Cyber-Physical System: Comparison
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Cyber-Physical System (CPS) is a symbol of the fourth industrial revolution (4IR) by integrating physical and computational processes which can associate with humans in various ways. In short, the relationship between Cyber networks and the physical component is known as CPS, which is assisting to incorporate the world and influencing our ordinary life significantly. In terms of practical utilization of CPS interacting abundant difficulties. Currently, CPS is involved in modern society very vastly with many uptrend perspectives. All the new technologies by using CPS are accelerating our journey of innovation. In this paper, we Researchers have explained the research areas of 14 important domains of Cyber-Physical Systems (CPS) including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. We also demonstrated the challenges and future direction of each paper of all domains. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 158 papers are studied in this survey as well as among these, 98 papers are directly studied with the 14 domains with challenges and future instruction which is the first survey paper as per the knowledge of authors. 

  • 3C
  • 5C
  • NIFU
  • Cyber Physical System
  • 14 domains of CPS

1. Introduction

The Cyber-Physical System (CPS) is the key concept of Industry 4.0, which the German government advocates for to develop smart factories and fetch in the 4th industrial revolution. When an NFS session was organized in Austin, Texas, the United States in 2006, the concept of CPS officially emerged [1]. Industry 1.0 was about mechanization and steam power, and then mass production and assembly line which was known as Industry 2.0, and digitalization and automation are Industry 3.0, and finally, Industry 4.0 is planned for the distributed engender through shared amenities in the combined global industrial structure for on-demand manufacturing to succeed personalization and resource efficiency [2]. It has far-reaching consequences for both producers and consumers. The term Industry 4.0 refers to a trend in industrial automation that incorporates some new technologies to improve worker health at work, as well as plant productivity and quality.
The smart factory approach is part of Industry 4.0 and is divided into three categories including smart production, smart services, and smart energy. From the previous statement, it is clear that energy conservation is a concern in any sort of factory. This is because the end product must be produced at a low cost while maintaining high quality. As a result, energy conservation boosts productivity and maybe creates job opportunities. The Cyber-Physical System is a major idea in Industry 4.0. [3]. CPS are advanced technologies that connect physical reality operations with computing and network infrastructure [4]. With typically integrated devices, which are supposed to function like independent devices, CPS focuses on connecting multiple devices [5]. A CPS comprises a monitoring system, generally, one or even more microcontrollers that regulate and transmit the information acquired from the sensors and actuators required to deal with the actual environment. A communication interface is also required for such embedded systems to share information with other embedded systems or the cloud. The most significant element of a CPS is information interchange, because information may be connected and analyzed centrally. A CPS, to look at it another way, is an embedded system that can communicate with other devices via a network. The Internet of things [6] is a term used to describe CPS that are hooked up to the internet. With integrated technology, the Internet of Things (IoT) will connect all the company’s elements, machinery, and Goods.
Herein, the research areas of 14 important domains of Cyber-Physical Systems (CPS) are explained, including aircraft transportation systems, battlefield surveillance, chemical production, energy, agriculture (food supply), healthcare, education, industrial automation, manufacturing, mobile devices, robotics, transportation, and vehicular. Challenges and future direction are demonstrated. Almost all articles have limitations on security, data privacy, and safety. Several projects and new dimensions are mentioned where CPS is the key integration. Consequently, the researchers and academicians will be benefited to update the CPS workspace and it will help them with more research on a specific topic of CPS. 
The common acronyms used in CPS field are tabulated in Table 1.
Table 1.
Used and known Acronym about Cyber Physical System.
Acronym Full Form Acronym Full Form
CPS Cyber Physical System NFS National Science Foundation
IOT Internet of Things IOS Internet of Services
IOD
  • Internet of Services (IoS): It includes new communication models such as those offered by service-oriented design (SOA) and REST technology
  • [
  • ]
  • .
  • Internet of Data (IoD): It allows for the efficient transmission and storage of large amounts of data, as well as the creation of innovative analytical techniques for analyzing large amounts of data [12].
WOne can also say about CPS that it is a convergence of 3C containing computation, communication, and control or 5C or NIFU.
The characteristics of CPS among 3C, 5C [1] and NIFU is discussed in Table 2.
Table 2.
Characteristics of CPS.
Name Description
3C
  • Computation in a CPS refers to the processing and analysis of data from physical and computational components to make decisions and control physical processes.
Internet of Data
OCS

