This paper studies the design and implementation of an interactive real-time cloud supervisory control and data acquisition (SCADA) platform. The platform relying on C# and client/server architecture provides full support for data supervision of the cloud control system (CCS). Users are allowed to design supervisory interfaces by dynamically creating and customizing virtual instruments, which are seamlessly integrated into the platform by reconstructing it. Both the scalar and matrix data from different cloud nodes are supported for supervising simultaneously in real-time through receiving data asynchronously. The user can tune the parameters of the CCS online via duplex channels based on the transmission control protocol/internet protocol (IP). To overcome the disturbance of network delays to data display, a stable data and real-time data communication scheme are proposed. All the supervised data can be stored in separate files for further analysis. Finally, the online simulation and experiment are provided to demonstrate the feasibility of the designed SCADA platform.

References

References
1.
Wang
,
L.
, and
Ranjan
,
R.
,
2015
, “
Processing Distributed Internet of Things Data in Clouds
,”
IEEE Cloud Comput.
,
2
(
1
), pp.
76
80
.
2.
Atzori
,
L.
,
Iera
,
A.
, and
Morabito
,
G.
,
2010
, “
The Internet of Things: A Survey
,”
Comput. Networks
,
1
(
15
), pp.
2787
2805
.
3.
Zhou
,
Y.
,
Zhang
,
D.
, and
Xiong
,
N.
,
2017
, “
Post-Cloud Computing Paradigms: A Survey and Comparison
,”
Tsinghua Sci. Technol.
,
22
(
6
), pp.
714
732
.
4.
Xia
,
Y.
,
2012
, “
From Networked Control Systems to Cloud Control Systems
,”
Control Conference
, Hefei, China, July 25–27, pp.
5878
5883
.https://ieeexplore.ieee.org/document/6390971
5.
Xia
,
Y.
,
2015
, “
Cloud Control Systems
,”
IEEE/CAA J. Autom. Sin.
,
2
(
2
), pp.
134
142
.
6.
Xia
,
Y.
,
Qin
,
Y.
,
Zhai
,
D. H.
, and
Chai
,
S.
,
2016
, “
Further Results on Cloud Control Systems
,”
Sci. China Inf. Sci.
,
59
(
7
), pp.
1
5
.
7.
Ma
,
L.
,
Xia
,
Y.
,
Ali
,
Y.
, and
Zhan
,
Y.
,
2017
, “
Engineering Problems in Initial Phase of Cloud Control System
,”
36th Chinese Control Conference
, Dalian, China, July 26–28, pp.
7892
7896
.
8.
Kamalapurkar
,
R.
,
Fischer
,
N.
,
Obuz
,
S.
, and
Dixon
,
W. E.
,
2016
, “
Time-Varying Input and State Delay Compensation for Uncertain Nonlinear Systems
,”
IEEE Trans. Autom. Control
,
61
(
3
), pp.
834
839
.
9.
Zhang
,
X. M.
,
Han
,
Q. L.
, and
Yu
,
X.
,
2016
, “
Survey on Recent Advances in Networked Control Systems
,”
IEEE Trans. Ind. Inf.
,
12
(
5
), pp.
1740
1752
.
10.
Li
,
Y.
,
Tan
,
C.
, and
Liu
,
G.
,
2016
, “
Output Consensus of Networked Multi-Agent Systems With Time-Delay Compensation Scheme
,”
J. Franklin Inst.
,
353
(
4
), pp.
917
935
.
11.
Pang
,
Z.
,
Liu
,
G.
,
Zhou
,
D.
, and
Sun
,
D.
,
2017
, “
Data-Driven Control With Input Design-Based Data Dropout Compensation for Networked Nonlinear Systems
,”
IEEE Trans. Control Syst. Technol.
,
25
(
2
), pp.
628
636
.
12.
Albattat
,
A.
,
Gruenwald
,
B.
, and
Yucelen
,
T.
,
2017
, “
Design and Analysis of Adaptive Control Systems Over Wireless Networks
,”
ASME J. Dyn. Syst., Meas., Control
,
139
(
7
), p.
074501
.
13.
Ward
,
J. S.
, and
Barker
,
A.
,
2014
, “
Observing the Clouds: A Survey and Taxonomy of Cloud Monitoring
,”
J. Cloud Comput.
,
3
(
1
), pp.
1
30
.
14.
Aceto
,
G.
,
Botta
,
A.
,
Donato
,
W. D.
, and
Pescapè
,
A.
,
2013
, “
Cloud Monitoring: A Survey
,”
Comput. Networks
,
57
(
9
), pp.
2093
2115
.
15.
Abrahao
,
S.
, and
Insfran
,
E.
,
2017
, “
Models@runtime for Monitoring Cloud Services in Google App Engine
,”
IEEE World Congress on Services
, Honolulu, HI, June 25–30, pp.
30
35
.
