Wireless Networks (2021) 27:2595–2613 https://doi.org/10.1007/s11276-021-02579-1 (0123456789().,-volV)(0123456789(). ,- volV) Smart grid cyber-physical systems: communication technologies, standards and challenges A. V. Jha1 • B. Appasani1 • A. N. Ghazali1 • P. Pattanayak2 • D. S. Gurjar2 • E. Kabalci3 • D. K. Mohanta4 Accepted: 23 February 2021 / Published online: 30 March 2021 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The recent developments in embedded system design and communication technologies popularized the adaption of the cyber-physical system (CPS) for practical applications. A CPS is an amalgamation of a physical system, a cyber system, and their communication network. The cyber system performs extensive computational operations on the data received from the physical devices, interprets the data, and initiates effective control actions in real-time. One such CPS is the smart grid CPS (SG-CPS) consisting of physical devices with diverse communication requirements, and intermediate communication networks. Thus, reliable communication networks are paramount for the effective operation of the SG-CPS. This paper is an elaborate survey on the communication networks from the perspective of the SG-CPS. This paper presents the state-of-art communication technologies that can meet the communication requirements of the various SG-CPS applications. The communications standards and communication protocols are also comprehensively discussed. A systematic mapping among communication technologies, standards, and protocols for various SG-CPS applications has been presented based on an extensive literature survey in this paper. Furthermore, several challenges, such as security, safety, reliability and resilience, etc., have been addressed from SG-CPS’s perspective. This work also identifies the research gaps in the various domains of the SG-CPS that can be of immense benefit to the research community. Keywords Communication technologies Cyber-physical systems Smart grid Resilience and reliability Challenges in smart grid communications Resilience Reliability 1 Introduction The term ‘‘cyber-physical systems’’ (CPS) refers to modern systems that provide combined computational as well as physical capabilities through interaction, processing, computation, and control [1]. They are controllable and & P. Pattanayak prabina.pattanayak@ieee.org 1 School of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, India 2 Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Assam 788010, India 3 Department of Electrical and Electronics Engineering, Nevsehir Haci Bektas Veli University, Nevsehir 50300, Turkey 4 Department of Electrical and Electronics Engineering, Birla Institute of Technology, Ranchi 835215, India observable systems that integrate physical devices such as sensors, actuators, hardware, etc., with cyber systems having computational capabilities. The CPS’s physical unit comprises various data collection and sensing devices that interact with the cyber system. The cyber system processes the data received from these devices, analyses the data, performs computations, and generates an actuating signal that is communicated to the physical system over communication networks. These cyber and physical systems must be properly integrated to ensure secure, safe, reliable, and continuous service availability. Thus, the communication networks are the backbone of the CPS and are also referred to as cyber networks. A typical cyber-physical system is illustrated in Fig. 1, where the communication infrastructure interconnects cyber systems and physical systems to exchange data. The most comprehensive survey on various aspects of CPS is presented by Zhou et al. [2]. Particularly, a lot of research has gone into design and analysis of the 123 2596 Wireless Networks (2021) 27:2595–2613 Fig. 1 A cyber-physical system communication networks for CPSs. These works can be broadly segregated into the following categories: • • • • CPS CPS CPS CPS security safety reliability and resilience According to the Scopus database, the statistics for the year-wise research articles published in the context of the CPS cyber networks are illustrated in Fig. 2. It can be observed that research has been focused on addressing the security and safety issues in CPS. However, not much research has been found on the reliability of the cyber networks. The rapid development and standardization of devices such as sensors, actuators; advancement in the technologies such as machine-to-machine (M2M), Internet-of-Things (IoT); evolvement in networking such as wireless sensor networks (WSN), vehicular area network (VANET), mobile ad-hoc network (MANET); and exponential improvement in the computing algorithms and processing capabilities have together transformed the way we communicate, control and monitor the parameters in various domains of practical application. The CPS is also a Fig. 2 Publication statistics on cyber networks 123 consequence of such developments that encompasses several recent technologies, including the burgeoning IoT, artificial intelligence, machine learning, etc. [3]. CPS’s potential applications sprawled over various domains, ranging from agriculture to industries, from entertainment to critical information sharing, from critically evolved infrastructure to assistive living, and many more [4]. Surveillance, autonomous cars, automated insulin delivery pumps in healthcare, home automation, smart grid, etc., are key applications under the CPS paradigm [5–9]. The CPS technologies in several domains have been intensively explored in the past. The CPS technologies in agriculture management, environmental monitoring, industrial automation, smart transportation, aviation, logistic management, smart grid, telemedicine, healthcare, etc. are well explored by the experts from the academia and industries [10, 11]. The smart grid is one such CPS, which is rapidly evolving due to its pervasive heterogeneous applications such as synchrophasor measurement, wide area monitoring systems (WAMS), advanced metering infrastructure (AMI), vehicle-to-grid (V2G) networks, home area network (HAN), etc. The smart grid cyber physical system (SG-CPS) envisages modernizing the smart grid by enabling efficient, effective, secure, and reliable operations across all its phases, including data sensing, processing, computing, analyzing, storing, and exchanging. 