Paper ID #35009

Laboratory Instruction and Delivery of a Pilot IoT Course

Mr. Steven T RowlandMr. Michael William EckelsDr. Ramakrishnan Sundaram, Gannon University

Dr. Sundaram is a Professor in the Electrical and Computer Engineering Department at Gannon Univer-sity. His areas of research include computational architectures for signal and image processing as well asnovel methods to improve/enhance engineering education pedagogy.

c©American Society for Engineering Education, 2021

Work-in-Progress: Laboratory Instruction and Delivery of a Pilot

IoT Course

Abstract This work-in-progress paper discusses the laboratory setup and delivery of a pilot course on the

fundamentals of Internet of Things (IoT). Hands-on laboratory experiments and project-based

experiences are adopted to introduce and reinforce IoT-related concepts. The laboratory

experiments introduce the students to (a) the collection of data using temperature and motion

sensors (b) program the microcontroller, and (c) to communicate between WiFi-enabled

modules. Rather than using the hardware and software tools from an established vendor in the

areas of IoT, we chose to design and assemble our laboratory experiments and projects with

simple, cost-effective, off-the-shelf components. The project activities focused on system design

and integration based on the laboratory experiments to configure IoT devices.

The students are expected to have basic knowledge of electrical circuits and electronics, as well

as programming skills in higher-level languages such as C/C++. The introductory laboratory

experiment engages the student with the Arduino microcontroller and IDE. Students program the

microcontroller to communicate with two temperature sensors, one digital, the other analog. This

introduces the idea behind configuring the laboratory setup to read data from sensors locally and

record the outcomes based on simple programming steps. Later laboratory experiments involve

(a) wireless modules for node-client communication, (b) node to node serial Bluetooth streaming

over Universal Asynchronous Receiver/Transmitter (UART), (c) integration of sensors and other

devices into one "Base station", and (d) using the "Base station" with an ESP8266 WiFi module

to send data to the cloud. One of the term projects required the students to use the collective

knowledge of the laboratory experiments outlined above to create an IoT-based detection

system.. Despite the health pandemic, remote instruction and delivery of course materials, as

well as the evaluation and assessment of the submission of each student was successfully

accomplished.

Introduction Emergent technologies in wireless data communication and computing are rapidly altering the

engineering landscape. The engineering programs at universities across the world must adapt

their courses and curricula to incorporate modern technologies in their courses, classrooms, and

engineering laboratories. The engineering students who enroll at these universities expect to be

educated and trained with the latest industry-approved tools and technologies to function

effectively in the engineering industry. In recent years, the internet-of-things (IoT) [1]-[5] has

burgeoned at astounding rates. The IoT describes the network of physical devices and machines,

loosely labeled "Things" to the internet. These devices are embedded with sensors, software, and

microcontrollers to connect and exchange data with the other devices and/or systems of devices

over the internet. The devices can span the gamut of basic household appliances to complex

industrial tools and machines.

IoT is enabling the following industries to function efficiently and productively at the highest

possible levels.

• Manufacturing – production-line monitoring, detect equipment malfunction

• Automotive – early notification of potential sensor/actuator failures

• Retail – manage the inventory, optimize the supply chain, reduce operational costs, and

improve the experiences of the customer

• Healthcare – patient monitoring, access to medical records

The authors of this paper realized the need for IoT-related laboratory experiments and project

activities in the Electrical and Computer Engineering (ECE) curriculum. Figure 1 illustrates the

intent of the course. The first two authors had been managing and coordinating the ECE

laboratories for the past three to four years and had considerable familiarity, knowledge, and

skills with the components and equipment needed to assemble and test IoT devices. Each device

in the Figure can be assembled and tested without recourse to expensive laboratory-based

equipment. The pilot IoT course was first offered by the ECE department at our University in the

Spring 2020 semester even as the health pandemic was rapidly becoming a global crisis. The first

two authors showed the foresight to prepare the laboratory experiments and project activities not

requiring desktop equipment. This proved extremely useful when the spread of the pandemic led

to the shutdown of the buildings and facilities campus-wide. Even though each student was

unable to access the ECE laboratories and had to work remotely and in isolation, he/she could

still perform the laboratory experiments and project activities of the pilot IoT course. This can be

attributed to the fact that the list of components needed for each laboratory activity was packaged

in portable units and issued to each student. Details of some of the laboratory activities and

project activities are presented later in this manuscript.

