=Paper=
{{Paper
|id=Vol-2616/paper19
|storemode=property
|title=A Mixed Fog/Edge/AIoT/Robotics Education Approach based on Tripled Learning
|pdfUrl=https://ceur-ws.org/Vol-2616/paper19.pdf
|volume=Vol-2616
|authors= Roman Hasko, Nataliya Shakhovska, Olena Vovk, Roman Holoshchuk
|dblpUrl=https://dblp.org/rec/conf/coapsn/HaskoSVH20
}}
==A Mixed Fog/Edge/AIoT/Robotics Education Approach based on Tripled Learning==
https://ceur-ws.org/Vol-2616/paper19.pdf
A Mixed Fog/Edge/AIoT/Robotics Education Approach
based on Tripled Learning
Roman Hasko[0000-0002-0567-2995], Nataliya Shakhovska[0000-0002-6875-8534],
Olena Vovk [0000-0001-5523-0901], Roman Holoshchuk[0000-0002-1811-3025]
Lviv Polytechnic National University, Lviv 79013, Ukraine
r.hasko@gmail.com, nataliyabshakhovska@lpnu.ua,
olena.b.vovk@lpnu.ua, roman.o.holoshchuk@lpnu.ua
Abstract. The article describes a triple learning approach to educational process
using a specialized three-level training robotics platform to train professionals
in the higher education system, which includes software and hardware for the
sequential study of general intelligence related AI, such as Internet of Things,
Cloud Computing, Robotics, and more. The described solution is based on
Fog/Edge/AIoT stack and allows to build both set of laboratory works and
complex completed projects in the context of the concept of tripled learning, as
well as to carry out research projects. The platform described is modular and
contains several levels of difficulty for optimal configuration according to the
course syllabus. The usage of an Artificial Intelligence approach with remote
data preprocessing (at edge level) can significantly reduce the computing and
time resources and complexity, as well as refuse centralized storage of the
entire array of data, which brings the processing of data much closer to online
mode. The proposed platform allows improving the quality of the educational
process, to increase its orientation to practical experience and to reflect the
current state of AI synergy through Fog/Edg/Cloud with IoT.
Keywords: AIoT, Fog, Edge computing, Robotics, Cloud, Tripled Learning.
1 Introduction
The educational process for teaching an engineer in scientific information
technology using modern software and hardware is high complicated and must be in
permanent progress [1]. The development of IT, like other evolutionary processes,
can be characterized by spiralling growth. First of all, a return to saved solutions, but
to a higher technological level. The principle of operation is a mainframe with an
extended system of terminals for operators. Merged factual terminals have gradually
grown and evolved into "smart" terminals, using personal computers (PCs).
Autonomous PCs have become integrated into networks, with the development of
which the Internet has become a self-contained phenomenon. Accordingly, the
dominance of the client-server architecture can be considered to some extent
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0). COAPSN-2020: International Workshop on
Control, Optimisation and Analytical Processing of Social Networks
equivalent to the "mainframe" transposed to a higher technological level. Gradually,
individual servers began to merge into clusters, and a cloud-based distributed
computing system developed, and the Internet began to integrate a huge number of
peripherals, the Internet of Things (IoT).
The ever-increasing amounts of information, including high-resolution video
streams from a variety of surveillance cameras, and, consequently, the need to process
them in near real-time, have led another developmental spiral to delegate some
computing capabilities to the Cloud level.
The emergence of Edge/Fog computing [2] and the emergence of "smart" IoT -
AIoT is a result of the synergy of IoT and artificial intelligence (AI).
At the same time, there is a rapid evolutionary development of a fundamentally
new concept for human civilization - creation of robots endowed with certain artificial
intelligence, oriented mainly in narrow spheres, such as:
─ orientation and autonomous movement in different terrains,
─ the performance of specific technological functions and other activities that do not
require high level of ownership.
The next stage of development is the emergence of "full" artificial intelligence -
Strong Artificial Intelligence, known as Strong AI in English sources [3], or full AI
[4] or Artificial General Intelligence (AGI) [5].
The purpose of the article is to describe the principles of blended learning
using a robotic platform and artificial intelligence on the Internet of Things.
