A Secure Interoperable API Wrapper Tunnel for Integration of GIS-

Based ICS-IIoT and Digital Twins in Industry 4.0 Clouds

HASAN TARIQ

, SHAFAQ SULTAN

Department of Electrical Engineering, College of Engineering,

Qatar University, Doha, QATAR

Faculty Of Education, Allama Iqbal Open University

Islamabad, PAKISTAN

Abstract: - Industry 4.0 is the most disruptive revolution engulfing all the digital transformation from sensors

nodes to cloud applications. A sustainable and secure Industry 4.0 ecosystem is a core challenge for stakeholders

and state agencies addressed in this work by layered API tunnel interoperability instead of conventional

cybersecurity methods and techniques. In this work, an interoperable application programmer interface (API)

wrapper is being proposed to manage heterogeneous cloud architectures using Eucalyptus, Apache CloudStack,

and OpenNebula. A comprehensive design and implementation plan from things layer to user interface has been

accomplished in this work. Infrastructure as a Service (IaaS) is being automated using Ansible from Web Servers

Layers through Common Gateway Interface (CGI) scripting. The Web Servers are being managed using

WebSockets by micro–Web Servers Gateway Interface (uWSGI) from cloudstack managers (CSM) API. The

Platform as a Service (PaaS) for big data, web servers, and machine learning platforms is accomplished using

uWSGI from CSM. The public and private clouds are being managed using individual APIs. The wrapper,

proposed in this work enables the big challenge of Industry 4.0 for realizing interoperability, scalability,

heterogeneity, and automated cloud integration by functioning at the Software as a Service (SaaS) Layer.

Key-Words: - Industry 4.0, GIS, Digital Twins, CloudStack, IIoT, SCADA, uWSGI, IaaS, PaaS, SaaS, IoT, and

Wrapper

Received: July 26, 2021. Revised: February 22, 2022. Accepted: March 27, 2022. Published: April 21, 2022.

1 Introduction

In secure urban scale, cloud managed services [1]

and applications interoperability, scalability,

heterogeneity, and automated cloud integration have

always been a dreamed opulence. Urban applications

[2] and processes challenges resulted in many

systems, architectures, platforms, frameworks, and

mechanisms. The canvas of environmental

monitoring, infrastructure resilience, energy

optimization, transportation routing, and health

ubiquity can only be addressable by the Internet of

Everything (IoE) in smart city level tasks [2-4].

By the start of 2020, the number of connected

machines is estimated to reach more than 34 billion

[3-4] more than fourfolds of the entire global

population. The journey [5] from the open geospatial

consortium (OGC) to InfraGML has a major gap in

sensor to geo-dashboard interoperability. The work

SHM-UCM implementation in [6] solved the

interoperability problem by manual scripting that

overcasts the manpower and technical resource in

urban scale implementation by bulking the laborious

jobs. Furthermore, the convergence [6-8] of

application layer protocols MQTT, XMPP, CoAP,

and AMQP with device libraries and information

exchange template formats like JSON and XML

again needs file-level parsing to assist tools like

Ansible and Jenkin for network automation.

Different simulation models and implementations

were proposed over time [9]. Taking into account of

UrbanSense to SEMSim hyper-converged

implementation a plethora of gaps and peculiarities

can be observed like information-centered gateways

automation from sensors to application front-end and

interoperability of the UrbanSense platform and

SEMSim clouds [10, 11]. A micro-Web Server

gateway interface (uWSGI) is needed that can

automate the Infrastructure as a Service (IaaS)

skeleton for network and REST API milestones using

WebSockets [12]. The massive data produced in

urban scale cloud operations [13] needs to be

managed for creating, reading, updating, and deleting

(CRUD) purposes in Big Data interfacing with

platforms such as Hadoop Distributed File System

(HDFS) [13-15]. The work in [16] proposed

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geospatial intelligence enablement using machine

learning that needs real-time sensor data storage

optimization and access for machine learning. In [17,

18], Software as a Service (SaaS) applications’

automation needs IaaS access control to assist big

data and machine learning capabilities. The public

cloud tools for Azure, AWS and any third party need

APIs interoperability, and the same stands also for

open-source private clouds like Kaa, Kubernetes, and

OpenStack [19-22]. An API processing wrapper is

needed that can facilitate inter-cloud APIs to work

while controlling the uWSGI and CGI at the same

time [21, 23]. This is proposed, as a novel

contribution, to this work as an interoperability API

wrapper (IAW). The heterogeneity enablement using

a single interoperable API wrapper is the proposed

solution.