4. Cyber and Physical Mapping

The goal of CPS is to provide new features to physical systems by combining computation and communication with physical processes [20]. CPS provides real-time sensing, dynamic control, and information services for complicated processes through strong collaboration between the 3Cs. CPS has placed a heavy emphasis on the cyber world’s tremendous communication and computation abilities which could also improve the physical world’s precision and efficiency [21,22][21][22]. Additionally, whether it is a three-tier, five-tier or service-oriented design all of the CPS designs described by academics concentrate on control instead of mirrored representations [23]. The tasks of CPS are enabled by mutual mapping, real-time communication, and effective collaboration between both the cyber and physical worlds. The computing system may have impacted more than one physical object. For example, a structure may consist of several hardware elements. As a result, the mapping link between the cyber and physical worlds of CPS is a one-to-many connection rather than a one-to-one.

5. Integration in CPS

CPS evolves and it becomes more advanced over time as an industrial automation system [24]. To compensate for the limitations among networks, technologies, tools, and devices, and along the value chain, an ever-increasing demand for connectivity is highly suggested [25,26][25][26]. With the new problems provided by incorporating cyber-physical systems (CPSs) into commercial applications and the issue of integration is becoming even more important for the introduction of new technologies and their acceptability by professionals when speaking of Industry 4.0 concepts [27].

