Smart Cities V2I Cloud based Infrastructure using Road Side Units

Tamer Omar

, Kevin Guerra, Christopher Mardoyan, Shannen Sharma and Xavier Rangel

Department of Electrical and Computer Engineering, California State Polytechnic University, Pomona, U.S.A.

Keywords:

Road Side Units (RSUs), K-means Clustering, Internet of Vehicles, Smart Cities, Vehicle to Infrastructure

(V2I).

Abstract:

Vehicles and city infrastructure can be interconnected through Road Side Units (RSUs) and On Board Units

(OBUs) that utilize Radio-frequency identiﬁcation (RFID) technology to send and receive information about

various road conditions in real time. The objective of this work is to create and test a mesh network through

Internet of Things (IoT) devices to emulate and test the RSUs capabilities. A vehicle-to-infrastructure (V2I)

network is established through parent & child approach that rely on previously established infrastructure. The

purpose for this design is to extend the reach of the system while limiting the amount of endpoint nodes needed.

The mesh network is meant to target areas affected with road congestion. Using this information, the vehicles

and users will be aware of trafﬁc conditions on their routes in real time. Connected vehicles will be able to

adjust their routes to experience more efﬁcient commutes. The mesh network is capable of taking information

from vehicles and transmitting it through the network until being uploaded to the cloud. In particular, the

number of vehicles passing through an endpoint RSU within a certain time frame is collected and sent through

the network, along with the location of the endpoint RSU. The parent node receives this information through

a relay RSUs and uploads it to a cloud service where the data is collected and then analyzed through a data

mining software. The software applies the k-means clustering algorithm to classify the trafﬁc conditions of

the road at a particular time. Results shows the capability of the algorithm to detect and classify the different

trafﬁc conditons.

1 INTRODUCTION

Recently Internet of Vehicles (IoV) is considered one

of the most accelerating research araes. Investiga-

tion optimal communication and computing methods

for vehcle connectivity is deemed essentail to create

an IoV. Successful efforts is recognized in innovating

Autonomous Vehicles (AV) include using On Board

Units (OBUs) that can communicate wirelessly to as-

sist AVs staying in the correct lane or during self-

parking. To further innovation and efﬁciency, Road

Side Units (RSU) can be implemented to assist ve-

hicles by collecting data regarding various road con-

ditions, and then use this data to reroute vehicles to

alternative roads that decrease travel time. Addition-

ally, the data collected will provide useful information

for emergency services as they quickly respond to ac-

cidents and other hazards that appear on roads.

RSUs are designed to create more efﬁcient routes

and a much safer environment for fully autonomous

vehicles on the road. The RSU communication hap-

https://orcid.org/0000-0001-5619-8747

pens in an ad-hoc network where the vehicles can

communicate among one another, but the data ex-

change can be expanded with the use of the roadside

units(Silva and Meira, 2016). In (Silva and Meira,

2015) the data is processed and translated into useful

information and recommendations to assist the users

of the transportation and transit authorities. The RSU

will be combined with the communications from Ve-

hicle Ad-hoc Network (VANETs) where the RSUs

disperse messages at designated locations along the

road network, and will expand on the types of in-

formation collected such as weather conditions, road

work, the time, ﬂuctuations of trafﬁc, and so forth,

which will lead to much more accurate coverage of

data, and expand coverage as referenced in (Albouq

and Fredericks, 2017; Lin and Rubin, 2017).

For this system to operate, the corresponding com-

ponents need to be able to collect, track, and distribute

information at high rates, and in large volumes. The

studies in (Ansari et al., 2013; Santos et al., 2016)

used a system consists of a microcontroller, GPS,

Digital Short-Range Communication (DSRC) mod-

ule, WIFI module, and several other components. The

270

Omar, T., Guerra, K., Mardoyan, C., Sharma, S. and Rangel, X.

Smart Cities V2I Cloud based Infrastructure using Road Side Units.