2 Problem Formulation

The connection of people, processes, systems,

frameworks, and platforms is a slugfest task. Moving

from instrument to SaaS requires in-depth integration

wrappers programming [24]. This work has four

module structures that map the functionality and

applications in one layout. The topmost layer

Business as a Service(BaaS) interacts with Thing as

a Service using IAW.

Fig 1. IAW Model Architecture Bridges TaaS-BaaS Functional Gap

Fig. 1 presents the complete concept and

implementation block diagram enabling a BaaS-TaaS

interaction. The addition of only 4 interface layers

can perfectly map Industry 4.0 and BaaS-TaaS

workflow architecture and frameworks with OSI

architecture from software, hardware, and

communication prospect. Following are the four

layers of this work as a single tunnel:

A. Level 0 Things-IaaS API Wrapper

B. Level 1 IaaS-PaaS API Wrapper

C. Level 2 PaaS-SaaS API Wrapper

D. Level 3 TaaS-BaaS API Wrapper

This work enables the integration of the OSI

model and the IoT standard architecture and

framework by filling the fundamental gaps. The cost,

effort, and time can be minimized by optimizing the

OSI skeleton mapping over the Industry 4.0 skeleton

as a one-turnkey solution.

3 Problem Solution

The gaps in the problem definition presented in fig 1

were addressed using a four-layer interoperable API

wrapper (IAW) that can be integrated and deal with

the physical cloud infrastructure as well as a PiL-

MiL/HiL-SiL unified Digital Twin architecture. The

effort and time can be minimized by optimizing the

OSI skeleton mapping over the Industry 4.0 skeleton

as a one-turnkey solution.

3.1 Level 0 Things-IaaS API Wrapper

Step 0, the ICS network architecture realization

consists of N

ICS

sensor/actuator nodes with Industrial

Communication Network Bus (B

ICN

) with a topology

ICS

as standard ICS and for a specific Process

Control System (PCS) as T

ICS-PCS

, Manufacturing

Execution System (MES) as T

ICS-MES

, Batch

Processing Systems (BPS) as T

ICS-BPS

. Let's consider

an ICS-IIoT system with N number of PLCs

connected in topology ICS

IIoT-PLCs

with gateway

ICS

IIoT-Gateway

having X interfaces for GIS-SCADA

Server S

IIoT

for ICS cloud C

ICS-IIoT

. The ICS-IIoT

integration in a GIS Digital Twin is a system and

when joined with an interoperability chain of

Software in Loop (SiL), Hardware in Loop (HiL),

Process in Loop (PiL) and Model in Loop (MiL)

workflow. The maximum number of IIoT nodes

(PLCs, VFDs, HMIs, etc) for a GIS-based ICS-IIoT

in a ICS

IIoT-Gateway

concerning the maximum number

of packets ICS

Packets

, for a given gateway, are given

as follows

ICS

Packets

∑(



(I)) +

∑



}

(1)

where ΔG

ICS-IIoT

is the change in the geospatial patch

of ICS system deployment.

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3.2 Level 1: IaaS-PaaS API Wrapper

The Things-IaaS has to be added with network

and web-scripting capabilities like JhonnyFive (i.e.

JS), Bash, PowerShell, and Python. Furthermore,

Ansible is a basic open-source IT infrastructure

automation tool that allows easy application

deployment, intra-service orchestration, cloud

provisioning, etc.