Technologies in CPS

There are two popular technologies in CPS. They are stated below:
  • Communication Technology in CPS: In the digital system, CPS is linked to electronic gadgets, portable devices, and industrial instruments [28]. Communication, networking, processing of data, storage systems, and transmission power are all vary among components [29]. For example, Smartphones have the ability for networking, processing, communicating and data storing. Each of this stuff can be linked using network, communication, and software technologies. A self-configuration technology of humanized CPS algorithm employing simple binary and mathematical operations can shorten convergence time and enhance scalability [30]. The use of state-space approaches in the development of a novel connected cyber-physical system (CPS) framework and the use of feedback control to dynamically change CPS resource utilization and efficiency is discussed [31]. The oriented cuckoo search algorithm (OCS) is a new evolutionary algorithm [32]. In OCS, a mixture of two separate random distributions dominates the global search capabilities. Petri net models have been generalized for creating dynamic manufacturing techniques in order to enable traceability analysis [33]. By using a three-phase design strategy that includes cross association, sensitivity analysis, and a systematic methodology, optimal IDS design for creating intrusion detection systems (IDS) is intended at lowering the number of monitored parameters [34]. A different task may have different CPS needs, such as productivity, power efficiency, and privacy and a real-time issue is also a crucial factor [35]. For example, the efficacy of CPS depends on the ability to select a suitable existing framework for choosing work periods in real-time using predetermined priority controller jobs planned using a rate monotonous algorithm [36]. Specialized real-time location sensing (RTLS) labels provide an effective methodology to enabling bidirectional coordination between physical construction materials and their virtual models, improving real-time construction continuity, and assisting proactive strategic decision-making [37].
  • Networks Involved in CPS: Computational and physical resources are closely connected and mutually reliant in cyber-physical systems [38]
  • Communication in a CPS refers to the exchange of data between physical and computational components to facilitate control and monitoring of physical processes. This involves the use of communication networks and protocols to ensure that data is transmitted accurately and in a timely manner. Effective communication is critical in a CPS to ensure that physical processes are controlled and monitored accurately and in real-time.
  • Control in a CPS refers to the ability of the computational components to influence and manipulate physical processes. This involves the use of sensors, actuators, and other physical devices to monitor and manipulate the physical environment. The computational components of a CPS use data analysis and decision-making algorithms to determine the appropriate actions to take to achieve the desired control outcomes.
  • . It is important to ensure and enhance performance in various technology-related domains, such as manufacturing, energy, transportation, and healthcare, in order to develop and implement resilient and reliable CPS networks. WSNs, wireless networks, WLANs, cloud-based networks, social networks, and other heterogeneous networks are all used in CPS
  • [39,40][39][40]. CPS assesses the applications and technological criteria for effectively blending CPS with sensor network plane from a security perspective, as well as the techniques for transmitting information among remote monitoring locations and widely implemented sensor nodes [41]. Topology regulation by node selection could enhance data transmission performance while conserving energy and extending the network’s lifetime [42]. Wireless sensor network topology optimization is a critical topic. A topology optimization approach for wireless sensor networks based on complex network theory and cyber-physical systems is developed using software-defined wireless sensor network architecture [43]. To promote the successful integration of CPS, an Intelligent Control Box transforms diverse wireless signals, including Bluetooth, ZigBee, and RF [44]. As shown in a literature review, discusses the benefits of Web of Things (WoT) and CPS integration. A CPS for structural event observation with WSNs is presented, as well as a novel approach based on network decision in the CPS entitled MODEM [45] proposes a method for coordinating a network of suppliers in a cyber-physical system [46]. The dynamics of the agent are nonlinear, arbitrary in size, and sometimes heterogeneous. In [47], the authors examine the state prediction problem in cyber-physical systems (CPS) where a wireless sensor measures a dynamical physical process and transmits the measurements to a remote state estimator. In addition, in