2 The educational robotic platform
It is suggested to use a specialized training platform to train professionals in the
higher education system, which includes software and hardware for the sequential
study of general intelligence related AI (AI), such as Internet of Things, Cloud
Computing, Robotics, and more.
The described solution allows to build both complex laboratory works and
complete completed projects in the context of the concept of tripled learning [6], as
well as to carry out research projects. The platform described is modular and contains
several levels of difficulty for optimal configuration according to the course syllabus.
The first level is a versatile standalone T-Bot mobile platform based on the
popular Arduino training microcontroller with distance, motion and color sensors.It is
also possible to use a more powerful microcontroller from the STM32 line. This level
has already been successfully implemented in the educational process during the three
years of study in the course “Algorithmization and Programming. Part 1” of the first
year of study at the Department of Artificial Intelligence of the Lviv National
Polytechnic University. Photo of this hardware presented on Fig.1
The second level is structurally placed above the first and is responsible for more
intelligent capabilities such as image recognition, object analysis and communication
with the Cloud. Technically, this layer is a Raspberry Pi 3/4 microcomputer with a
video/web camera and OpenCV or OpenVINO. The main task class is machine
learning, that is, the computation of pre-trained and optimized neural networks,
usually using Keras with TensorFlow. Because the Raspberry Pi's computing
resources are not enough to fully support artificial intelligence tasks, particularly
when moving from cloud to Edge/Fog Computing, the next component of this layer is
a separate, dedicated neural network-oriented USB module.
Fig. 1. Appearance of the first-level T-Here robotics-training platform with Arduino and
ultrasonic rangefinder.
Examples of this approach are Intel's Myriad ™ X VPU [7] - the third generation and
the most advanced VPU from Movidius ™, Intel, or the Google Edge Coral
Accelerator [8]. Intel's Myriad ™ X VPU for the first time in its class features the
Neural Compute Engine, a specialized hardware accelerator for deep neural networks.
The combination of 16 powerful SHAVE cores with intelligent memory makes
Myriad X a leader in the use of deep neural networks and computer vision software.
Google Edge Coral Machine Accelerator - Edge TPU ASIC developed by Google.
Provides high performance machine learning (ML) for TensorFlow Lite models.
The third level is the extension of the described two-tier platform for robotics tasks
and provides both laboratory work in an appropriate course and opportunities for
research in the fields of robotics, artificial intelligence and the Internet of Things. The
Robot Operating System (ROS/ROS2) open source software framework is a software
framework that provides a suite of libraries of software and tools for programming
robotic systems of varying complexity and contains a wide range of applications,
from drivers to state-of-the-art algorithms and powerful developer tools.
In addition to software, this level involves the use of various actuators and sensors,
which are typical in robotics tasks. One of the benefits of ROS is the ability to both
pre-simulate robotic systems in a Gazebo virtual environment and run on real
hardware.
3 From Cloud to Edge/Fog/AIoT
Modern artificial intelligence and IoT technologies are closely linked and
complementary. The classic topology of IoT solutions involves the analytical
processing of information on the Cloud side and, by analysing data obtained from
many sources of information, produces a synergistic effect of Artificial Intelligence
on the Internet of Things (AIoT).
The downside, as mentioned above, is today's cloud-based data analytics.
Collecting huge amounts of data from thousands or millions of peripherals requires
not only the continued growth of Cloud computing resources, but also high-speed and
broadband communication channels. Since the performance of the neural networks in
machine learning tasks depends not least on the quality of the data received, it also
causes an increase in data transmission.
One way to solve this problem is to deploy artificial intelligence at the earliest
stages of information transfer, using the concept of moving from Cloud computing to
Fog computing [9].
In other words, Edge devices become "smart" and capable of performing a
wide range of typical artificial intelligence tasks, including machine learning (ML)
and deep learning (DL), on their own high-performance neural network-oriented
hardware, without sending them to the Cloud. Results are no longer sending a raw
data to the Cloud, but rather as structured metadata. For example, instead of
continuous high-definition video streaming, only the results of detected events or
objects with the appropriate description are sent to the Cloud, and this process is not
continuous, but only as certain changes in the observed space are detected, ie from
time to time.