Figure 1: IoT devices and the backbone network

This paper is organized in six sections. Section 1 outlines the setup of the course and includes the

course description and the course outcomes (CO), the schedule of the laboratory experiments and

project activities, the use of performance indicators (PI) for each student outcome (SO) as

mandated by ABET [6] and used to measure the CO, and the mapping of each CO to the proper

SO (student outcome). Section 2 provides a summary of each laboratory experiment and the

equipment used. Section 3 summarizes the project activities. Section 4 outlines the remote

instruction during the health pandemic. Section 5 discusses the learning outcomes assessment,

and Section 6 includes conclusions and plans for future work.

Section 1: IoT Course Setup The course is an introduction to IoT at the system, subsystem, and component level. Students

will gain hands-on experience assembling and testing IoT devices to achieve node to client

communication, node to node communication, and peer to cloud communication. The student

enrolled in the course are expected to have knowledge of electric circuits and electronics, as well

as have programming skills in higher-level languages such as C/C++. The laboratory

experiments and project activities are designed to make the student assemble the hardware

components on a breadboard or use wires to interface the components directly to the pins of the

microcontroller or IoT device. The microcontroller IDE [7] provides the ability to create, edit,

and save a program (called a sketch) on the local workstation. The IDE also includes example

software written in C/C++ available for upload to the board. A Web Editor is also available,

enabling programs to be modified and added on/to the Cloud.

The four COs are identified as follows:

CO_1: Describe what IoT is and how it works today

CO_2: Design and program IoT devices

CO_3: Examine the security and privacy challenges of IoT

CO_4: Find proper security/privacy solutions for IoT

The course meets twice each week for fourteen weeks. The duration of each meeting is eighty

minutes (one hour and twenty minutes). Table 1 identifies the topical theme, the laboratory

experiment(s), and project(s) associated with each theme and the session number(s).

Table 1: IoT course outline Theme Laboratory Experiment/Project Session #(s)

Course overview: IoT technology and impact Arduino microcontroller 1-3

Sensors & data communication in IoT

XBee modules 4-6

LoRa Packet Radio 7,8

Bluetooth audio 9,10

UDP transmitter and receiver 11,12

Case study: IoT application and design issues Arduino-based detection system

13,14

Term project 1 14,16

Cybersecurity overview in IoT Encrypted LoRa

17,18

Security threats and data privacy in IoT 19,20

Cloud and IoT integration Access point and Webserver 21,22

Cloud-enabled IoT application ESP and sensor data to Google Sheet 23,24

Term project 2 ESP-based detection system 25-28

Unfortunately, due to the health pandemic, some sessions were missed to allow the students to

transition from working in teams of two in the laboratory to working individually and remotely.

The ESP-based experiment named with the theme Cloud-enabled IoT application (Session #23,

Session #24) was not assigned. Also, the laboratory experiment titled UDP transmitter and

receiver was not assigned but will be on the list for the IoT course in Spring 2021.

The PIs (Performance Indicators) are associated with each SO. PIs are used to gauge the

competency of the student to meet or exceed course expectations in each CO. The key

assignment is used to measure the PI. The typical relationship between the key assignment and

the justification for the mapping to the chosen PI, the mapping of the PI to the CO, and the

corresponding SO is shown below for CO_2 and CO_3.

SO_6: An ability to develop and conduct appropriate experimentation, analyze, and interpret data,

and use engineering judgment to draw conclusions

CO_2: Design and program IoT devices

PI_6_2: Conduct experiments and perform measurements

Key Assignment: Lab Experiment 2

Justification: Lab Experiment 2 requires the student to (a) set up node-client

communication and (b) send the state of a sensor from the node to the

client. Lab experiment 2 measures the ability to conduct experiments and

perform measurements (PI_6_2), and the ability to develop and conduct

appropriate experimentation, analyze and interpret data, and use

engineering judgment to draw conclusions (SO_6).