5C
  • Connection in a CPS, the physical and computational components must be connected and integrated with each other to achieve a specific function. This involves establishing communication channels between sensors, actuators, and other physical devices and the computational components, such as processors and communication networks.
  • Conversion in a CPS refers to the process of converting physical data into digital data that can be processed and analyzed by computational components. For example, in an autonomous vehicle, sensors detect the physical surroundings, and the data from those sensors is converted into digital data that is processed by the onboard computer to make decisions about the vehicle’s movement.
  • The term “cyber” in CPS refers to the computational components of the system, including hardware, software, and communication networks.
  • Cognition in a CPS refers to the system’s ability to process, analyze, and make decisions based on data from physical and computational components. This involves using artificial intelligence and machine learning algorithms to analyze data and make decisions about how to control physical processes.
  • Configuration in a CPS refers to the arrangement and setup of physical and computational components to achieve a specific function. Configuration is critical in ensuring that the CPS is optimized for its intended application and operates effectively and efficiently.
Oriented Cuckoo Search
NIFU
  • A CPS is typically composed of multiple physical and computational components that need to communicate with each other to achieve the desired functionality. A network, such as a wired or wireless communication network, is used to establish communication between these components.
  • The intelligence of a CPS refers to the system’s ability to process data, learn from data, and make decisions based on data. This involves using algorithms and techniques such as artificial intelligence, machine learning, and data analytic to analyze data from physical and computational components. The intelligence of a CPS enables it to optimize physical processes, improve safety and efficiency, and enable intelligent automation.
  • The functionality of a CPS refers to its ability to perform a specific task or achieve a specific goal. The functionality of a CPS can vary widely depending on the application, and can range from simple tasks, such as adjusting temperature and lighting in a smart home, to complex tasks, such as controlling an autonomous vehicle.
  • User-friendliness in a CPS refers to the ease of use and intuitiveness of the system’s interface. This involves designing the system with the user’s needs and preferences in mind and providing an interface that is easy to navigate and understand. A user-friendly CPS can help ensure that the system is used effectively and efficiently and can help reduce the potential for errors or accidents.
 3C Computing, Communication, Control IDS Intrusion Detection Systems
RTLS Real-Time Location Sensing WoT Web of Things
NoC Network-On-Chip KF Kalman Filter
ACPS Aviation Cyber Physical System UAVs Unmanned Aerial Vehicles
CPPS Cyber Physical Production System ECPS Energy Cyber Physical System
PHEVs Plug-in Hybrid Electric Vehicles HESS Hybrid Energy Storage System
SeDS Sensor-Drone-Satellite ICT Information & Communication Technology
MDR Monitoring detecting responding CCP Collaborative Control Protocol
MCPS Medical Cyber-Physical System EHR Electronic Health Record
MPPT maximum power point tracking IASs Industrial automation systems
ICPS Industrial Cyber-Physical Systems IAS Industrial Automation and Software
CPSSs Cyber-physical product-service systems RE Requirements Engineering
PHM prognostics and health management DTs Digital Twins
TCPS Transportation Cyber-Physical Systems DEDR Dynamic En-route Decision real-time Route
CF car-following EV Electric Vehicle
ITS Intelligent Transportation Systems FC Fog Computing
SA Smart Agriculture SCSAS Smartphone based construction site safety awareness system
3C Computation, Communication, and Control 5C Connection, Conversion, Cyber, Cognition and Configuration
    NIFU Network, Intelligence, Functionality, and User friendliness)