The round-trip time (RRT) for JSON and XML based

data transfer for a given DevOps deployment YML

playbook be T

ICS-YML-RRT

for a single variable for any

sensor/actuator in PLC node N

PLC

= {N

PLC1

, N

PLC2

…N

PLCN

} for a specific GIS-based ICS sitemap base

is the difference between the time to send (TTS) and

application constraint channel tunnel allocation time

YML-Ch-Alloc

at a given geographical area under G

ICS-

IIoT-Obs

observation as:

ICS-YML-RRT

YML-Ch-Alloc

ICS-IIoT-Obs

)-T

YML-

TTS

(ΔG

ICS-IIoT

) (2)

In B

ICN

, every packet from for a unit actuator or

sensor connected to PLC for a specific C

ICS-IIoT

data

loop for PiL will be based on the equation (2) and

vary for different T

YML-Ch-Alloc

3.2 Level 2: PaaS-IaaS API Wrapper

The interaction of Keras-Tensorflow using Docker,

MapReduce-Hadoop using VM at WSGI is possible

by a python API. This API would be using the

pyvmomi library [13] with ESXi Server or VSphere.

Furthermore, pyvmomi reacts with PyDoop to

interact with MapReduce to optimize and control

Hadoop operations. To allow the Cloud to interact

with Sensors we propose to use a Python-PHP-

WebSockets API. The architecture model is given in

Fig. 2. In Fig. 2, a complete python-focused RPC-

API implementation is displayed that uses RPCs to

handle cloud APIs using python CloudMesh. ARISTA

eAPI connects uWSGI with vSphere and ESXi clients

assisted by PyDoop can be used to interact with

Hadoop. Secondly, gunicorn could be used with

Connexion to make the best out of Tensorflow and

Keras deep learning interfaces inside Docker (an

Enterprise Application Container Platform). PyOne

handles XML-RPC API and Python JSON-RPC API

handles JSON and routes XML and JSON through a

REST API in the overall uWSGI architecture. Three

key nodes with bottlenecks exist in the automated

ICS infrastructure: a) A CGI, the bottleneck in B

ICN

between the PLCs, RTUs, and SCADA; b) uWSGI

bottleneck between Docker and the VMs for Digital

Twins to emulate the entire infrastructure; c) ARISTA

eAPI core, the bottleneck between uWSGI and

CloudStacks. This created a situation where the entire

throughput TP

ICS-IIoT

became dependent on the

ARISTA eAPI core and uWSGI. For a given G

ICS-IIoT-

Obs

, there must a exist a minimum value for T

ICS-YML-

RRT

for the sum of all allocated channels under T

YML-

Ch-Alloc

as well as their effective areas for a set of

unique interfaces I = {I

, I

, …I

} given as:

ICS-IIoT

{Ʃ 



()  Ʃ



()}





(3)

Higher the number of dynamically allocated T

ICS-

YML-RRT

less will the throughput of PiL-MiL chain in

ICS and its digital twin respectively.

3.3 Level 3: TaaS-BaaS API Wrapper

The user-centered automated operations and

processes are the conscience of Industry 4.0 based

architectures. The maximum throughput of ICS

ICS-IIoT

can be calculated from PiL-MiL chain for

ICS-YML-RRT

for all the gateway interfaces termed as

the gateway mesh for a specific YML in the current

state of the ICS Cloud. Based on ICS-YML and the

RPC-APIs the total channel capacities based on the

Gateway-YML-Ch-Alloc

data rate size divided by the sum of

channel-specific round time trips are given as:

ICS-IIoT

∑



()





∑



()



(4)

Precisely it is based on giving access to users to

control the instrumentation below through parametric

flexibility to achieve the production goals [25].

Fig 2.

IAW Implementation for Industry 4.0 based ICS-IIoT Cloud

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In fig 2, a clear formulation has been shown that

enables a user to interact from a user interface (UI)

for the price paid to have a respective user experience

(UX). Furthermore, it can be elaborated as:

1. Any premium user will have cloud-level

business services supported by machine

learning and big data regardless of the cloud

being used.

2. Any enterprise user will have the webserver

portal to invest in his needed platforms to

complete the task in a cloud he/she is part of.

3. Any basic user will have mobile apps or web

clients to only interact with the factory

instrumentation underneath.

4. The ICS-IIoT is always a multiple input and

multiple output systems in a PLC/DCS and

SCADA systems for Digital Twin

integration it must be a super-set of the

model predict controllers (MPCs) as MIMO-

MPCs for KPIs control using PiL/MiL

integration presented in fig 3.