DOI: 10.5220/0010469402700277

In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 270-277

ISBN: 978-989-758-504-3

system is (Ferre et al., 2013) is used for charging sta-

tions, the system being implemented is altered to meet

the demands of being on a road with live trafﬁc. Ra-

dio frequency identiﬁcation allows for transfer of spe-

ciﬁc data, and with the integration of sensors and mi-

crocontrollers along with the cloud provides the com-

munication protocols, and service applications for an

efﬁcient network (Salahuddin et al., 2015).

While RSUs seem like a viable solution for most

issues regarding VANETs and autonomous vehicles,

they do come with their own sets concerns. As pre-

sented in (Ota et al., 2017), RSUs placement can

have a huge inﬂuence in how efﬁcient their energy

consumption is as well as how many RSUs must be

placed per area. With cost in mind, RSUs must be

placed efﬁciently to obtain the best service for the

money being spent. Fixing the energy consumption

issue can be done in numerous ways, such as solar

power which can be used as the main power source

for the RSUs, as their placement will consist entirely

of outdoor settings (Khezrian et al., 2015). Addition-

ally, authors in (Mostoﬁ et al., 2013) suggests a vi-

able solution for energy consumption by proposing a

ON/OFF sleep schedule for the RSUs wherein there

are designated times at which less-used RSUs will be

shut off. These RSU locations will be based on the

frequency at which roads are used at certain times.

As (Silva and Meira, 2015) mentions, a combina-

tion of stationary RSUs combined with mobile data

centers, which will be vehicles with on board units,

creates a network that has a wide coverage range and

the most diverse capabilities due to the variety of in-

formation that can be collected from the mobile units,

as well as the available information from the station-

ary RSUs. The stationary devices can be installed on

the side of the road or on a roadside pole and come

equipped with storage, processors, and network capa-

bilities that allow for the communication among vehi-

cles with the use of DSRC.

With the support of VANETs, autonomous vehi-

cles will be able to travel with uniform spacing be-

tween cars which will alleviate trafﬁc jams and im-

prove fuel efﬁciency. One issue with pure VANET

systems is the amount of data that can be passed

through, which causes this communication to be lim-

ited. So, with the support of RSUs, the amount of

data being used can be increased which will provide

a more accurate and more robust infrastructure.

A wireless mesh network is a communications

network made up of nodes conﬁgured in a mesh topol-

ogy. The network topology is an arrangement of how

the system is communicating to one another. An ad-

hoc network is a form of a mesh network that con-

tains wireless nodes. These nodes can form a network

that does not rely on ﬁxed network infrastructure.

Multiple studies discussed the differences between

VANETs, MANETs, and Intelligent Transportation

System (ITS) as sub-classes of an ad-hoc network.

All of these networks and systems combine the telem-

atics to the roads so that a system can maintain the

goals for achieving better roadways which will create

a safer road, environmental protection, efﬁciency, and

beneﬁt the economy (Sharmila and Shanthi, 2016).

The mesh ad-hoc network is designed with three

types of nodes, each with a speciﬁc function. Gate-

ways are devices that can connect beyond the mesh

network and have access to the cloud. Repeaters are

devices that are capable of forwarding data between

endpoints in the mesh network. Endpoints are de-

vices that are not capable of sending messages for

other devices within the mesh network. These three

types of nodes create the mesh network that is capa-

ble of transmitting data of the road conditions to the

server via 5G (Lee, 2018; Kuo et al., 2013).

There are multiple types of ad-hoc networks that

can be used to create a mesh network – mainly Wi-

Fi direct, Bluetooth or ZigBee conﬁgurations. All

have strengths and faults. Wi-Fi direct has superior

range and data transmission rates. However, as ad-

hoc network information cannot inherently be shared

between two client systems and must instead only be

shared to a host, it also cannot perform any node-

hopping techniques to send information (Wibisono

and Bayhaki, 2015; Ashritha M and Sridhar C S,

2015).

Road conditions are constantly changing in dif-

ferent areas, which cause back-ups, sudden speed

changes, and other types of hindrances on the road.

