systemsmall, and our system does not capture figures. The details of
the crowdedness estimation using the RSSI are presented in
Section V.
IV. S YNCHRONIZED SAMPLING SYSTEM
The synchronized sampling system enables each sensor
node to acquire two kinds of RSSI: the inter-node RSSI andsurrounding RSSI. The inter-node RSSI is the strength of radio
waves when a sensor node receives signals that another sensor
node transmits. If a person is present between sensor nodes,
then the inter-node RSSI decreases because of shadowing.
This decrease enables us to capture the change in a location.
Each node measures the strengths of non-modulated sine
waves transmitted from another sensor node as the inter-node
RSSI. A person’s average walking speed is approximately 1.25
m/s, and a person’s average width is approximately 30 cm.
Sampling within 240 ms enables the system to capture theinfluence on the RSSI of the passage of people.
The surrounding RSSI is the strengths of radio waves whena sensor node receives signals that other sensor nodes do
not transmit. When no sensor nodes are communicating, eachsensor node measures the surrounding RSSI. The surrounding
RSSI enables us to capture the communication states ofwireless communication devices such as wireless LAN and
Bluetooth.
A. Concurrent transmission flooding
The proposed system enables sensor nodes to measure thesynchronized RSSI using “Choco” [13], [14]. The synchronized RSSI enables us to capture the radio wave environmentof a location at a certain moment. Choco provides accurate
time synchronization and reliable low-power data collection
using concurrent transmission flooding [15].
Fig. 2 presents an example of concurrent transmission
flooding. Concurrent transmission flooding transmits a packet
to a destination node through simultaneous broadcasting by
all nodes, rather than through single path transmission. The
quadrangle indicates the sink node, the triangle represents a

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Fig. 3. Example of synchronized sampling operation
Fig. 2. Concurrent transmission flooding

source node, and the circles denote relay nodes. The sink nodetransmits control packets to the other nodes. The control packets enable all nodes to schedule packet transmission timing.
Upon receiving a packet, relay nodes immediately rebroadcast
the packet. This simultaneous broadcasting enables a packet
to spread in the entire network. Even when some nodes do
not receive a packet, this chained re-broadcasting enables thepacket to reach the destination node.
Choco achieves low power consumption by schedulingpacket transmission according to time slots. A sink node
utilizes a control packet to dictate the following four operations
to sensor nodes:
1) “which slot a node transmits packets in,”2) “which sequence number a node transmits packets in,”3) “which node a node transmits packets to,”4) “which data a node transmits.”
Each sensor node transmits packets according to the schedule. Each packet from each sensor node has a backlog field
in a header. A backlog represents the number of packets totransmit. The sink node calculates the number of packets tocollect from a backlog field, and allocates slots to sensor
nodes. If backlogs on all nodes are zero, then the sink nodebroadcasts a sleep packet. When receiving a sleep packet, asensor node transits to sleep mode. Determining “which slot
a node transmits packets in” and “which sequence number anode transmits packets in” enables sensor nodes to reduce thestandby time.
B. Synchronized sampling
Concurrent transmission flooding is also utilized for synchronized sampling. A sink node transmits a synchronization
packet to sensor nodes every second using concurrent transmission flooding. The synchronization packets enable sensor
nodes to synchronize with 1 µs accuracy.
The proposed system uses synchronized sampling to acquire
the RSSI. This synchronized sampling involves synchronizing
all sensor nodes with the sink node and aligning the sampling
timing among all sensor nodes. The synchronized sampling
maintains the time consistency of the RSSI acquired by eachsensor node.

Fig. 3 illustrates the operation synchronized sampling.
Sensor nodes alternately measure the inter-node RSSI andsurrounding RSSI through synchronized sampling every 200
ms. One slot consists of 50 ms, and each cycle is 200 ms.
Each slot consists of the two periods: the Choco period anddata measurement period. The Choco period is 15 ms, and thedata measurement period is 35 ms. The sink node transmits acontrol packet and collects data in the Choco period. Sensor
nodes measure the inter-node RSSI and surrounding RSSI in
the data measurement period.
The sink node transmits the control packet to all sensor
nodes at the beginning of each slot. The control packet
includes the schedule of sensor nodes. The sink node strictly
manages the operation of sensor nodes using the time synchronization and reliable transmission. This management enables
sensor nodes to improve power efficiency.
Sensor nodes measure the inter-node RSSI and surrounding
RSSI in the data measurement period of the first slot on eachcycle. First, no sensor nodes transmit any packets, and theymeasure the surrounding RSSI. Each sensor node stores thesurrounding RSSI. Next, sensor nodes take turns broadcasting
unmodulated sine waves and measuring the inter-node RSSI.
Each sensor node stores the source node’s ID and the internode RSSI. The source node’s ID is determined from thetransmission time, according to the schedule in the control
packet. Sensor nodes transmit the RSSI to the sink node using
concurrent transmission flooding.
Sensor nodes transmit the RSSI to the sink node in theremaining three slots of the Choco period. A sensor node thatreceives the control packet transmits the inter-node RSSI andsurrounding RSSI to the sink node, according to the schedulein the control packet. Sensor nodes do not measure the RSSI
and transit to sleep mode in the remaining three slots of thedata measurement period.
V. C ROWDEDNESS ESTIMATION USING THE RSSIWe propose the two kinds of crowdedness estimation: estimations of the number of people and the number of devices.
The inter-node RSSI enables us to estimate the number ofpeople, and the surrounding RSSI enables us to estimate