 2. What Is Cyber-Physical System?

2. What Is Cyber-Physical System?

Cyber refers to Computing, Networking, and controlling that is distinct, switched, and logical. Physical systems are natural and human-made systems that are regulated by physics rules and executed on time [7]. A CPS is characterized as a revolutionary technology for controlling associated systems between their computation capabilities and physical assets [8]. A CPS is a network of interconnected IT components that is used to control physical such as mechanical and electronic items. The Internet is used as an information infrastructure for connectivity. A CPS is made up of two major functional elements.
  • Advanced gateways, which help to ensure real-time data obtained from the physical world as well as info responses from the cyber environment; and
  • Intellectual data processing, computer simulation, and data analysis abilities, which build the cyber.
Several technologies are intimately linked to the CPS for instance Sensor networks, IoT, wireless, and cloud computing. Wireless networks are thought to be an important part of CPS [9]. The Internet offers critical techniques for optimizing the productivity of cyber-physical systems. The following parameters are included in internet technology perspectives.
  • Internet of Things (IoT): It entails using IP addresses to communicate with smart systems. This allows any physical device to be assigned its IP address

3. Compatibility of CPS with Industry 4.0

A CPS is a highly integrated system of physical components containing sensors, actuators, and diverse equipment, along with cyber components boasting ubiquitous processing and effective communication, which is a rapidly emerging research topic. Addressing demands, challenges, and possibilities across a variety of industrial sectors might help speed CPS research. The focus is to create new systems science and engineering methodologies for designing high-confidence systems that are flexible, synergistic and interconnected at all levels. Industry expenditures in CPS technology and research have been extensive in the past and present, but have primarily concentrated on shorter-term, faster-payoff proprietary solutions. Ministries and some industries have recently made investments in lengthier, pre-competitive technologies and testbeds [13]. CPSs, which include transport systems, power systems, water/gas distribution systems, and autonomous factories, are regarded as the most potential industrial systems from an engineering standpoint. A wide range of industrial robots with an inertial navigation device or other sensors are programmed to move along a predetermined route to fulfill production tasks together [14,15][14][15]. The tight coordination of cyber and physical aspects in these systems gives higher freedom, productivity, usability, security, and flexibility. Moreover, industrial CPSs are viewed as a key component of the 4th industrial revolution [16,17][16][17] and significant efforts have been undertaken to demonstrate their significance. Industrial CPSs are massive, globally distributed, federated, collaborative, and life-critical systems with a large number of integrated sensors and actuators that are connected to provide real-time inspection and closed-loop control. Furthermore, sensor networks and distributed control networks play a significant role in the execution of industrial CPSs. In other words, in a widely deployed CPS architecture, these two networks are frequently required [18]. Sensor networks are typically implemented in the interior or surrounding plants to collect lots of important information in order to make accurate perceptions of the physical plants. Actuators may be able to respond in real-time to changes in physical plants using this information. As a result, when merging the cyber and physical worlds, a closed loop is produced by both communications between sensors and actuators and controlling plants via actuators [19]. Many industrial sensors and actuators with some communication and data processing capabilities have been made accessible in recent years, due to the efforts of research institutes and enterprises all around the world.
  • [
  • 48], the authors describe a dependable Network-On-Chip (NoC) technique that may be applied to an FPGA-based system. They have looked at the stochastic stability of Kalman filter (KF) based state estimation in geographically distributed cyber-physical systems using a lossy network.