2 Related work and motivation Despite of having numerous research articles on the CPS, there are very few articles that envisage the smart grid as the CPS. This fact can be corroborated from Fig. 3, where, Fig. 3a represents the number of research articles that are published in the Scopus database from the years 2000 to 2009 and Fig. 3b represents the articles published from the years 2010 to 2019 respectively. From these statistics, it can be concluded that in the latter decade, the smart grid from the perspective of CPS has received impressive Fig. 3 Published works on SG as CPS a 2000–09 b 2010–19 Wireless Networks (2021) 27:2595–2613 attention (19% publication on SG-CPS) compared to the previous decades (only 2% publication on SG-CPS). Thus, a survey on the smart grid as a CPS is vital to identify technical bottlenecks and challenges that hinder the SGCPS efficacy. To realize the next generation SG-CPS applications with the capability to monitor, control, and operate efficiently in real-time without or little human interference, it is pertinent to discuss the existing research and their suitability to SG-CPS. Even though there exists extensive survey on the CPS in literature, the survey of CPS from the SG perspective is limited. Moness et al. have presented a survey in the domain of wind energy conservation application of the CPS [12]. Jia et al. have considered the vehicular cyber physical system to present the most comprehensive survey which discusses platoon management, clustering, coordinating, and cooperative adaption vehicular-to-vehicular communication, and other such issues for CPS based vehicular application [13]. A panoramic survey on big data technology and its extension to CPS was presented by Atat et al. [14]. It is a nice overview of different aspects, such as data handling, processing, computing, cybersecurity, etc. Understanding the significance of data analysis in SG CPS, the most comprehensive review is done by Rossi et al. [15]. Unlike previous works with similar themes, Rossi et al. have reviewed the data analytics for all the smart grids domains such as smart meter, energy management, distribution, demand-load, consumer, markets, etc. A different approach has been adopted by Xu et al. in [16], where authors have given an excellent survey on Industrial Internet of Thing (I-IoT) from the perspective of CPS. Moreover, the survey was fascinating from the technological aspects of I-IoT CPS. Particularly, three technological domains have been covered, namely, networking, computing, and control. In [17], authors have discussed different testbeds that can be used to design and validate CPS’s model. The survey is rich in its content, but it is focused on the evolution of testbeds and interrelated technological efforts. Furthermore, since the authors intended to survey the testbeds for smart grid CPS, some of the critical issues such as security, safety, reliability, and resilience in SG-CPS domain were not discussed. In He et al. [18], have realized the complexity of the smart grid CPS and related potential threats. Even though several security challenges and defense strategies have been reviewed systematically, the smart grid CPS network’s security perspective was less explored in this paper. For clarity and better understanding, some of the prominent surveys and their research domains have been summarized in Table 1. In a nutshell, to the best of our knowledge, none of the existing papers review the smart grid cyber-physical system from the perspective of enabling communication technologies, standards, protocols, security, safety, 2597 reliability, and resilience. Thus, the state-of-art review on all these issues and provisioning methodologies seems to be of paramount interest. The motivation for this work is to present these topics comprehensively. The rest of the article is organized as follows: Sect. 3 presents the SG from CPS’s perspective. A pragmatic review on architectural overview, key characteristics, and domains of SG-CPS have been presented in this section. Different communication technologies, standards, and protocols of the smart grid cyber-physical system are discussed in Sect. 4. Considering the complexity due to several domains of the SG-CPS, the state-of-art review on various communication protocols and standards have been presented in this section along with their scope. For a better comprehension, a systematic mapping between communication technologies, protocols, standards, and application domains for the SG-CPS has also been presented in Sect. 4. Furthermore, some of the critical responsibilities of the communication networks are also elaborated in this section. A panoramic review of the different challenges such as security, safety, reliability, and resilience; and their countermeasures are presented in Sect. 5. Besides, the risk assessment standards for both security and safety in the context of a typical CPS is also discussed in this section. Finally, the paper is concluded in Sect. 6, identifying the potential research gaps in various domains of the SGCPS paradigm. 3 Smart grid cyber physical system We begin this section by presenting the smart grid from the perspective of the CPS. The conventional power grid was not designed as a flexible grid to incorporate future demand such as smart metering, smart monitoring, control, incorporation of renewable energy sources, etc. SG transforms the existing grid into a flexible grid, an amalgamation of many disciplines, including generation, transmission, distribution, consumer, operation, markets, etc., that envisages the real-time monitoring and control of the grid’s health status. Such, typical cyber-physical system based SG is depicted in Fig. 4. The SG-CPS aims to integrate computational components with physical components to embed monitoring, processing, and control capabilities in real-time. The SGCPS is the effective integration of the physical systems and the cyber system. In SG-CPS, power network infrastructures and sensors correspond to the physical system, while the controlling and monitoring entities correspond to the cyber system. SG from