Fig 3. PiL/MiL based Integration of ICS-IIoT in Public and Private

Cloud Digital Twins

In fig 3, it can be observed that at the factory/plant

level a private cloud-based Digital Twin with local

MiL/PiL in MATLAB-ANSYS and a public cloud-

based Digital Twin with public MiL/PiL in any of

Microsoft Azure or AWS will run continuously.

4 A Case Study of Smart Factories

Cluster for Beverages Production using

ICS-IIoT

The ICS-IIoT for the industrial internet of things

(IIoT) gateway developed in our past work [26] has

been capitalized as a case study.

4.1 Problem Statement

Supervisory Control and Data Acquisition (SCADA)

systems are Industry 4.0 compliance production

automation and integration systems that use IIoT.

With the ever-varying production requirement and

technological upgrades in the Beverages production

ecosystem due to innovation in process analytical

technology (PAT), 70% of the factories have shared

facilities. The challenge in these shared facilities is

the remote configuration and pre-production trial due

to multi-OEM ISA equipment based on IEC

61131/61499 (PLC/DCS) for ANSI/ISA-88. The

mega inter-factory production flow and inter-OEM

technology interoperability challenges cost millions

of dollars and that raises the costs and production

time of ISO 9001 certified standard. Furthermore, the

production technologists have to back up and test the

automation codes for a single PLC 30% of the time.

For multiple PLCs, this problem multiplies in

magnitude.

4.2 Problem Statement

Capitalizing the proposed research, a python-based

GIS YML to address this interoperability

heterogeneity the implementation process is

explained below:

1. The SCADA-IIoT

LOC

{(Lat1, Lon1), (Lat2, Lon2)}

class calls one constructor for every

Production

Schema

and created a 2D plane with a

spatial diagram.

2. The production effective Basemap instance for

production Production

BaseMap-Schema

is a vector file

that was created for maximum outer boundary

values of {(Lat1, Lon1), (Lat2, Lon2)}.

3. For every production application

Production

Application

, the Production

Schema

defined

production infrastructure simulation will be

performed for pre-production for

control/mathematical models over the relevant

PLC catalog numbers for 3 PLCs in three different

smart factories: a) PLC1 at factory 1 (F1), PLC2 at

factory 2 (F2), and PLC3 at factory 3 (F3).

4. The pre-trial of production was simulated in

SCADA-IIoT Server and then an ANSI/ISA-88

compliance online models in loop (MiL)

simulation was performed using IAWT ICS-IIoT

with GIS Playbook Clustering.

5. The MiL quality assurance was verified for FDA

CFR-21 as G

CSMI

instance; for a set of unique

Production AI libraries Lib

Production-AI

={Lib

Production-

AI-1

, Lib

Production-AI-2

, … Lib

Production-AI-N

} the test

Unit

Production-Test

for performed as per figure 4

comprehended V-Model.

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Fig 4. Smart Factories Production System Infrastructure for Geo-

Spatial Production Application Implemented using IAWT ICS-IIoT

GIS in Python

6. After the validation of the working

configuration and end-to-end the IAWT ICS-

IIoT for FDA CFR 21 in F1, F2, and F3, the

GIS Production

Playbook

(SCADA-IIoT

LOC

CSMI

, A

LBS

, Gateway ID, IP, MAC) was

created for every production gateway node

connected to the production schema relevant

PLCs.

7. As a fail-safe GIS cluster, a Production

IAWT ICS-IIoT MiL docker was packaged

and transferred to every EAI-IIoT

Gateway

for

Production

Application

Fig 5. IAWT ICS-IIoT MiL Docker Packaged as BackUp

8. After the OTA by the backup of the last

working firmware and configuration of the

PLCs the EAI-IIoT

Gateway

, existing QoS was

recorded by using python library PyPing a

python command sent a ping after ever

Ack_Flag=TRUE to pool the production

topology.