References

  1. Jiang, J.R. An improved cyber-physical systems architecture for Industry 4.0 smart factories. Adv. Mech. Eng. 2018, 10, 1687814018784192.
  2. Cicconi, P.; Russo, A.C.; Germani, M.; Prist, M.; Pallotta, E.; Monteriu, A. Cyber-physical system integration for industry 4.0: Modelling and simulation of an induction heating process for aluminium-steel molds in footwear soles manufacturing. In Proceedings of the 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), Modena, Italy, 11–13 September 2017; pp. 1–6.
  3. Brettel, M.; Friederichsen, N.; Keller, M.; Rosenberg, M. How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. FormaMente 2017, 12, 37–44.
  4. Jazdi, N. Cyber physical systems in the context of Industry 4.0. In Proceedings of the 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, Romania, 22–24 May 2014; pp. 1–4.
  5. Darwish, A.; Hassanien, A.E. Cyber Physical Systems Design, Methodology, and Integration: The Current Status and Future Outlook. J. Ambient Intell. Humaniz. Comput. 2018, 9, 1541–1556.
  6. Pivoto, D.G.; de Almeida, L.F.; da Rosa Righi, R.; Rodrigues, J.J.; Lugli, A.B.; Alberti, A.M. Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review. J. Manuf. Syst. 2021, 58, 176–192.
  7. Czekster, R.M.; Metere, R.; Morisset, C. Incorporating Cyber Threat Intelligence into Complex Cyber-Physical Systems: A STIX Model for Active Buildings. Appl. Sci. 2022, 12, 5005.
  8. Lee, J.; Bagheri, B.; Kao, H.A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23.
  9. Kos, A.; Tomažič, S.; Salom, J.; Trifunovic, N.; Valero, M.; Milutinovic, V. New benchmarking methodology and programming model for big data processing. Int. J. Distrib. Sens. Netw. 2015, 11, 271752.
  10. Islam, M.M.; Uddin, J.; Kashem, M.A.; Rabbi, F.; Hasnat, M.W. Design and implementation of an IoT system for predicting aqua fisheries using arduino and KNN. In Proceedings of the Intelligent Human Computer Interaction: 12th International Conference, IHCI 2020, Daegu, Republic of Korea, 24–26 November 2020; Springer: Berlin, Germany, 2021; pp. 108–118.
  11. Reis, J.Z.; Gonçalves, R.F. The role of internet of services (ios) on industry 4.0 through the service oriented architecture (soa). In Proceedings of the IFIP International Conference on Advances in Production Management Systems, Seoul, Republic of Korea, 26–30 August 2018; pp. 20–26.
  12. Shafiq, S.I.; Szczerbicki, E.; Sanin, C. Manufacturing data analysis in internet of things/internet of data (IoT/IoD) scenario. Cybern. Syst. 2018, 49, 280–295.
  13. Kocabay, A.; Javadi, H. Cyber-Physical Systems—Manufacturing Applications. In Industry 4.0; Springer: Berlin, Germany, 2023; pp. 35–56.
  14. Turygin, Y.; Božek, P.; Nikitin, Y.; Sosnovich, E.; Abramov, A. Enhancing the reliability of mobile robots control process via reverse validation. Int. J. Adv. Robot. Syst. 2016, 13, 1729881416680521.
  15. Božek, P.; Bezák, P.; Nikitin, Y.; Fedorko, G.; Fabian, M. Increasing the production system productivity using inertial navigation. Manuf. Technol. 2015, 15, 274–278.
  16. Colombo, A.W.; Karnouskos, S.; Kaynak, O.; Shi, Y.; Yin, S. Industrial cyberphysical systems: A backbone of the fourth industrial revolution. IEEE Ind. Electron. Mag. 2017, 11, 6–16.
  17. Leitao, P.; Karnouskos, S.; Ribeiro, L.; Lee, J.; Strasser, T.; Colombo, A.W. Smart agents in industrial cyber–physical systems. Proc. IEEE 2016, 104, 1086–1101.
  18. Ding, D.; Han, Q.L.; Wang, Z.; Ge, X. A survey on model-based distributed control and filtering for industrial cyber-physical systems. IEEE Trans. Ind. Inform. 2019, 15, 2483–2499.
  19. Jiang, X.; Li, S. Plume front tracking in unknown environments by estimation and control. IEEE Trans. Ind. Inform. 2018, 15, 911–921.
  20. Wang, L.; Törngren, M.; Onori, M. Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 2015, 37, 517–527.
  21. Cassoli, B.B.; Jourdan, N.; Nguyen, P.H.; Sen, S.; Garcia-Ceja, E.; Metternich, J. Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review. Procedia CIRP 2022, 112, 567–572.
  22. Napoleone, A.; Negri, E.; Macchi, M.; Pozzetti, A. How the technologies underlying cyber-physical systems support the reconfigurability capability in manufacturing: A literature review. Int. J. Prod. Res. 2022, 61, 3122–3144.
  23. Zhu, Q.; Rieger, C.; Başar, T. A hierarchical security architecture for cyber-physical systems. In Proceedings of the 2011 4th International Symposium on Resilient Control Systems, Boise, ID, USA, 9–11 August 2011; pp. 15–20.