SO_4: An ability to recognize ethical and professional responsibilities in engineering situations

and make informed judgments, which must consider the impact of engineering solutions in

global, economic, environmental, and societal contexts

CO_3: Examine the security and privacy challenges of IoT

PI_4_2: Understand technology, its application, and potential consequences

Key Assignment: Lab Experiment 4

Justification: Lab Experiment 4 requires the student to implement secure peer to cloud

data communication (PI_4_2). Lab experiment 6 measures the ability to

recognize ethical and professional responsibilities in engineering situations

and make informed judgments, which must consider the impact of

engineering solutions in global, economic, environmental, and societal

contexts (SO_4).

Section 2: Laboratory Experiments The following primary devices are used in laboratory experiments.

• Arduino Mega 2560 [Figure 2(a)]

• ESP8266 WiFi module [Figure 2(b)]

• XBee wireless module [Figure 3(a)]

• LoRa packet radio module [Figure 3(b)]

• Bluefruit Low Energy (BLE) module [Figure 4]

(a) (b)

Figure 2: (a) Arduino Mega 2560 (b) ESP8266 WiFi

(a) (b)

Figure 3: (a) XBee wireless module (b) LoRa packet radio module

Figure 4: Bluefruit Low Energy (BLE) module

For instance, Figure 5 shows the wiring diagram for the connection between the digital

temperature sensor (MCP9808) and the Arduino Mega board.

Figure 5: Arduino Mega to the digital sensor interface

Table 2 summarizes the activities and the equipment [8] needed in each laboratory experiment.

Table 2: Laboratory experiment and activities summary

Lab Number Laboratory Experiment and Activities Summary

Introduction

Arduino Microcontroller:

Students will experiment with the Arduino microcontroller and IDE. Students will

communicate with two temperature sensors, one digital, the other analog. This will

introduce the idea behind reading from sensors locally.

1

Server-Client Communication with XBee Modules:

Students will experiment with an IR reflective sensor and 915MHz XBee wireless

modules. Students will set up a node and client (Router and Access Point). The node will

broadcast the state of the sensor, and the client will request the state.

2

Server-Client Communication with LoRa Radio Modules:

Students will experiment with a PIR motion sensor and RFM915 LoRa modules. Students

will set up a node and client communication. The node will broadcast raw motion data.

The client will request recent data packets.

3

Bluetooth Audio Transmission:

Students will experiment with an electret microphone and a BLE module. Students will set

up a Bluetooth stream over UART from one node to the other. Raw microphone data will

be sent to be analyzed later.

4

Base Station Configuration & Encrypted Communication with LoRa:

Students will use the knowledge learned thus far to implement all sensors and

communication devices into one "Base station". The base station will be able to receive

interrupt-driven data from all wireless sensor stations.

5

Access Point and Webserver

Students will use the "Base station" with an ESP8266 to send data to the cloud. Students

will use Circussofthings.com and the Arduino API to link the sensors to the cloud.

6

IoT-Based Detection System

Students will use the collective knowledge of experiments 1-5 to create two models of a

"Wildlife detection system". Students will use the sensors and wireless methods discussed

in the earlier lab experiments.

For instance, the laboratory experiment on Bluetooth audio transmission includes the circuit shown

in Figure 6. The BLE module (Figure 4) is configured for serial communication using UART and

together with an electret microphone connected to the Arduino microcontroller board.

Figure 6: BLE module and electret microphone with Arduino

The Arduino microcontroller is programmed to transmit the audio data stream from the

microphone to the mobile device (e.g., phone) using Bluetooth. The student sets up the Bluefruit

App on the mobile device [Figure 7(a)] and selects the BLE based on the signal strength [Figure

7(b)]. The BLE connection to the mobile device reveals the available modules [Figure 8].

(a) (b)

Figure 7: (a) Bluefruit App (b) Device selection

Figure 8: Available modules

Thereafter, the student confirms data transmission by speaking into the microphone and

observing (UART option) and plotting the data stream on the mobile device (Plotter option), as

shown in Figure 9(a) and Figure 9(b) respectively.