the CPS perspective is different from the traditional automation based application pyramid [42]. SG-CPS is primarily based on decentralization, whereas traditional automation applications focus on the 123 2598 Wireless Networks (2021) 27:2595–2613 Table 1 List of surveys on SG-CPS Survey domain Contents Publication year References Communication technologies, standards and protocols Different communication technologies, standards, and protocols focusing smart grid and other CPS oriented applications [2010–2013] [19–23] Testbeds for SG-CPS Realization of testbeds for SG-CPSs to facilitate real-time implementation of the SG-CPSs with improved performance and reduced cost [2014–2017] [24–27] SG-CPS security Security issues of cyber and physical components [2012–2020] [18, 28–36] SG-CPS privacy Privacy concerns of smart grids and legal frameworks; several approaches to handle privacy issues such as anonymization, trusted computing, cryptographic, perturbation, verifiable computation, etc. [2012–2019] [30, 32–34] Reliability Reliability challenges and countermeasures in the smart grid communication networks [2015–2020] [35, 37–39] Resiliency Resiliency analysis of smart grid, and SG-CPS in response to natural and unnatural threats/attacks [2019–2020] [35, 36, 40, 41] Fig. 4 Smart grid cyberphysical system hierarchical structure [43]. The SG-CPS typically exhibits the following characteristics [44]: • Systematic dynamic integration of the virtual world components with the real-world components. • Capable to dynamically integrate the various sources of energy such as coal, nuclear, thermal, solar, wind, etc. to the mainstream for maximum and efficient utilization of the resources. • Dynamically processing the inputs fed from the SGCPS’s physical components, analyzing them, and responding in real-time. • The interactions between the SG-CPS’s physical and cyber components in a dynamic environment over communication or cyber networks (e.g., infrastructure network, ad hoc network, hybrid networks, etc.). • The real-time exchange of the data with minimum delay. 123 • The real-time integration of grid with the distribution system such as microgrid, mini-grid, etc., in a dynamically varying environment. • The capability to adapt, organize, learn, and respond automatically to several challenges such as faults, attacks, and emergencies to make the SG safer, secure, reliable, and resilient. • Perform parallel computations to achieve timely decisions and ensure SG’s efficient operation across all the layers of the CPS such as transient, scheduling and distribution layers. There has been some pioneering research in SG-CPS, including physical systems, communication networks, standards, protocols, security, safety, privacy, cloud computing, etc., as discussed in [45]. The modeling and simulation of SG-CPS are essential to understand its efficacy. The dynamics of SG in the context of CPS was carried out in [46]. A control system approach was adopted to simulate the interaction and its dynamics between real and physical Wireless Networks (2021) 27:2595–2613 units of the SG-CPS. The issues of design, simulation, and modeling of SG-CPS have been presented in [24, 47–52], respectively. Furthermore, the seminal work by authors in [53] corresponds to modeling and assessment of the power system from the perspective of CPS. They have also outlined the significance of the energy management system and the Internet of energy. Further, knowing the widespread adoption of the hierarchical control system approach for the power system operation, authors in [54] have extended the hierarchical control systems modeling to the cyber-physical power system. The SG’s highly complex and interdependent nature requires an extensive experimental simulation that can meet the real implementation requirements. So far, the researchers have mainly focused on a simulation-based result for the SG [55, 56]. To address the real-world challenges in implementing the SG-CPS, a testbed for the prototype implementation is the present requirement. Moreover, the testbeds to implement the smart grid CPS prototype are the fastest way to confirm the degree of truthfulness for researchers and serve as an educational platform for the student working in different smart grid domains. An extensive survey is presented by Cintuglu et al. in [57] for the SG testbeds. According to NIST [58], the SG comprises of seven domains that are interconnected. These domains include customer domain, market domain, service provider domain, operational domain, bulk generation domain, transmission domain, and distribution domain. These domains include various physical and cyber components such as sensors, actuators, intelligent electronic devices (IEDs), smart meter, distributed energy resources (DERs), etc. The physical components are interconnected to exchange the data through cyber components such as 2599 software, communication networks, and infrastructures. Figure 5 indicates different domains of the SG-CPS and their functional dependencies. • Generation system The different generation sources of the power system include traditional sources such as coal, thermal, nuclear, hydrothermal, etc. Despite these, the small-scale non-traditional sources of the generation include renewable sources such as wind, solar, geothermal, biomass, etc. Smart grid cyber-physical system aims to integrate traditional and renewable generation sources, which could be one way to tackle the depletion issue of traditional energy sources. The SG-CPS envisages putting a roadmap for systematic integration of the distributed generation system and paves the way for incorporating the energy sources at a small scale. E.g., energy generated by the individual consumers can also be integrated into the main grid. • Transmission system The energy generated from various sources at the generation systems is transmitted over transmission lines. Thus, the interaction between the generation system and transmission lines becomes a critical parameter for the smart grid CPS. The SG-CPS connects the dispersed generation system to the distribution system using the critical transmission system power infrastructure to supply electricity to the loads or microgrids. • Distribution system The large-scale