9. In IAWT ICS-IIoT MiL Docker, the pings

were stored as real-time UNIX clock format

variables using SciPy and statistically

processed as YML playbook with arguments

as (Production

Packet

Production

BaseMap-Schema

EAI-IIoT

Gateway

Data, Production Topology,

Acknowledgement Flag) at UNIX time t.

10. The Plotly data frame is formed by mapping

Production

Topology

over Production

BaseMap-

Schema

11. The arrow colors and dots are shown in Figs.

3 and 4 are plotted as it is with the same

semantic descriptions in the final GNA

results from ours.

12. The arrow colors and dots are shown in Figs.

3 and 4 are plotted as it is with the same

semantic descriptions in the final GNA

result.

These 12 operations ensured the successful

implementation of IAWT ICS-IIoT and deployment

of the GIS Playbook over the Edge AI IIoT Gateway.

4 Results and Discussion

The systematic stepwise implementation of the 12

IAWT ICS-IIoT MiL based on procedures presented

in Sections 2 and 3 enabled the QoS results. Steps 1

and 2 mapped created geospatial charts displayed in

figure 6 given below.

The results followed the proposed methodology

steps sequence given in section 2 for regional

computations. The second step was virtual circuit

socket creation and start transmission and

establishing a link between HQ SCADA-IIoT Server

and IIoT Gateway and Factories.

a. HQ at Google Earth

b. Spatial Layout c. Mapped Layout

Fig 6. IAWT ICS-IIoT Operations 1 and 2 for HQ MiL Deployment

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a. Wireshark I/O Graph b. OTA BackUp

Fig 7. ICS-IIoT GIS results for Factory Sites to Gateway, G

I/O Graph

for Wireshark I/O Graph for SCADA-IIoT

Address

In Fig. 7, the 3 PLCs backups to the IIoT gateway

and its archiving at the start of the process is

exhibited. The maximum 1535 packets/second

transmission took place from factory sites to IIoT

Gateway i.e. 49,120 bits/second as per table 2. Only

OTA backup data F1, F2, and F3 were transmitted

upward in the hierarchy. For 100 sensors/actuators at

a single PLC with 100+100 floating-point 32-bit

variables per PLC per site, theoretically segment

length needs to be stabilized to the lowest value after

the GIS has been deployed on the gateway.

a. Wireshark Segment Length Trace b. IAWT MiL

Fig 8. Reduction in Throughput after the deployment of GIS

implementation. for Factory Sites to Production

Gateway

From (5) the WiFi

MSS

achieved a segment length of

1388 bytes for 7 seconds only is less than the MTU,

which means that the Production

Playbook

was deployed

without using the maximum capacity of IIoT network

capacity. Achieving event response below MTU at

such a low data rate was the very competitive

benchmark. The less than 20 Bytes segment length

will result in lower round trip times (RTTs).

a. Wireshark Trace for GIS Round Trip Time b. G

Ping

Fig 9. RTT results for successful deployment and stable production

Operations based on ICS-IIoT GIS results for Factory Sites to G

In fig 9, the pre-settlement time between (40 to

56) ms was a better docket porting performance range

for ISA equipment based on IEC 61131/61499

(PLC/DCS) for ANSI/ISA-88.

a. Wireshark Sequence Numbers Trace b. MiL

Deployment

Fig 10. Relative Stationarity in Sequence Numbers for Factory Sites to

In Fig. 10, the stationarity of sequence numbers

exhibits consistency in acknowledgment. After

26000+ sequence numbers, the IAWT ICS-IIoT data

stream achieved maturity as reflected by a horizontal

line. The GIS events generation stopped 27000th

token at 360 seconds means not all possible V

updates and event responses are expected.

4 Conclusion

This work has enabled a secure poly-lithic API

wrapper tunnel to enable secure Industry 4.0

deployment without the usage of conventional

cybersecurity tools like SSL, TSL, OAuth, firewalls,

and encryption. Infrastructure has been secured with

the help of heterogeneity and interoperability tunnel.