the number of devices. These RSSIs are measured by thesynchronized sampling method detailed in Section IV-B.
A. Estimation of the number of people
Algorithm 1 Estimation of the number of people
1: for x in S do
2:
for y in S do22
+ V (x, y, T )3:σsum⇐ σsum4:
end for
5: end for 2σsum
6: σ 2 ⇐ |S|2
7: if σ 2 ≤ γ then8:
u= 0
9: else10:u ⇐ ⌊α × σ 2 + β + 0.5⌋
11: end if

TABLE IVARIABLES USED IN Algorithm 1
variable nameS
V (x, y, T )σ2role in the algorithm
the set of sensor nodes
the variance of inter-node RSSI between x and y
the average of the variance of inter-node RSSI

We estimate the number of people using the variance of
the inter-node RSSI. The variance of the inter-node RSSI
enables us to reduce the environmental influence. The internode RSSI changes depending on the temperature, humidity,
and movement of people. The fluctuation of the inter-node
RSSI owing to user passage is large and instantaneous, andfluctuations owing to temperature and humidity are small andgentle. In addition, the synchronized sampling approach from
Section IV-B enables us to distinguish whether a user moves
or multiple users are present.
Algorithm 1 describes the estimation of the number ofpeople. Table I lists the variable names and roles in Algorithm
2
1. Here, S is the set of sensor nodes, σsum
is the variable2
for calculation, σ is the average of the variance of the internode RSSI between all nodes, u is the estimated crowdedness,and V (x, y, T ) is a function for calculating the variance of
the inter-node RSSI between the sensor nodes x and y for T
seconds.
Lines 1 to 6 in Algorithm 1 calculate the variance of theinter-node RSSI. The variance of the RSSI on each path iscalculated individually, and all of the variances are averaged
over. Lines 7 to 9 in Algorithm 1 estimate whether or nota person is present. If the variance of the inter-node RSSI isless than γ, then it is estimated that there is no person, and 0
is substituted into u. If the variance of the inter-node RSSI isgreater than or equal to γ, then Algorithm 1 substitutes therounded value of α× σ 2 +β into u. In addition, γ is a constantcalculated from experimental data and α and β are constants
calculated by the least-squares method, using the variance of
the inter-node RSSI and the actual number of people.
B. Estimation of the number of devices
We estimate the number of devices using a clustering
algorithm with the surrounding RSSI. If wireless devices

TABLE II
VARIABLES USED IN Algorithm2
variable nameS
D(x, t)Dsum(S, t)min(D(S, t))xmeans(N (S, T))role in algorithm
the set of sensor nodes
node x’s surrounding RSSI
the average of surrounding RSSI
the minimum surrounding RSSI
the number of total clusters

communicate, then the surrounding RSSI close to the devices
increases probabilistically. The minimum time required fortransmitting and receiving one packet in Wi-Fi is approximately 20 µs. The sampling interval of each sensor node is
200 ms.
The total number of surrounding RSSI patterns may represent the number of devices. For example, if there is one active
device in a location, then the surrounding RSSI is divided
into two patterns: the case that all sensor nodes measure apacket from a device and that no sensor nodes measure any
packets from a device. If the number of devices in a location
increases, then each sensor node measures many patterns of
the surrounding RSSI.
To calculate the total number of surrounding RSSI patterns,
the estimation employs the x-means method [21]. The x-means
method is a clustering algorithm that automatically determines
the total number of clusters using the Bayesian information
criterion (BIC). The x-means method performs clustering by
repeating the following two processes:
1) divide each cluster into two and calculate the BIC foreach cluster,2) perform 1) if the BIC after this division is smaller thanthat before the division, and stop 1) if not.
Algorithm 2 Estimation of the number of devices
1: for x in S do
2:
Dsum(t) ⇐ Dsum(t) + D(x, t)
3: end for
4: Dsum(t) ⇐ Dsum(t)|S|
5: if Dsum(S, t) > γ then6:ϵ ⇐ min(D(S, t))
7:
for x in S do
8:
N (x, t) ⇐ D(x, t) − ϵ
9:
end for
10: end if
11: θ ⇐ λ · xmeans(N (S, T))