16.
Zou
,
D.
,
Zhang
,
W.
,
Qiang
,
W.
,
Xiang
,
G.
,
Yang
,
L. T.
,
Jin
,
H.
, and
Hu
,
K.
,
2013
, “
Design and Implementation of a Trusted Monitoring Framework for Cloud Platforms
,”
Future Gener. Comput. Syst.
,
29
(
8
), pp.
2092
2102
.
17.
Dong
,
B.
,
Lee
,
C.
,
Chiu
,
Y.
, and
Huang
,
Y.
,
2017
, “
An Intelligent Embedded Cloud Monitoring System Design
,”
International Automatic Control Conference
(
CACS
), Pingtung, Taiwan, Nov. 12–15, pp.
1
4
.
18.
Abawajy
,
J. H.
, and
Hassan
,
M. M.
,
2017
, “
Federated Internet of Things and Cloud Computing Pervasive Patient Health Monitoring System
,”
IEEE Commun. Mag.
,
55
(
1
), pp.
48
53
.
19.
Zhang
,
W.
,
Tomizuka
,
M.
, and
Byl
,
N.
,
2016
, “
A Wireless Human Motion Monitoring System for Smart Rehabilitation
,”
ASME J. Dyn. Syst., Meas., Control
,
138
(
11
), p.
111004
.
20.
Adissi
,
M. O.
,
Filho
,
A. C. L.
,
Gomes
,
R. D.
,
Silva
,
D. M. G. B.
, and
Belo
,
F. A.
,
2017
, “
Implementation and Deployment of an Intelligent Industrial Wireless System for Induction Motor Monitoring
,”
ASME J. Dyn. Syst., Meas., Control
,
139
(
12
), p.
124502
.
21.
Tautz-Weinert
,
J.
, and
Watson
,
S. J.
,
2017
, “
Using SCADA Data for Wind Turbine Condition Monitoring—A Review
,”
IET Renewable Power Gener.
,
11
(
4
), pp.
382
394
.
22.
Pingali
,
S.
,
2017
, “
Cloud Computing and Crowdsourcing for Monitoring Lakes in Developing Countries
,”
IEEE
International Conference on Cloud Computing in Emerging Markets
,
Bangalore, India
,
Oct. 19–21
, pp.
161
163
.
23.
Roopaei
,
M.
,
Rad
,
P.
, and
Choo
,
K. K. R.
,
2017
, “
Cloud of Things in Smart Agriculture: Intelligent Irrigation Monitoring by Thermal Imaging
,”
IEEE Cloud Comput.
,
4
(
1
), pp.
10
15
.
24.
Chen
,
Q.
,
Ahmed
,
Q.
,
Rizzoni
,
G.
, and
Qiu
,
M.
,
2016
, “
Design and Evaluation of Model-Based Health Monitoring Scheme for Automated Manual Transmission
,”
ASME J. Dyn. Syst., Meas., Control
,
138
(
10
), p.
101011
.
25.
Wang
,
J.
, and
Zhang
,
Y.
,
2013
, “
Monitoring System of Machine Tools Based on the Intouch
,”
International Conference on Mechanical and Automation Engineering
,
Jiujang, China
,
July 21–23
, pp.
70
72
.
26.
Yangang
,
X.
,
Han
,
W.
,
Xingqi
,
L.
, and
Qiang
,
H.
,
2010
, “
Monitor System Design for Machine Electric Spindle Based on MCGS
,”
J. Networks
,
5
(
12
), pp.
248
252
.
27.
Toru
,
N.
,
Yasuhiro
,
K.
, and
Ahmed
,
K.
,
2018
, “
Consensus-Based Cooperative Formation Control for Multiquadcopter System With Unidirectional Network Connections
,”
ASME J. Dyn. Syst., Meas., Control
,
140
(
4
), p.
044502
.
28.
Liu
,
G.
, and
Zhang
,
S.
,
2018
, “
A Survey on Formation Control of Small Satellites
,”
Proc. IEEE
,
106
(
3
), pp.
440
457
.
29.
An
,
B. R.
, and
Liu
,
G. P.
,
2012
, “
Networked Real-Time Controller Based on PC/104
,”
Intelligent Control and Automation
, Beijing, China, July 6–8, pp.
831
834
.
30.
Tan
,
H.
,
Wu
,
M.
, and
Huang
,
Z.
,
2016
, “
Coordinated Control for Multi-Agent Systems Based on Networked Predictive Control Schemes
,”
Control and Decision Conference
, Yinchuan, China, May 28–30, pp.
2530
2535
.
31.
Pang
,
Z.
, and
Liu
,
G.
,
2010
, “
Model-Based Recursive Networked Predictive Control
,”
IEEE
International Conference on Systems Man and Cybernetics
, Istanbul, Turkey, Oct. 10–13, pp.
1665
1670
.
You do not currently have access to this content.