voltage carrying through the transmission lines is reduced to the substations’ distribution voltage. Thus, the distribution system bridges the gap between the transmission system and the consumer system. The various components of the distribution system include a smart metering system, DERs, and other loads. The use of microgrids Fig. 5 NIST defined domains of SG-CPS 123 2600 • • • • can improve the efficiency and efficacy of the SG system. Operation This is one of the most critical SG-CPS domains, responsible for the uninterrupted flow of electricity. Further, the operation domain is primarily responsible for the maintenance of the health of the power system. The operation domain handles the smart grid’s five core functionalities, namely, detect, respond, recover, identify, and protect. Thus, it is regarded as the manager of the SG-CPS. Service provider The service provider domain is responsible for providing services to the customers and other utilities. The different aspects of cybersecurity have to be incorporated in this domain to make the system resilient to cyber-attacks and any other threats. Market This domain includes various operators, customers, and other participants. This domain’s prime responsibilities are to build a competitive market for the SG-CPS, which caters to several advantages such as affordable cost, solution, security measures, remove monopoly, and other SG-CPS services. Customer In general, the customers are the end-user entity, which consumes the electricity. However, in SGCPS, a customer may also generate (renewable sources), manage, and store the electricity, integrated into the mainstream of the SG-CPS. Thus, it will undermine the customer as only a consumer of the electricity in SG-CPS. Traditionally, the customers are grouped into three main categories based on their domain: industrial, commercial, and residential. Wireless Networks (2021) 27:2595–2613 packet drop, more reliability, high resilience, better scalability, high availability, etc. in addition to the requirements of typical cellular-based commercial applications [60]. A comprehensive survey of CPS communication infrastructure and its requirements is presented in [61]. Some of the other prominent surveys in this direction is also summarized in Table 1. One can deduce that the communication network infrastructure is sparsely explored in the context of SG-CPS [62]. A. Communication technologies The communication technology for the smart grid CPS is broadly classified into two main categories: wired and wireless as shown in Fig. 6. The wired communication technology includes power line communication, and fibre optic communication. Microwave, cellular and satellite-based communication fall in the wireless category. (i) Power line communication The power line communication (PLC) technology utilizes the existing power line transmission media to exchange the data between the substation and the control centre [63]. This communication technology uses the existing infrastructure and thus, its implementation requires less additional cost. The PLC technologies include narrowband PLC (NBPLC) and broadband PLC (BB-PLC). The NB-PLC is used for low data rate applications, 4 SG-CPS communication network: technologies, standards and protocols Many essential functionalities of the SG-CPS heavily depend on communication infrastructures or cyber networks [59]. Some of the critical responsibilities of the communication network include: • Real-time monitoring and control of the grid in a dynamic environment. • Exchanging the data pertaining to grid health among the various entities of the SG in a real-time. • To provide uninterrupted, reliable, safe and secure service. • Protection of various key elements of the SG including generation, transmission, and distribution infrastructures. The requirements of the communication infrastructure for SG-CPS are different from the typical communication requirements. For e.g., the SG-CPS requires less delay, less 123 Fig. 6 SG communication network technologies Wireless Networks (2021) 27:2595–2613 (ii) (iii) (iv) and the BB-PLC communication technology supports high data rate applications. For the smart grid applications, which is a missioncritical, the BB-PLC would be a better choice supporting data rates of 2-3 Mbps typically. However, due to the fading effect, the signal to noise ratio (SNR) degrades. Further, it becomes difficult to model the PLC-based channel for signal analysis due to the presence of heavy background noise. Thus, PLC technologies are only a limited choice for most of the SG CPS applications. Fibre optic communication The fibre optical communication (FOC) supports long-distance data communication with high security as well as a high data rate. Extensive research has successfully reduced the cost of optical fibre-based communication systems. At the same time, the signal quality in terms of SNR has witnessed significant improvement. Therefore, the optical fibre’s adoption seems to be the best choice for mission-critical applications in SG-CPS [64]. Several applications such as substation automation, tele-control and protection, etc., already utilize the optical fibre-based communication infrastructures. Even though, optical fibre is seen as the best choice for communication infrastructure in SG-CPS, however, due to wired nature, it is plagued with many issues such as high installation cost, scalability, significant installation time, etc. Microwave communication The demand for mission-critical applications can be fulfilled using microwave-based wireless communication [65], which is referred to as a microwave communication (MWC). As a result of technological advancement, the data rate has increased to many folds to meet most of the communication requirements of the SG-CPS applications. It has various advantages compared to wired communication technologies, such as support a high data rate (up to several Gbps), less installation time, low installation cost, and easy connectivity to remote places where a wired connection is not feasible, etc. Data reliability and security are significant concerns. Cellular communication Cellular communication provides another viable alternative for communication in SGCPS [66, 67]. The technological advancement 2601 (v) in this field has paved the way to achieve a data rate of up to 100 Gbps using cellular technologies and evolving continuously. Cellular technology provides a higher data rate with minimum communication delay, making it one of the best choices for several applications. Network availability and security issues plague its utility. Satellite communication Satellite communication has seen tremendous growth in a few past decades, including the data rate, transmission efficiency, communication delay, etc. Despite achieving a higher data rate, the delay incurred in satellite communication is much higher than all other wireless technologies [68]. Thus, today’s satellite communication technology can cater to the demand of some of the SG applications, however, the choice is not optimum with respect to the delay in the context of missioncritical SG-CPS. B. Communication standards and protocols SG-CPS involves a wide range of devices that exchange data catering to different applications. Several standards have been developed to ensure the smooth operation of the smart grid through the combined efforts of different standard developing organizations (SDOs) such as the Institute of Electrical and Electronics Engineers (IEEE), International Standards Organizations (ISO), International Electrotechnical Commissions (IEC), the American National Standards Institute (ANSI), etc. In an SG, Phasor measurement units (PMUs) are important sensors responsible for monitoring various aspects of the grid. Their prominence and practical utility led to their wide-scale deployment in the last few decades. These are known as synchrophasor devices, and the applications involving these devices are known as synchrophasor applications. The IEEE has devised a standard to felicitate the connectivity and data transfer among these devices manufactured by various manufacturers. The IEEE standards for the synchrophasor applications were originally documented in IEEE Std. 1344-1995 [69]. The various shortcomings of this standard were rectified, and it was reaffirmed in the year 2001 and was replaced in the year 2005 with IEEE Std. C37.118-2005 [70]. This standard mostly concentrated on source coding aspects but did not discuss the communication channel’s transmission aspects in depth. The revised standard modified the original frame structure defined for carrying the synchrophasor data. This standard has given explicit methodologies to distinguish the communication requirements and 123 2602 Wireless Networks (2021) 27:2595–2613 measurement requirements. Further, IEEE has sliced this standard into two parts for its wider adoption, which are: (1) IEEE Std C.37.118.1 [71, 72], and (2) IEEE Std. C.37.118.2-2011 [70, 73]. The later standard specifies the communication requirement whereas, the IEEE Std C.37.118.1 specifies the measurement requirements for the synchrophasor applications. Another important application of the SG-CPS is substation automation. IEC 61850 and its subsequent variants such as IEC 62351-6 (with enhanced security features) are considered as the de-facto standards for communication between substations with intelligence capability, lower cost and seamless connectivity [74]. IEC 61850 [75] standard defines the communication protocols for exchanging the data between intelligent electronic devices (IEDs) at the electrical substations for automation. Initially, this standard was defined for communication within a substation, which is now evolved to support inter substation communication over wide area networks. The substation communication architecture using IEC 61850 standard is as shown in Fig. 7. The advanced metering infrastructure (AMI) is another vital application in the SG-CPS, primarily responsible for exchanging energy consumption-related data between enduser and service providers. IEC 62056 and ANSI C12 are the two most widely used standards for AMI applications. Initially, IEC 61107 was developed for direct load data exchange, which was further succeeded by IEC 62056 standards [76] for communicating the smart metering data. The efficacy of the IEC 62056 has been improved through various successive developments in the recent past. The most recent development is documented in IEC 62056-84:2018, which specifies the PoweRline Intelligent Metering Evolution (PRIME) communication profiles using the OFDM modulation techniques as recommended in ITU-T G.9904:2012 [77, 78]. PRIME incorporates state-of-the-art methodology for enhancing security through advanced encryption standards for communication (AES-COM) 128 Fig. 7 IEC 61850 substation automation architecture 123 bytes encryption technology and other authentication and password management techniques. Similar to the IEC 62056 standard, the ANSI C12 is used in AMI applications in North America. However, the former is widely adopted to use in European countries. A comprehensive summary of these communications standards are summarized in Table 2 along with other standards for various SG-CPS applications. Moreover, key features of these standards and their suitability make it handy for the readers to design, test, and validate the SGCPS based applications. The SG-CPS protocols are the means to exchange information supported by the cyber network infrastructure. A large geographical distance separates the generation station and consumers. The various physical components belonging to generation and consumer systems are interconnected to one another and to control centers over communication networks. The communication networks encompass different private and public domain networks. The communication network of the SG-CPS can be designed with several wired protocols such as Ethernet (IEEE 802.3); or wireless protocols such as Wi-Fi (IEEE 802.11), WiMAX (IEEE 802.16), ZigBee (IEEE 802.15), Mobile-Fi (IEEE802.20) [79], etc. Moreover, communication networks are generally hybrid in nature, involving both wired and wireless networks. E.g., IEC 61850 standards use Ethernet protocols for substation automation in the power system [80]. Independent work for requirements and applications of some of these standards in relation to the smart grid is carried out by Appasani et al. [81]. The SG-CPS is a complex system that integrates a wide range of devices. Hence, the