(a) (b)

Figure 9: (a) Data transmission (b) Plot of the data transmitted & received

Section 3: Project Activities The project activities comprise the design of the integrated system to respond to the motion of

small and large moving objects e.g., wildlife. The subsystems needed for the integration are

identified from the laboratory experiments. Specifically, the goal is to assemble and test the IoT-

based detection system to track the activity of wildlife based on the ambient temperature. The

student will recognize the need for portability and sensitivity of the system in the context of the

size of the species of wildlife. The ESP8266-based IoT-detection system is better suited for

wildlife of smaller size e.g., birds, and the Arduino-based IoT-detection system is intended for

wildlife of larger size e.g., deer or rhinos as shown in Figure 10.

Figure 10: Wildlife and IoT-based detection system

ESP-based IoT-detection system for small wildlife

First, the student is required to assemble and test the subsystems designed in the previously

completed laboratory experiments. Figure 11 shows the subsystems.

Figure 11: Subsystems of the ESP-based IoT-detection

The wiring diagram for the assembled system is shown in Figure 12.

Figure 12: Wiring diagram of the ESP-based IoT system

Figure 13 shows the system assembled from the subsystems in Figure 11.

Figure 13: ESP8266-based integrated system

The program code (sketch) was compiled and loaded on to the ESP8266. The display (two

snapshots) of the ambient temperature due to the activation of the motion sensor (IR switch) on

the mobile device is shown in Figure 14.

USB-mini

to USB-B

FTDI

ESP8266

TMP36

IR switch

Figure 14: Two snapshots of temperature recorded when the motion sensor is activated

Arduino-based IoT-detection system for large wildlife

Figure 15 displays the subsystems for the Arduino-based IoT system.

Figure 15: Subsystems of the ESP-based IoT-detection

The integrated system is shown in Figure 16.

Figure 16: Arduino-based integrated system

The evidence of the operation of the system is shown in Figure 17.

(a) (b)

Figure 17: (a) Temperature data (b) Temperature plots

Section 4: Remote Instruction and Delivery of the Course One of the major advantages of the pilot IoT course is that it can adapt to the switch from in-

person team-based laboratory-confined instruction and delivery to individual-based and remote

instruction and delivery. This became necessary during the second half of our Spring 2020

semester when the classrooms and laboratories in our building were inaccessible due to the

health pandemic. The first two authors assembled portable kits with all the components and issue

one kit to each student enrolled in the class. The laboratory experiments, written as step-by-step

guides, are posted to our learning management tools (EvalTools and Blackboard) for the student

to remotely access from a computer or mobile device. As noted above, the laboratory

experiments are stand-alone i.e., do not require typical function generators and oscilloscopes

which are only accessible in the traditional in-person laboratories and unavailable for use during

the health pandemic. The mobile device such as the phone or laptop suffices for the collection of

evidence in each experiment and project. Zoom-based meetings during the class periods enabled

each student to share their progress with the instructor.

Section 5: Learning Outcomes Assessment The pilot IoT course was offered as a cross-listed class (undergraduate and graduate engineering

students) in the Spring 2020 semester. The class comprised the following engineering students.

• Undergraduate (electrical and computer, mechanical, biomedical)

• Graduate (electrical, embedded software)

The survey completed by each student at the end of the semester comprised the quantitative

section and the qualitative section. The quantitative section consisted of specific questions in

categories related to the COs (Course Outcomes), course items, and the overall evaluation of the

course. The qualitative section asked the students to comment on the course content and delivery.

Course Outcomes

The undergraduate students enrolled in the pilot course comprised students from the Mechanical

Engineering (ME) and Biomedical Engineering (BME) programs at Gannon University. There

were no Electrical and Computer Engineering (ECE) students enrolled in the course. Table 3

displays the responses of the five undergraduate students to the COs. The ME and BME

undergraduate students had some or no programming experience in C/C++. The students used

the internet search engines to understand and apply the steps of each program. The average

response of the students ranged from 40% to 60% between agreement and strong agreement that

the COs were achieved.