The study in (kumar Gupta and Khara, 2015) calls for

a system that will obtain current location road condi-

tions, which is very speciﬁc for obtaining proper road

conditions. There are several techniques that can han-

dle the network connection for a multi-hop data ex-

change between Vehicle to Infrastructure (V2I) and

between Vehicle to Vehicle (V2V), which will build a

much more secure communication system. Road Side

Units are relay nodes that can connect the VANET

nodes with the rest of the network, and make the

RSUs a key component for applications that require

more than one application in the VANET.

This work aims at proposing a method for V2C

communication that rely on VANET. The RSUs in

the VANET will communicate information to one an-

other so the data can quickly transfer to each repeater,

which will then transmit the data to an endpoint. This

endpoint will upload data to a cloud from which in-

formation will be dispersed to vehicles on the road.

Using this method, the data collected will be read-

Smart Cities V2I Cloud based Infrastructure using Road Side Units

271

ily available for vehicles to receive miles away from

the origin destination. This will be possible through a

node-hopping method of information transfer, where

a node will use its neighboring nodes as relay points

to send the data to a speciﬁc node.

Using strategic placement, the RSU can be opti-

mized for ideal coverage with minimal RSU deploy-

ment, and cover cities as well as suburban areas. Ad-

hoc networks do not rely on previous infrastructure,

thus can decrease expenses required for RSUs to act

as a gateway. The gateways receives information from

various nodes and is the only component capable of

conveying data to the cloud. The repeater is capable

of only receiving and exporting data from other re-

peaters or a gateway, which will expand the range of

communications. With proper placement techniques,

data will be transferred from one RSU node to the

next, quickly providing seamless information to the

gateways in the mesh network.

Radio Frequency Identiﬁcation (RFID) will be

used to collect high volumes of data from vehicles on

the road. The vehicles will have an OBU that con-

tains a RFID reader and RFID tag to transmit and re-

ceive data. The RSU will be equipped with the same

technologies to create a two-way data transfer system.

With this dual transfer setup, cars will receive up-to-

date information about the roads they travel on, and

RSUs can receive up-to-date information from vehi-

cles that have already passed. This method may also

be used to update vehicles with speed limit informa-

tion as well as other localization data or obstructions

that may be located within the proximity of the RSU.

The information on various road conditions col-

lected from this network will be sent to a parent unit

that is connected to a cloud with use of a 5G network.

The RSU that contains 5G capabilities is referenced

as a parent unit, and only parents are connected via

5G and have the capabilities of collecting data from,

and sending data to, the cloud server. The server will

be used to store the data it has collected, which will

then be distributed to the RSUs and transferred to the

vehicles on the road. The information can modify ve-

hicle routes and let the vehicles be aware of other any

hindering obstructions on their routes.

2 SYSTEM ARCHITECTURE

Vehicle to Cloud (V2C) connectivity requires differ-

ent forms of communication and this is where the ar-

chitecture of the cloud comes into play. It is impor-

tant to remember that when using for example a 5G

network to connect to the cloud, three different types

of communication among the vehicles and infrastruc-

ture can be created. These three types of communi-

cations are: vehicle-to-roadside unit (V2R), vehicle-

to-vehicle (V2V), and vehicle to infrastructure (V2I).

These components allow for edge and core cloud lay-

ers to be created, which means it can support data

collection and exchanging, fully mining and utiliza-

tion of collected data, and using various real-time

efﬁcient and secure applications. Reliable and on-

demand vehicular data services and applications can

be implemented in the cloud. Furthermore, in these

cloud core and edge layers exists the permanent and

temporary cloud computaional resources. In the core

cloud there are temprory computing resources orga-

nized and made available to provide trafﬁc managa-

ment and analysis as required. In the edge cloud,

storage and communication resources of vehicles and

road-side units are implemented to extend the core

cloud and provide capabilities of storage and cloud

computation.

Additionaly, there are security issues that do arise

with this 5G cloud connectivity and IoT devices. One

of the main issues is the security and privacy preser-

vation of collected data from the VANET. This mainly

has to do with the types of data and information that

are being passed and collected in the cloud. This is a

big worry for a lot of users when it comes to stor-

ing information into the cloud because information

breaching is very common. It is recommended to in-

corporate proper security solution with the proposed

vehicular to cloud platform to ensure security and pri-

vacy.