Algorithm 2 presents the estimation of the number ofdevices every T seconds. Table II lists the variable names
and roles in Algorithm 2. Here, S is the set of sensor nodes,
T is the set of elapsed times, t is an element of T, D(S, t)
is the surrounding RSSI acquired by the sensor nodes at t, ϵand Dsum(t) are variables for calculation, Dsum(S, t) is theaverage of the surrounding RSSI at t, N (S, t) is the normalized
surrounding RSSI at t, θ is the estimated number of devices,min(D(S, t)) is a function that calculates the minimum valueof the surrounding RSSI at t, and xmeans(N (S, T)) is afunction that calculates the total number of clusters.
Line 5 in Algorithm 2 removes noise from the data. If
the average of the surrounding RSSI is less than or equal to

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Fig. 4. Development positions of the sink node and sensor nodes
Fig. 5. Success rate of data collection

γ, then Algorithm 2 removes this data, because data below
γ is regarded as noise. If there are no data transmitting
devices, then the surrounding RSSI becomes small. Here, γ
is a constant calculated from experimental data. Line 8 in
Algorithm 2 normalizes the surrounding RSSI. The normalization of the surrounding RSSI enables the system to makethe base value at every time equal to zero and classify the data
for each pattern. The normalization of the surrounding RSSI
consists of subtracting the minimum surrounding RSSI fromthe surrounding RSSI of each sensor node.
Line 11 in Algorithm 2 estimates the number of devices.
The total number of clusters represents the total number ofsurrounding RSSI patterns. Here, λ is a constant calculated bycomparing the total number of clusters by an experiment in an
anechoic chamber with the actual number of active devices.
VI. E VALUATION

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Fig. 6. Time series of the variance of the inter-node RSSI and number ofpeopleA. Experimental settings
We conducted an experimental evaluation in the laboratory
at Osaka University from 10:00 on November 28th, 2017
to 10:00 on December 1st, 2017. We installed a sink nodeand 10 sensor nodes in the laboratory. A Sonas CC-RM01
was used as the sink node [22], and Choco evaluation boards
based on Texas Instruments CC2650MODA were utilized as
sensor nodes. A Buffalo WZR-1750DHP2 was employed as
an access point. The radio channel was 13 (2.472 GHz). Fig. 4
illustrates the development locations of nodes. The sink node
was placed at 1, and the sensor nodes were placed at positions
2 to 11. A laboratory meeting was held from 15:00 to 16:30
on November 28th. Because the laboratory meeting was held
in a separate room, the number of people in the laboratory
rapidly decreased.
B. Success rate of data collection
Fig. 5 illustrates the data collection rate in the experiment.
The average success rate of the data collection was 99.6%. The
horizontal axis represents the time from 10:00 on November
28th, 2017 to 10:00 on December 1st, 2017, and the verticalaxis represents the success rate of the data collection. The
success rate continually exceeded 95%. The cause of packet
drops was interference from other wireless networks. For
example, the surrounding RSSI drastically increased owing to

the download of meeting documents between 15:00 and 16:30
on November 28th.
C. Evaluation of estimating the number of people
Fig. 6 illustrates the variance of the inter-node RSSI andthe actual number of people. The horizontal axis represents
the time from 10:00 on November 28th, 2017 to 10:00 on
December 1st, 2017. The vertical axis on the right side
represents the variance of the inter-node RSSI, and the verticalaxis on the left side shows the number of people in thelaboratory. Fig. 6 confirms two observations. The first is thatthe variance is large when people are present, and small when
they are not. We can observe that the variance is increasing
when the number of people is increasing at around 10:00. The
correlation coefficient between the number of people and thevariance of the inter-node RSSI is approximately 0.71. The
second observation is that the variance is too large comparedto the actual number of people between 15:00 and 16:30 on
November 28th. Radio waves from other wireless networks,
such as Wi-Fi, might influence the inter-node RSSI.
Next, we evaluated the crowdedness estimation by comparing the variance of the inter-node RSSI with the actual
number of people. Here, γ, α, and β in Algorithm 1 were
set to −90, 0.83, and 0.65, respectively, and T was 600. Fig.