communications system needs to handle voluminous data to cater to the diverse demand of the SG-CPS in real-time. Thus, selecting one communication technology among many is a complicated task. The appropriate communication technology depends upon many factors such as the type of application, the scope of applications, standards, deployment challenges, the openness of applications, use cases, etc. For e.g., some of the mission-critical applications of the SG-CPS, such as synchrophasor communication, require a minimum delay to work properly. Suppose the data is exchanged between PMU and phasor data concentrator (PDC) without error, but with a significant delay, it may result in the disruption of the service, or degrade the performance of the system, or even can result in a power blackout. On the other hand, SG-CPS’s advanced metering application is not concerned with the delay in data exchange. But, data integrity is of utmost priority. Consequently, it is very hard to predict the correct combinations of communication technologies, communication standards, and protocols for the SG-CPS. However, it was believed Wireless Networks (2021) 27:2595–2613 2603 Table 2 Some of the notable communication standards and their scope in SG-CPS Standards Key features Applications IEEE Std. 1344-1995 Defines synchrophasor communication parameters. Synchrophasor communication IEEE Std. C37.1182005 It defines the data communication protocols with both source coding aspects and transmission channel coding aspects. Synchrophasor communication IEEE Std C.37.118.1-2011 Includes measurement requirement Synchrophasor communication IEEE Std. C.37.118.2-2011 Includes communication requirement Synchrophasor communication IEC 61850 Defines communication protocols for IED at substations Communication for electrical substation automation IEC 62351-6 Enhanced security features for communication To enhance security features in IEC 60870, IEC 61850, IEC 61970 and IEC 61968 IEC 62056 It standardizes the device language message specification (DLMS) and companion specification for energy metering (COSEM) specifications AMI ANSI C12 Emphasis on accuracy and performance parameters for electricity meters AMI IEC 60870-5 Incorporates communication profile for exchanging basic telecontrol data between two systems through directly connected permanent data circuits connecting the systems Power system automation and telecontrol IEC 60870-6 Incorporates ISO standards and ITU-T recommendations for exchanging basic telecontrol data Power system automation and telecontrol IEC 60834 Includes performance requirement and testing methodology for teleprotection equipment of command type Teleprotection equipment of power system IEC 61970 Provides appropriate data exchange interfaces across existing and new systems An application program interface for energy management system IEC 61968 Defines interfaces for all key components of the distribution management systems interface architecture Communication between the electrical distribution system SAE J2836 It standardizes the communication protocols between an electric vehicle and the power grid for energy and other application-related data. Electric vehicle to utility grid communication PRIME It incorporates PLC standards, uses OFDM technology for efficient physical layer connectivity, improves security and authentication through AEC-COM 128 bytes encryption Advance metering, grid control and asset monitoring IEEE 1815-2012 (DNP3) IEEE 2030 It is compliant with IEC 62351-5. SCADA system It envisages a reference model for interoperability in smart grid applications Guide to smart grid interoperability IEEE 1901 Coexistence of inter-system protocol and supports CSMA/ CA and TDMA multiple access technologies. Over power line broadband communication Open Automated Demand Response (OpenADR) IEC approved OpenADR as publically available specification (PAS) to be included in IEC international standards Energy management system HomePlug Specification for PLC-based applications Communication between in-home electrical system and appliances M-bus Based on European standards EN 13757-2 for physical and link layers, EN 13757-3 for application layer Meter reading application BACnet Compliance with ASHRAE, ANSI and ISO 16484-5 standards Building automation and control networks Utility Smart Network Access Port (U-SNAP) It is a modular communication interface which is independent of protocol In-home communication of any home area network (HAN) or demand response standards. Protocol independent communication of smart grid utility with HAN. IEC 62055 Application layer protocols for payment systems based on standard transfer specification Electricity metering 123 2604 that there exists a strong correlation between standards, applications, and communication technologies. In this paper, an extensive literature survey has been conducted to analyze different communication technologies, standards, and protocols in the SG-CPS domain. Based on the extensive literature survey, a state-of-art framework is presented to establish a correlation between communication technologies, standards, and protocols for different applications of SG-CPS. This correlation is shown in Wireless Networks (2021) 27:2595–2613 Fig. 8. Nevertheless, this is not an exhaustive mapping. For e.g., for EMS application, the PRIME standard can be used over satellite or optical fibre technology. Thus, it is better to accept that this complex correlation is not an exhaustive representation and further correlations are feasible. Fig. 8 Communication technologies, standards and application correlation 123 Wireless Networks (2021) 27:2595–2613 5 Security, safety, reliability and resilience The CPS is different from the conventional computational information sharing system as its architecture includes heterogeneous technologies for physical, computational, and cyber components. The challenges related to the security, safety, reliability and resilience of communication network in the SG-CPS may result in adverse consequences. Some of these adverse consequences system are reported in [82, 83]. Another important challenge in this direction is the integration of traditional information technology (IT) systems with operational technology (OT) systems. OT systems differ from traditional IT systems with respect to the target, purpose, computing components, communication technologies, and interfaces as well as ownership and management. We now present some of the prominent work with key findings related to SG-CPS. In later sections, we analyse the standards for different challenges and then finally, we present some methodologies to evaluate the system performance in response to these challenges. A. SG-CPS security Like all other applications, the intrusion can disturb the normal performance of the SG-CPS. The physical or cyber intrusions may disrupt services, degrading the quality of service, or even power blackout conditionally. If the SG-CPS is not secured, then any of these events can result in substantial economic losses. Thus, cybersecurity becomes an essential aspect in the case of SG-CPS to avoid any form of intrusion. Moreover, domain-specific strategies must be employed instead of existing cyber-security solutions for SG-CPS, making it less vulnerable to cyber-attacks [84]. This has motivated the experts to design the specific protocols to make smart grid domains secure w.r.t intrusion. Tan et al. in [32], has presented an extensive survey of the security characteristics of sensor data in the SG through its various life stages involving generation, acquisition, processing, and storage. The most comprehensive survey on privacy, in addition to security, has been presented in [34]. Giraldo in [31] has presented a survey of the CPS from the perspective of both: security and privacy. (i) 2605 components: Confidentiality, Integrity, and Availability. These security-related fundamental components are popularly referred to as the CIA triad [86]. The SG-CPS, an industrial control system (ICS), differs from the traditional information technology (IT) systems in that SG-CPS needs all the IT systems’ security requirements. But, security analysis of the SGCPS must consider the complexity of the smart grid physical layer. The smart grid cyber physical system’s physical components are composed of many electrical networks spanning over large geographical area covering several kilometers that carry very high voltages that require monitoring, control, and maintenance on a real-time basis. Thus, the security measures in SG-CPS required additional features and the security measures of IT systems. Moreover, the data between the SG-CPS’s cyber and physical components are exchanged over the Internet, which is an open application to the public. Therefore, it adds a further risk of vulnerability to the SG-CPS. Nevertheless, the CIA triad’s priority levels in IT system changes to the AIC triad when it focuses on ICS applications such as SG-CPS, which is as shown in Fig. 9. Many research problems have been identified in the SG literature to ensure integrity, confidentiality, availability, authentication, etc., which can be found in [87, 88]. The special publication (SP) 800-82 of NIST [89] provides comprehensive security requirements in ICS and IT systems. It also provides approaches for securing ICS with unique performance, reliability, and safety requirements. The implementation guidance for NIST SP 800-53 controls is also standardized in NIST 800-82. The SG-CPS’s critical infrastructure can be made resistive to the cyber-attacks based Security assessment standards ISA 99 [85] states that security or cybersecurity envisages protecting the cyber environment of the organization or individual user including, networks and network resources: both software and hardware. As defined by ISO, security comprises three fundamentals Fig. 9 Priority indicator for SG-CPS 123 2606 Wireless Networks (2021) 27:2595–2613 (ii) on the framework outlined in the cybersecurity framework (CSF) [90]. The standards ISA 99 or IEC 62443 devise the mechanism to analyze the cyber risks and paves the way to countermeasure the security challenges at several stages of the SG, including design, testing, installation, maintenance, and inspection. IEC 62351 [91] overcomes the shortcoming of its predecessor IEC 61850 by adding the security measures. Moreover, distributed network protocol (DNP 3.0), IEEE Std. C37.118 and Modbus are some other standards that adopt the security measures in exchanging the synchrophasor data in the SG-CPS [92]. Security assessment methodology for SG-CPS The assessment of an SG-CPS from the perspective of security analysis is very important. The vulnerability threat control framework based methodology protects the system and its assets from security threats. Several methodologies exist in the literature for security risk analysis; few of them are listed in Table 3. It is worth noting that each assessment technologies are unique in how they are implemented to evaluate the security measures. The system designer can adopt appropriate technologies based on their application requirements of the SG-CPS. B. SG-CPS safety Safety analysis is essential in two folds- first human safety and second capital safety. Safety risks are caused by an interaction between the dynamic environment and SG-CPS components. Like in every industry, safety analysis is essential in SG-CPS as well. The safety in the SG-CPS incorporates software safety, functional safety, and operational safety. (i) Safety risk assessment standards The HARA (hazard analysis and risk assessment) is the fundamental approach to analyse the safety risk in the industries [102]. The IEEE Table 3 Security risk assessment technologies Security risk assessment technologies References Attack tree analysis [93, 94] Cyber-physical security [95] STPA-sec [96, 97] Traditional security technology [94] Bayesian network approaches [98–100] Block-chain [101] 123 (ii) 3000-series standards are developed on the HARA approach for the safety analysis in the SG. The other standards such as the U.S Department of Energy DOE-Std 1170-2007 are also based on the HARA approach for the SG, particularly involving nuclear-based generation systems. The safe failure function (SFF) is defined in IEC 61508 Standard along with the safety integrity level (SIL) to confirm the level of safety in deployed components of the SG. The HARA is the basic approach for SIL for the majority of the safety standards [103]. The standards