Vehicular cloud computing allow for higher vol-

umes of data to be transmitted and collected in the

cloud. With 5G become readily available, there will

be support for various connectivity and signiﬁcantly

faster transmission with 5G communications. A V2C

platform will be enabled to provide more reasonable

and realistic services with the support of a 5G net-

work. A computational infrastructure must be con-

nected with a the vehicle grid that contains RSUs

where it serves the purpose for creating a more efﬁ-

cient vehicle grid. Furthermore, the vehicle network

is referred to as VANET where vehicles have OBUs

installed and also roads are equipped with RSUs.

The V2C is capable of contributing in several dif-

ferent ways since it stores data of current road con-

ditions. The vehicles can be interconnected to the

cloud through the OBUs and the RSUs act as gate-

ways to send the data to the cloud. V2C will primar-

ily hold safety and also non-safety-related data that

will be transmitted to vehicles for increasing road efﬁ-

ciency. There are three methods for disseminating this

information to vehicles, one through the VANET us-

ing V2C communication, or using Vehicle to Vehicle

IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security

272

(V2V) communicaion, or by communication between

RSUs . These methods are all supported for exchang-

ing data in a modern transportation system according

to the distances the systems can reach.

2.1 System Design

The overall design of the system for developing a

wireless mesh network between nodes to collect the

data and transport it into three layers of communica-

tion is shown in 1. These layers will be comprised

of several devices and modules that will create a net-

work. Within this network, information about trafﬁc

from vehicles will be communicated to a cloud ser-

vice where data will be analyzed and sorted into cate-

gories that describe various road conditions

2.2 Parent Nodes

The Raspberry Pi acts as the gateway, which will

make it the only component of the entire system that

is able to receive data, send data, and upload data to

the cloud. The model being used is the Raspberry

Pi Model 3B, which was chosen since Bluetooth ca-

pabilities are preinstalled and will not require extra

modules to transmit the data along the network. Ad-

ditionally, this model Raspberry Pi also contains the

ability to be connected to Wi-Fi which is ideal for

testing since it can upload data at a faster rate. The

Raspberry Pi does have an additional 3G module that

can connect the Raspberry Pi to the server without the

use of Wi-Fi. The Raspberry Pi contains Python code

that allows the transmission of data from the Arduino,

and then the data collected will be saved as a CSV ﬁle

and then uploaded to the set server that will identify

patterns in trafﬁc conditions.

2.3 Repeater Nodes

The Arduino Uno connected with two Bluetooth mod-

ules creates the repeater portion of the system. It

does so by receiving data through one of the Blue-

tooth modules then proceeding to process and con-

tinue sending the data so that it may be received by

the gateway in a readable format. The Arduino com-

pletes this process by ﬁrst seeking an incoming mes-

sage from the Endpoint via the HC-05, then uses the

second Bluetooth module to relay the information to

the Gateway.

2.4 Endpoint Nodes

The endpoint Arduino Uno is the middle-point be-

tween the “vehicle” and the Repeater Arduino Uno.

Figure 1: System Design.

It is the ﬁrst interaction between a vehicle and the

system itself. The endpoint Arduino is in charge of

collecting the data from the vehicles. The method

for this data collection involves the use of RFID

via a MFRC522 RFID Reader module added to the

endpoint Arduino operating at a frequency of 13.56

MHz. Radio-Frequency Identiﬁcation allows a seam-

less, low-cost, and efﬁcient way of transmitting data

from the vehicles to the system. The endpoint Ar-

duino collects the data, processes it, and then trans-

mits that data and information to the Repeater Ar-

duino. The method of transmitting this data is ac-

complished through Bluetooth, via a Bluetooth mod-

ule added to the Repeater Arduino. The program that

the Repeater Arduino uses to collect the data, pro-

cess it, and transmit was developed using the Arduino

IDE with the Arduino Uno and MFRC522 libraries.

The Endpoint Arduino along with the Repeater Ar-

duino have the capacity to be battery-powered, allow-

ing them to have their own independent power source.

2.5 Cloud Service

The cloud service being used is called ThingSpeak.