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Fig. 8. Time series of the total number of clusters

Fig. 7. Time series of the estimated and actual numbers of people

7 illustrates the estimated and actual numbers of people. The
horizontal axis represents the time from 10:00 on November
28th, 2017 to 10:00 on December 1st, 2017. The vertical axison the right side shows the estimated number of people, andthe vertical axis on the left side represents the actual numberof people. Fig. 7 confirms two observations. The first is thatthe algorithm estimated the presence or absence of a personwith 92% accuracy. When people are present, an estimation
error tends to occur. In the case with people, the algorithm
correctly estimates their presence with 87% accuracy. When
there are no people, the algorithm estimates their absence with
97% accuracy. The second observation is that the algorithm
estimates the number of people with approximately 56%
accuracy when δ is zero and with approximately 79% accuracy
when δ is two. When the variance is large, an estimation error
tends to occur.
D. Evaluation of estimating the number of devices
To evaluate the effectiveness of the algorithm presented in
Section V-B, we conducted an experimental evaluation. We
placed three PCs in an anechoic chamber. The three PCs
were Panasonic CF-B11QWABR models with Ubuntu 16.04
LTS installed. One PC was wire-connected, and made into
an access point. The radio channel was 1 (2.412 GHz). We
used iperf, which is a network measurement tool, to transmit
radio waves from one PC to another at approximately 10
Mbps. Because radio waves from the outside do not reach theanechoic chamber, the surrounding RSSI is only influenced byradio waves transmitted from the three PCs.
Fig. 8 presents the time series of the total number ofclusters. The horizontal axis represents the elapsed time inthe experiment, and the vertical axis shows the total numberof clusters estimated by the x-means method. The estimation
was performed every 10 min. The total number of clusters
fluctuated between 10 and 15, although the number of active
devices is constant. This confirms that the proposed algorithm
does not perform effectively in estimating the number ofdevices.

Fig. 9. Time series of surrounding RSSI acquired by one sensor nodeFig. 9 shows the time series of the surrounding RSSI
acquired by one sensor node. The horizontal axis represents
the elapsed time in the experiment, and the vertical axis
shows the surrounding RSSI acquired by one sensor node.
We assumed that a value of −90 dBm or less represents
no transmitted radio waves. Fig. 9 confirms that there is afluctuation of approximately ±10 dBm around −60 dBm whena sensor node measures packets.
The fluctuation of the surrounding RSSI is caused byaveraging the RSSI using a wireless module on the sensor
node. IEEE 802.15.4 employs the RSSI for clear channel
assessment (CCA). IEEE 802.15.4 often considers the averagevalue over 128 µs rather than the instantaneous value of the
RSSI. To determine whether a fluctuation of the surrounding
RSSI occurred by calculating the average of the RSSI, wemeasured the surrounding RSSI using a power sensor that
acquires the instantaneous RSSI. Linear Technology LT5534
was employed as a power sensor.
Fig. 10 illustrates the RSSI acquired by the power sensor.
The horizontal axis represents the elapsed time in the experiment, and the vertical axis shows the surrounding RSSI
acquired by the power sensor. Fig. 10 confirms that thesurrounding RSSI is divided into three values: −65 dBm, −50
dBm, and −30 dBm. The power sensor does not measure an
RSSI smaller than −65 dBm, because of the dynamic range

Fig. 10. Time series of the surrounding RSSI acquired by a power sensor

of the power sensor. Values of −50 dBm are considered torepresent beacons and ACKs of the receiving PC, because
the number of −50 dBm is small. Values of −30 dBm areconsidered to represent the radio waves of the transmittingPC, because the number of −30 dBm is large.
VII. C ONCLUSION
This paper proposed a system to estimate the crowdedness,
namely the numbers of people and wireless communication
devices, in a location where a sensor network is deployed.
Using “Choco”, we proposed a mechanism to acquire andcollect two kinds of synchronized RSSI: the inter-node RSSI
and surrounding RSSI. We proposed and evaluated two algorithms, to estimate the number of people from the inter-node
RSSI and the number of wireless communication devices fromthe surrounding RSSI. An experimental evaluation confirmedthat our algorithm succeeded in estimating the number ofpeople. On the other hand, we observed some problems, suchas averaging the RSSI in a wireless chip, in the estimation of
the number of devices.
ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grant Number JP16H01718 and JP17KT0042.
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