  24. Senyondo, H. Formal Analysis and Verification of Cyber-Physical Systems for the Smart Grid. Ph.D. Thesis, University of Miami, Coral Gables, FL, USA, 2015.
  25. Chen, S.; Ma, M.; Luo, Z. An authentication scheme with identity-based cryptography for M2M security in cyber-physical systems. Secur. Commun. Netw. 2016, 9, 1146–1157.
  26. Kalogeras, G.; Anagnostopoulos, C.; Alexakos, C.; Kalogeras, A.; Mylonas, G. Cyber Physical Systems for Smarter Society: A use case in the manufacturing sector. In Proceedings of the 2021 IEEE International Conference on Smart Internet of Things (SmartIoT), Jeju, Republic of Korea, 13–15 August 2021; pp. 371–376.
  27. Passarini, R.F.; Farines, J.M.; Fernandes, J.M.; Becker, L.B. Cyber-physical systems design: Transition from functional to architectural models. Des. Autom. Embed. Syst. 2015, 19, 345–366.
  28. Zhang, Y.; Xu, Q.; Guan, X.; Chen, C.; Li, M. Wireless/wired integrated transmission for industrial cyber-physical systems: Risk-sensitive co-design of 5G and TSN protocols. Sci. China Inf. Sci. 2022, 65, 1–16.
  29. Hasan, M.K.; Habib, A.A.; Shukur, Z.; Ibrahim, F.; Islam, S.; Razzaque, M.A. Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations. J. Netw. Comput. Appl. 2022, 209, 103540.
  30. Bordel, B.; Alcarria, R.; Martín, D.; Robles, T.; de Rivera, D.S. Self-configuration in humanized cyber-physical systems. J. Ambient Intell. Humaniz. Comput. 2017, 8, 485–496.
  31. Bradley, J.M.; Atkins, E.M. Optimization and control of cyber-physical vehicle systems. Sensors 2015, 15, 23020–23049.
  32. Abdel-Basset, M.; Mohamed, R.; Mohammad, N.; Sallam, K.; Moustafa, N. An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems. Mathematics 2021, 9, 1140.
  33. Huang, J.; Zhu, Y.; Cheng, B.; Lin, C.; Chen, J. A PetriNet-based approach for supporting traceability in cyber-physical manufacturing systems. Sensors 2016, 16, 382.
  34. Niggemann, O.; Frey, C. Data-driven anomaly detection in cyber-physical production systems. at-Automatisierungstechnik 2015, 63, 821–832.
  35. Yang, Q.; Liu, Y.; Yu, W.; An, D.; Yang, X.; Lin, J. On data integrity attacks against optimal power flow in power grid systems. In Proceedings of the 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 8–11 January 2017; pp. 1008–1009.
  36. Du, C.; Tan, L.; Dong, Y. Period selection for integrated controller tasks in cyber-physical systems. Chin. J. Aeronaut. 2015, 28, 894–902.
  37. Bai, Y.; Park, J.; Tehranipoor, M.; Forte, D. Real-time instruction-level verification of remote IoT/CPS devices via side channels. Discov. Internet Things 2022, 2, 1–19.
  38. Chen, L.; Yue, D.; Dou, C.; Cheng, Z.; Chen, J. Robustness of cyber-physical power systems in cascading failure: Survival of interdependent clusters. Int. J. Electr. Power Energy Syst. 2020, 114, 105374.
  39. Shakshuki, E.M.; Malik, H.; Sheltami, T. WSN in cyber physical systems: Enhanced energy management routing approach using software agents. Future Gener. Comput. Syst. 2014, 31, 93–104.
  40. Zeng, J.; Yang, L.T.; Ma, J. A system-level modeling and design for cyber-physical-social systems. ACM Trans. Embed. Comput. Syst. (TECS) 2016, 15, 1–26.
  41. Ali, S.; Qaisar, S.B.; Saeed, H.; Farhan Khan, M.; Naeem, M.; Anpalagan, A. Network challenges for cyber physical systems with tiny wireless devices: A case study on reliable pipeline condition monitoring. Sensors 2015, 15, 7172–7205.
  42. Huang, S.; Tao, M. Competitive swarm optimizer based gateway deployment algorithm in cyber-physical systems. Sensors 2017, 17, 209.
  43. Huang, R.; Chu, X.; Zhang, J.; Hu, Y.H. Scale-free topology optimization for software-defined wireless sensor networks: A cyber-physical system. Int. J. Distrib. Sens. Netw. 2017, 13, 1550147717713626.
  44. Singh, H. Big data, industry 4.0 and cyber-physical systems integration: A smart industry context. Mater. Today: Proc. 2021, 46, 157–162.
  45. Bhuiyan, M.Z.A.; Wu, J.; Wang, G.; Cao, J. Sensing and decision making in cyber-physical systems: The case of structural event monitoring. IEEE Trans. Ind. Inform. 2016, 12, 2103–2114.
  46. De Persis, C.; Postoyan, R. A Lyapunov redesign of coordination algorithms for cyber-physical systems. IEEE Trans. Autom. Control 2016, 62, 808–823.
  47. Cao, X.; Cheng, P.; Chen, J.; Ge, S.S.; Cheng, Y.; Sun, Y. Cognitive radio based state estimation in cyber-physical systems. IEEE J. Sel. Areas Commun. 2014, 32, 489–502.
  48. Deshmukh, S.; Natarajan, B.; Pahwa, A. State estimation over a lossy network in spatially distributed cyber-physical systems. IEEE Trans. Signal Process. 2014, 62, 3911–3923.
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