ThingSpeak is an Internet of Things (IoT) provider

that will allow the storing of data to be possible

through the internet. With ThingSpeak, it is possible

for one to send data through a private cloud, analyze

the collection data, and create graphs using MATLAB

and essentially act on the data as well. Utilizing this

service, a channel will be created to collect all the data

and effectively display the amount of data being col-

lected at different trafﬁc conditions. This data will

be stored in the cloud and then saved as a CSV ﬁle,

which will then store and graph the data using a chan-

nel ID on ThingSpeak. On ThingSpeak, there are two

separate ﬁelds used to represent that number of cars

Smart Cities V2I Cloud based Infrastructure using Road Side Units

273

Figure 2: K-means Block Diagram.

passed and the location that the vehicle is at. From

here, the data will be classiﬁed in real time showing

the progression of trafﬁc as well as the differentiation

of location.

2.6 System Data Analysis

RapidMiner is a data service software that will be

used to facilitate organizing, sorting and analyzing the

data received from the vehicles. The software is capa-

ble of providing a machine learning and deep learning

environment for our data to help discover meaningful

patterns in the data. The project will use RapidMiner

to demonstrate how the system can be beneﬁcial once

implemented. More speciﬁcally, in this program, the

k-means clustering algorithm is used to cluster the

data created. These clusters are based off of similar-

ities between the data points uploaded from the CSV

ﬁle. The clusters will serve as classiﬁcations to our

three trafﬁc scenarios, low trafﬁc, medium trafﬁc and

high trafﬁc.

In order to utilize the k-means algorithm in Rapid-

Miner, the block operators as shown in 2 were used to

allow the data from the CSV ﬁle to be imported. The

select attributes operator’s purpose is to only select

the subset of attributes needed and exclude the other

data, so in this case, the entry ID number will be ex-

cluded. The set role block allows the user to select

the data in the number of vehicles passing and make

it target role. Lastly, the clustering operator uses the

k-means algorithm to classify a set of k-clusters based

on the similarity of the data.

K-means operates by taking n observations of in-

formation and creates k-sets by which to divide the in-

formation into. The algorithm that determines a mean

for each data set and uses that mean along with the

rest of the points present within the set to determine

the actual centroid of the data set. The algorithm ﬁ-

nally uses the new centroid to determine if more data

points from the original set belong in its respective

cluster based on its variance from the centroid and

calculate it as follows equation 1:

arg

min

∑

i=1

∑

XεS

X − µ

= arg

min

∑

i=1

var S

(1)

In this equation, S

is the respective set that the clus-

ter is being determined for and µ

is the mean of the

points within S

as well.

3 SYSTEM CONFIGURATION

An important aspect of this project is being able to ob-

tain real-time and accurate data. The objective when

it comes to collecting data focuses on speciﬁc param-

eters. These parameters are localization, number of

cars passing, and a real time timestamp. With these

parameters, we hope to be able to create a RSU al-

gorithm which will allow for the trafﬁc congestion to

be improved thoroughly. The sampling of data will

all revolve around a simulation which we will create

ourselves. It is difﬁcult to test this out in a real-life

scenario because we do not have the resources for a

large-scale type of testing to be performed. The main

study variables will be the successful detection rate of

the RFID tags with the RFID readers. To maximize

the detection rate as much as possible, it is vital that

the readers on the side of the road are able to capture

the data from the vehicles RFID tags consistently, es-

pecially considering the speed of the vehicles as they

pass the readers. All data will be captured from the

vehicles and successfully passed through road-side

units and eventually to the cloud. The other detection

rate we will be observing is the transfer of data from

the road-side units to the vehicles’ on-board RFID

readers. A successful transfer of data between the

vehicle and the reader will include data such as the

number of vehicles passing by the RSU in order to

determine the volume of trafﬁc. All of this data will

be uploaded to the cloud through the parent RSUs.

This will allow the classiﬁcation of trafﬁc scenarios.

Table1 shows the test bed speciﬁcations and Fig. 3

shows the used hardware.

The Raspberry Pi communicate with the Arduinos

IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security

274

Table 1: Test bed speciﬁcations.