The results can be summarized in 3 key findings: 1)

Systematic interoperability is the basic constraint for

dense heterogeneous installation to work

successfully using inter-sector wrappers; 2) The APIs

have to talk to each other to finish the end-to-end

chaining; 3) Global or Urban user-centered

applications must have a UI and UX flow to interact

with the lowest instrument in the Industry 4.0

entrusted business service. The cloud automation

APIs and the wrapper API constitute one of the prime

achievements that have enabled gigantic

transformations of IoTs for stocks and businesses.

5 Future Recommendations

In our current research, this effort contributed to

beverages industry, in future this research can be

improved and some possible gaps that may exist in

this research by utilizing this research as base for:

1. AI-based Digital Twins for Industry 4.0

2. AI-based Digital Twins for Computer

Integrated Manufacturing Robotic Work

Cells.

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3. GIS-Based ICS-IIoT Digital Twins with

Spatio-Temporal Topology Control of

Production Flows in Packaging in

Pharmaceuticals.

4. Geo-AI for optimization of Digital Twins in

Process in Loop and Model in Loop for

Batch Processing Systems.

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[18] Hasan Tariq, Abderrazak Abdaoui, Farid

Touati, Mohammed Abdulla E Al-Hitmi,

Damiano Crescini, and Adel Ben Mnaouer, A

Real-time Gradient Aware Multi-Variable

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Handheld Urban Scale Air Quality Mapping IoT

System. IEEE International Conference on

Design & Test of Integrated Micro & Nano-

Systems, 2020.

[19] Aydin Teymourifar, Ana Maria Rodrigues, Jose

Soeiro Ferreira, Geographically Separating

Sectors in Multi-Objective Location-Routing

Problems. WSEAS Transactions on Computers,

Volume 19, 2020, Art. #13, pp. 98-102, 2020.

[20] Hasan Tariq, Farid Touati, Mohammed A. E.

Al-Hitmi, Damiano Crescini, and Adel Ben

Mnaouer, Design and Implementation of

Cadastral Geo-spatial IoT Network Gateway

Analyzer for Urban Scale Infrastructure Health

Monitoring. 10th Annual Computing and

Communication Workshop and Conference,

2020.

[21] Stanislav Bovchaliuk, Sergii Tymchuk, Sergii

Shendryk, Vira Shendryk, The Fuzzy Control

Automation Architecture of Parallel Action for

the Intelligent Smart Grid Networks. WSEAS

Transactions on Computers, Volume 19, 2020,

Art. #3, pp. 21-25, 2020.

[22] Hasan Tariq, Abderrazak Abdaoui, Farid

Touati, Mohammad Abdullah Al Hitmi,

Damiano Crescini, and Adel Ben Mnaouer,

Real-time Gradient-Aware Indigenous AQI

Estimation IoT Platform. Advances in Science,

Technology and Engineering Systems Journal

Vol. 5, No. 6, 1666-1673, 2020.

[23] Muneer Bani Yassein, Omar Alzoubi, Saif

Rawasheh, Farah Shatnawi, Ismail Hmeidi.

Features, Challenges and Issues of Fog

Computing: A Comprehensive Review. WSEAS

Transactions on Computers, Volume 19, 2020,

Art. #12, pp. 86-97, 2020.

[24] Hasan Tariq, Abderrazak Abdaoui, Farid

Touati, Mohammed Abdulla E Al-Hitmi,

Damiano Crescini, and Adel Ben Mnaouer,

Design and Implementation of a Multi-

Parametric Geo-Seismic Realization Engine for

Programmable Mechatronic IoT Geo-

Mechanics Simulators. International Journal of

Geology, 2019.

[25] Roumen Trifonov, Slavcho Manolov, Georgi

Tsochev, and Galya Pavlova, Automation of

Cyber Security Incident Handling through

Artificial Intelligence Methods. WSEAS

Transactions on Computers, Volume 18, 2019,

Art. #35, pp. 274-280, 2020.

[26] Hasan Tariq, Abderrazak Abdaoui, Farid

Touati, Mohammed Abdulla E Al-Hitmi,

Damiano Crescini, and Adel Ben Mnaouer,

IoT/Edge Structural Health Monitoring System

as a Life-Cycle Management tool for SDG-11

using Utility Computing Platform. WSEAS

TRANSACTIONS on COMPUTERS, 2019.

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