Table 3: Course Outcomes - Undergraduates

The graduate students comprised two groups of electrical and embedded software students. The

first group of seven students took the course as a technical elective. Table 4 shows the responses

of the first group in the CO category. Unlike the undergraduate students, the ECE graduate

students had the skills necessary to complete the hardware assembly and programming steps in

C/C++. The students are strongly in agreement (85.7%) for three of the four COs. The outlier,

one student for this sample size of seven, did not see evidence of security and privacy solutions

in the laboratory experiments.

Table 4: Course Outcomes - Graduates (seven students)

The second group of three graduate students chose this course to complete the course project

report. This is necessary to fulfill the requirements of the MSEE graduate degree. The course

project reports are titled (a) Intelligent Traffic Management System (b) Temperature and

Humidity Monitoring System, and (c) Smart Home Design. Table 5 displays the responses of the

three graduate students with the course project option. Two students out of the three in this group

(66.7%) are strongly in agreement that three of the four COs were achieved. The outlier, one

student for this sample size of three, did not see evidence of security and privacy challenges and

solutions in the laboratory experiments.

Table 5: Course Outcomes - Graduates (three students)

Course Items

Table 6 displays the survey responses in the category of Course Items for the five undergraduate

students. Four students out of the five undergraduate students (80%) were (a) very interested in

the course, (b) found the extent of work required in the course to be comparable to other courses,

and (c) satisfied that the course was effective in achieving the instructional objectives and

student learning outcomes.

Table 6: Course Items - Undergraduates

Table 7 displays the survey responses for the first group of seven graduate students. Six students

out of the seven students in this group (85.7%) were (a) very interested in the course, (b) satisfied

that the course was effective in achieving the instructional objectives and student learning

outcomes. However, only four students out of the seven students (57.1%) found the extent of work

required in the course to be comparable to other courses.

Table 7: Course Items – Graduates (seven students)

Table 8 displays the survey responses for the second group of the three graduate students with

the course project option. All the students in this group were (a) very interested in the course,

(b) found the extent of work required in the course to be comparable to other courses, and (c)

satisfied that the course was effective in achieving the instructional objectives and student

learning outcomes.

Table 8: Course Items - Graduates (three students)

Overall Evaluation

Table 9 shows the survey responses in the category of Overall Evaluation by the five

undergraduate students. Four students out of the five undergraduate students rated the overall

quality of the course and the performance of the faculty as excellent. The entire class of five

students rated their overall learning experience as excellent.

Table 9: Overall Evaluation – Undergraduates

Table 10 shows the survey responses by the group of seven graduate students. Six students out of

the seven students in this group rated (a) the overall quality of the course, (b) the performance of

the faculty, and (c) their overall learning experience as excellent.

Table 10: Overall Evaluation - Graduates (seven students)

Table 11 displays the survey responses by the group of three graduate students. All the students in

this group rated (a) the overall quality of the course, and (b) their overall learning experience as

excellent. Two students out of the three students in this group rated the performance of the faculty

as excellent.

Table 11: Overall Evaluation - Graduates (three students)

Section 6: Conclusions and Plans for the Future The pilot IoT course can be linked to the three domains of Bloom’s Taxonomy [9],[10]. The

cognitive domain, characterized as What should I know?, relates to recall and recognition of

specific facts, patterns of procedure, and concepts that aid in the development of intellectual

abilities and skills. The course incorporates the knowledge, comprehension, application, analysis,

and synthesis of electrical and computer engineering principles to the design of IoT devices. The

affective domain, characterized by How should I know? relates to the mental state and is the

motivation and attitude of the student to the experiments and projects in the course. The

psychomotor domain, characterized by What should I be able to do with what I know?, relates to

the skill level of the student to perform and complete the tasks assigned in each laboratory

experiment and project activity.

The course will be integrated with the course on applied artificial intelligence (A2I). IoT

connects the devices to enable data collection while AI makes the decisions and takes the actions

based on the data collected. Thus, data-driven IoT applications can link to machine learning and

deep learning concepts covered in A2I.

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