Component Description

Arduino Uno Data Gathering Child Nodes and data repeater nodes

HC-05 Bluetooth Module “AD-HOC” Network (connection in place for Wi-Fi option)

RFID-RC522 RFID Reader/Writer Components

Raspberry Pi 3 B+ Network Connected Parent Node

Raspberry Pi 3G-4G/LTE Base Shield V2

Provides 3G/4G capabilities for Raspberry PiQuectel UC20-G Mini PCle 3G Module

LTE-G-086 Cellular Miniature PCB Antenna

Figure 3: System Footprint.

to receive the data collected by them, and pro-

cess/store the data to a cloud service using wireless

and 4G communication. The Arduinos will work in

tandem to collect and transmit the data received from

the vehicles through the use of RFID and Bluetooth

communication. The Arduinos will then pass the data

along to the Raspberry Pi/Cloud network. The use of

an mPCI-E Base Shield and a UC20-G became nec-

essary as it will allow for 4G netwrok. Furthermore,

this connection is being made through the use of a

sim-card that is connected to the service provider and

ThingSpeak service is used to collect all the incom-

ing data through various channels that collects spe-

ciﬁc data and make a visual representation of the data.

This data is then being ran in RapidMiner which will

create K-mean map that shows a cluster of the data.

For relaying purpose two Arduinos are connected,

the goal was to connect the Endpoint Arduino, which

will receive the data from the vehicles, to the Repeater

Arduino, which will pass the data on to the Raspberry

Pi and continue along the mesh network. The method

of communication between the two Arduinos was

Bluetooth, using a HC-05 Bluetooth Module attached

to each Arduino. The Endpoint Arduino had both the

RFID RC522 module and the Bluetooth module at-

tached to it through its digital ports. The Repeater

Arduino had two HC-05 Bluetooth modules attached

to it through its digital ports. The Endpoint Arduino

was programed to keep track of the number of ve-

hicles that pass through the RFID scanner/simulated

road in a speciﬁed amount of time. In order to sim-

ulate data being collected from multiple sources, we

conﬁgured the code to switch between two identiﬁers

to mimic multiple endpoints so that we will have an-

other parameter by which to sort the data from. This

data was sent to the Repeater Arduino through Blue-

tooth to process and then transmit it to the Raspberry

Pi. The data for the number of vehicles passing by

was desired in order to analyze and determine what

the trafﬁc congestion is on that certain route.

Three trafﬁc scenario (low, medium, high) were

developed for testing purposes, the road being tested

will either have low trafﬁc if the range of vehicles

passed was between 1-15, medium trafﬁc from 16-

30 vehicles, and high trafﬁc from 31-50 vehicles. The

data send and receive is done using the RFID Arduino

by transmitting that data over to the Raspberry Pi.

When conﬁguring the Raspberry Pi 3 to be compat-

ible with the rest of the system, there were numerous

factors to ensure that it can connect to the Repeater

node. The ﬁrst was ensuring that the native baud rate

of the Raspberry Pi matched the HC-05 that it was

to be paired with on the Arduino. This required sev-

eral commands to be written in the terminal to con-

nect the HC-05. Once paired the HC-05 was conﬁg-

ured to match the same baud rate of 115200, this re-

quired forcing the HC-05 into its AT command mode

where the UART number can be conﬁgured. This

baud rate was chosen due to its speed as well as the

ease to conﬁgure the HC-05 as opposed to the Rasp-

berry Pi. We then used a script that can take in the data

and display it on a common window. Once the entire

system was successfully connected, all that remained

was processing the data on the Raspberry Pi to pre-

pare it for use on RapidMiner. RapidMiner is a data

collection software with a clustering algorithm called

k-Means. This algorithm will take the data collected

from the cars and distribute it so it can be classiﬁed as

high trafﬁc, medium trafﬁc, and low trafﬁc.

Smart Cities V2I Cloud based Infrastructure using Road Side Units

275

Figure 4: Number of vehicles Passing/Scan.