Obviously, this approach can significantly reduce the requirements for data
channels, cloud-computing resources and provides a number of additional benefits,
such as the ability to improve AIoT peripherals without making changes to the system
architecture as a whole. At the same time, the released cloud resources will allow
more efficient processing of structured metadata and decision-making. Because
AIoT/Edge/Fog technologies are in active development and development, there are
different approaches both to architecture as a whole and to sectioning into separate
logical and structural layers. One of the most appropriate is to take the conditional
section vertically from the peripherals through the "smart fog" (Fog) to the cloud
level. Other authors find this approach somewhat simplistic and use two-dimensional
views with detail and a complex interconnection system.
According to Mukherjee et al [10], Aazam and Huh [11] and Muntjir and others
[12], the Edge/Fog architecture of computation consists of six layers:
1. Physical and virtualization: virtual sensors and their virtual network; physical
sensors, devices, their wireless network;
2. Monitoring: activity, capacity, resources, reactions (responses) and services;
3. Pre-processing of data: analysis, filtering, reconstruction and restoration,
cleaning;
4. Temporary storage: data dissemination, replication and de-duplication;
virtualization of storage space and devices (NAS, FC, ISCSI, etc.);
5. Security: encoding / decoding, privacy, integrity;
6. Transportation: Uploading prepared and secure data to cloud services.The
physical and virtualization layer includes different types of nodes such as physical
nodes, virtual nodes, and virtual sensor networks. These units are managed and
maintained according to their types and maintenance requirements.
The monitoring level monitors the use of resources, the presence of sensors, and the
monitoring of Edge / Fog nodes and network elements. It keeps track of all the tasks
performed by the nodes in a given layer, monitoring how energy consumption and
computing time are monitored, which node performs what task, at what time, and
what it will require in the next moment.
The performance and status of all applications and services deployed in the
infrastructure depends largely on the effectiveness of the monitoring. The pre-
processing layer performs the task of managing the data. The data collected is
analysed, filtered and cleared of unnecessary information and noise. Pre-processed
data is stored at the temporary storage level. Once the prepared data is transferred to
the cloud, it no longer needs to be stored locally and can be removed from temporary
storage. At the security level, encryption / decryption of data takes effect. In addition,
data integrity prevents unauthorized interference and measures are in place to protect
data from tampering.
Finally, in the transport layer, the pre-processed data is loaded into the cloud for
further analysis. Only a fraction of the data collected is downloaded to the cloud for
efficient energy use. In other words, a gateway device that connects the IoT to the
cloud processes the data before sending it to the cloud. This type of gateway is also
called the smart gateway.
Data collected from network sensors and IoT devices is transmitted through smart
gateways to the cloud. The resulting data is stored and used by the cloud to create
relevant services and services for users. Given the resource constraints,
communication protocols for Edge/Fog computing should be efficient and easy to set
up. The use of a microservice approach with remote data preprocessing (at edge level)
can significantly reduce the use of computing and time resources, as well as avoid
centralized storage of the entire data set, which brings the processing of data much
closer to online mode.
Next level of scalability is thinking about Fog/Edge computing from robotics
point of view. As an example of mobile IoT scenarios, in robotic deployments,
computationally intensive tasks such as run time mapping may be performed on peer
robots or smart gateways. Most of these computational tasks involve running
optimization algorithms inside compute nodes at run time and taking rapid decisions
based on results. In [13], authors incorporate optimization libraries within the Robot
Operating System (ROS) deployed on robotic sensor-actuators. Using the ROS based
simulation environment Gazebo, they demonstrate case-study scenarios for runtime
optimization. Instead of Intel Movidius with Raspberry Pi existing other similar
solutions. For example, in [14] a framework of such an edge computing system is
presented for robotic applications. The system consists of a recent machine learning
platform (Jetson TX2) integrated within a heterogeneous robotic environment of
UAVs and mobile robots operated through robot operating system (ROS).
4 Implementation of the concept of tripled learning
Vertically oriented, architecture is presented in Fig. 3, proposed in [14], which
describes Edge / Fog / Cloud in terms of distributed computing of the Internet of
Things.
Thus, the use of a microservice approach with remote preprocessing (at edge
level) can significantly reduce the use of computing and time resources, as well as to
avoid centralized storage of the entire data set, which brings the processing of data
much closer to online mode.