4 SIMULATION AND RESULTS

The goal of simulating the results was to have a ran-

dom number of passes for each 15 seconds with an al-

lowed maximum of 50 cars passing at a time as shown

in Fig.4. As progress continued on collecting data, the

group decided to ﬁnd the limits of the range of Blue-

tooth communication for the RSUs. It was an inter-

esting characteristic to investigate, because the most

efﬁcient way for cities to implement a system simi-

lar to ours will be to separate each child RSU as far as

possible in order to minimize the amount needed. The

group moved one RSU farther from another that it was

communicating with, until the pair lost connection to

each other. The group found the distance to be 78 feet

before the two RSUs can not send information to each

other. This was a considerable distance and surpassed

initial speculations. The design of the system was

made with the extremely limited budget in mind, so it

was motivating to see the inexpensive Bluetooth mod-

ules work at such a distance. This range can increase

by a very considerable amount for real-world appli-

cations as opposed to the small-scale model, with in-

creased budget and access to better technology can

extend the range of communication.

The design of the system was also efﬁcient in

transmitting the data we desired to send. Taking less

than a second per data point to collect, we were able to

collect approximately 100 points from which to base

the rest of the project off of. The system was able

to properly simulate multiple locations and send in-

dicators for each; this data was also successfully pro-

cessed by the parent node and placeholder locations

Figure 5: Collected Trafﬁc conditions by RSU.

were able to be created for the respective indicators.

After modifying the incoming data, that system was

also successful in uploading the data correctly to the

cloud server, Thing Speak, with no loss of data. In

Thing Speak, we were properly able to classify the

data into two different ﬁelds, one showing the num-

ber of vehicles passing and the second being the lo-

cation. The trafﬁc conditions were classiﬁed as low

trafﬁc if there were 1-15 vehicles passing, medium

trafﬁc if there were 16-31 vehicles, and lastly high

trafﬁc if there are 32 vehicles or more passing. For

testing purposes, there were 100 scans done. As pre-

sented below in Fig.5, the scanned trafﬁc is focused

on mainly medium and low trafﬁc.

When comparing the centroid values in the Table

2, it can be seen that the k-means algorithm is work-

ing as intended. By separating the data into three par-

titions, the algorithm was able to determine a value

that will become the center for each cluster, the cen-

troid. Using the average of the set of data points as the

centroid, the algorithm was able to determine roughly

the same range for the trafﬁc conditions that we had

set prior to the experiment. The algorithm then set

a new centroid based on the points gathered in each

cluster which can be seen in Fig.5 One thing to note,

however, is that we did set our trafﬁc parameters to

be roughly equal separations of our maximum trafﬁc

value of 50, so it is interesting to see that the centroids

that were calculated created clusters that matched our

classiﬁcations.

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Table 2: Test-Bed Speciﬁcations.

Centroid Table for Fig.5

Attribute Cluster 0 (Low Trafﬁc) Cluster 1 (High Trafﬁc) Cluster 2 (Medium Trafﬁc)

# Vehicles Passing 3.811 41.839 23.594

5 CONCLUSIONS

Through the use of IoT devices, we were able to cre-

ate an ad-hoc mesh network using a cloud-based in-

frastructure from these Road Side Units. With this

system designed, there are 3 speciﬁc nodes discussed

– an endpoint node, repeater node, and a parent node.

After each tag is scanned by the RFID reader, the end-

point node will collect 2 speciﬁc parameters, the lo-

cation and the number of vehicles passed within a 15

second time range. The purpose of the repeater node

is to receive the data via the ﬁrst Bluetooth module

and then relay that information to the parent node via

the second Bluetooth module. Lastly, the purpose of

the parent node is to act as a gateway by being able

to receive, send, and upload the collected data into

our cloud service provider, Thing Speak. With Thing

Speak, all the collected data is uploaded to the cloud

and can be exported as a CSV ﬁle. After this, Rapid

Miner, a data mining service is used to effectively uti-

lize the k-means clustering algorithm to display the

collected data into 3 clusters to show low, medium

and high trafﬁc. With the ﬁnal design, the system was

able to cover 78 feet indoors between the endpoint

and repeater RSUs.

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