General architecture of modern Edge/Fog/Cloud can be represented [13] as in
Fig. 2.
Fig. 2. The horizontal edge-fog-cloud architecture
Vertically oriented, architecture is presented in Fig. 3, proposed in [14], which
describes Edge / Fog / Cloud in terms of distributed computing of the Internet of
Things.
Thus, the use of a microservice approach with remote preprocessing (at edge level)
can significantly reduce the use of computing and time resources, as well as to avoid
centralized storage of the entire data set, which brings the processing of data much
closer to online mode.
Modern civilization is constantly on the lookout for effective ways of learning,
particularly in the face of rapid progress. In addition to traditional or "classic"
methods, such as lectures and workshops, there is also an online version of it as e-
learning and blended learning. In [6, 15], the term blended learning was introduced
for the first time, or triple learning as a combination of three different complementary
approaches:
1. Offline or "traditional" training that includes lectures, hands-on and / or lab work
according to the "classic" approach.
2. Self-study online on one or more pre-selected free online course (MOOC) courses,
selected by the lecturer according to the subject matter.
3. Team work on own projects with the possibility of involving third-party mentors
and experts for evaluation.
All three components are strongly interrelated and run concurrently within a
specific training course. According to the proposed structure of triple learning, which
is already practiced in separate courses, third-party independent experts noted the
increase in the effectiveness of training and awakening of creative potential among
students. For three years, we have successfully implemented the proposed triple
training as part of the Algorithm and Programming course. Part One ”for first year
students of the Artificial Intelligence Specialty.
Fig. 3. Vertical edge-fog-cloud architecture
This course is designed to learn the basics of C/C++ programming with examples
of using different algorithms. The basics of different basic concepts and concepts of
IT, such as operating systems, networks, Internet, client-server technologies, elements
of modern web development, project management process, Cloud computing, Internet
of Things, artificial intelligence, robotics, etc., are also presented in an overview. The
focus is on learning C/C ++ programming.
As a second component, for self-study, expert analysis of a number of different
online courses was conducted to select the best one according to the subject of this
course. The result is the internationally recognized online course "Introduction to
Computer Science. CS50" from Harvard University.
The third part is teamwork on different projects during the semester. As a result,
for three consecutive years, more than 120 first-year students are consistently formed
from 30 to 40 teams working together on self-selected topics, drafting projects during
the semester under the guidance of a lecturer, presenting them at an interim defence
and completing a presentation with an independent panel of experts invited from
various IT companies.
This practice made it possible to extend the concept of tripled learning to the next
academic years as well. Current projects provide opportunities for development in
subsequent courses and mentoring support from undergraduates. The next is the
introduction of a similar approach to the level of training in the training courses in
cloud technology, the Internet of Things, robotics with a common unifying approach
to machine learning within artificial intelligence.
The proposed robotic platform provides the technical capabilities for a quality
implementation of the concept of tripled learning in a crosscutting perspective, that is,
with the gradual, permanent development and development of projects from simpler
to more sophisticated as well as the continuation of student projects to the level of
research.
Thus, in the first year students use a ready-made robotic system with the specified
encoders and capacities for programming a narrow class of tasks - movement along
the trajectory, exit from the maze and so on. In the second year, depending on the type
of project, students add additional sensors, and form a front-end and back-end part to
control the robotics part. In the third year, students can add to the specified Raspberry
Pi 3/4 platforms with camera and OpenCV / OpenVINO while using the Myriad ™ X
VPU with a trained neural network. An example of this project is a mobile robotics
platform for detecting the movement of an object indoors. In addition, in the fourth
year, students take the AIoT part while studying the Cloud Computing and
Introduction to Robotics courses.
5 Conclusions
The article describes a three-level training robotics platform for use in the educational
process when performing laboratory work, developing course projects and in the
research process.
Significantly new in the work is the creation of a universal modular solution with
complementary components, the ability to apply at different levels of qualification
and modify according to current needs.
At the same time, the proposed platform is an organic part of the modern spiral
of development of information processes and systems and allows improving the
quality of the educational process, to increase its orientation to practical
experience and to reflect the current state of AI synergy through
Edge/Fog/Cloud with IoT.
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