also set the guidelines for analysing the safety performance of the system. Further, the IEC 61506 is viewed as the basic guideline for maintaining the safety standards that are applicable to all kinds of industries. Thus, it can also be utilized to set the safety standards for SG-CPS. Moreover, some of the process-specific safety standards such as ISA 84, IEC 61511, ISO 26262, etc. can also be incorporated while measuring the SG-CPS’s safety performance. Safety risk assessment methodology for SGCPS According to 99/IEC 62443, risk assessment and management cover three features: physical safety, functional safety, and cybersecurity [104]. The issue of physical safety covers the different causes that may be natural or unnatural. The natural causes responsible for physical safety issues are fire, earthquake, heavy rain, tsunami, accidents, etc. The unnatural causes for the interruption in the service may be the consequences of political, social, or criminal activities. Functional safety involves protecting, safeguarding, and monitoring the devices from accidental failures [105]. The safety risk assessment methodology primarily intends to identify the risk, analyze the risk, and estimate the risk’s impact with respect to the safety challenges in the SG-CPS. The safety risk assessment methodologies are primarily based on the HARA discipline. The HARA methodologies for the safety assessment are composed of two parts: first those which analyze the malfunctioning of the physical or cyber components; and second those which indicate the level of risk and measure it in terms of SIL. Some of the common safety assessment methodologies for CPS are listed in Table 4. Interestingly, the HARA based safety assessment methodologies would be the better choice for the SG-CPS. Wireless Networks (2021) 27:2595–2613 2607 Table 4 Safety risk assessment technologies Safety risk assessment technologies References Fault tree analysis (FTA) [106] Failure modes and effects analysis [107–109] Hazard and operability methodology [110–112] Model-based engineering [113] Goal tree-success tree and master logic diagram [114, 115] System theoretic process analysis [116] C. SG-CPS reliability The SG-CPS’s reliability measures the system performance in terms of service availability and trustworthiness [117]. The success of SG-CPS applications heavily relies on the reliability of various key components of the physical as well as the cyber systems, and the connecting cyber networks. Some of the key components of the SG-CPS, whose reliability analysis is of supreme importance, are enumerated below: • • • • Embedded hardware Control center Communication networks Software Most of the reliability analysis methods were directed towards the synchrophasor application in SG. The reliability estimation of the physical sensors like the PMUs was thoroughly discussed in [118]. The earliest work in the direction of reliability of communication networks between the PMUs and the control center was carried in [119] by Liu et al. In this, the reliability analysis of the IEEE 14 bus system was analyzed using the series-parallel methodology. Overall reliability analysis of PMU and its communication networks has been carried out by the authors in [120] through proper modeling. The reliability analysis for the underlying communication network infrastructure of the SG for various applications was investigated in [121, 122]. The reliability analysis of the SG based on the Markov chain model was investigated in [123, 124]. The reliability performance was measured in terms of packet loss probability, where the communication links were designed as cyclic polling queuing system. The reliability analysis was carried out in [125, 126] based on the fault tree analysis approach. A redundancy system for the smart grid is another alternative to make the SG-CPS reliable to the faults. Such reliability analysis was proposed in [127]. Each source-destination pairs are connected with two disjoint paths of which one is wired, and the other is wireless, to enhance the reliability. D. SG-CPS resilience According to [128], network resilience is defined as the capability of a communication network to provide an acceptable level of service and to maintain the quality of service in dynamic environments even in response to various faults and challenges. The resilience should be given paramount importance in SG-CPS as the faults in the infrastructure are inevitable. Further, resiliency analysis measures the system’s overall performance through security, safety, reliability, etc. during normal operation and even under various threats. Resilience can be divided into two disciplines: challenge tolerance that focuses on network design parameters, and trustworthiness that focuses on the network’s performance measure [129]. A comprehensive classification of resiliency discipline is shown in Fig. 10. Further, the challenge tolerance and trustworthiness together represent the robustness of the SG-CPS network infrastructure. Challenge tolerance is composed of disruption tolerance and traffic tolerance. Disruption tolerance includes several natural and unnatural factors. The natural disruptive tolerance includes dynamic environmental behavior such as earthquakes, rain or even natural disasters. Unnatural disruptive tolerance includes the failure of network infrastructure components, bugs in the software, malfunctioning of the hardware, disruption in energy supply, etc. The traffic tolerances include the factors responsible for sudden traffic injection into the networks, which may be legitimate resulting from heavy traffic during the peak hour of operation or illegitimate such as the distributed denial of service (DDoS) resulting from cyberattacks. However, resiliency to DDoS is the network’s ability to reject the Fig. 10 SG-CPS resilience discipline 123 2608 Wireless Networks (2021) 27:2595–2613 request identified as a malicious request. Trustworthiness comprises three fundamental measurable characteristics: reliability, safety, and security [128]. Moreover, trustworthiness is a measurable characteristic that defines the system’s assurance to deliver the service as expected under all conditions and with minimum delay [117]. As discussed in the earlier section, the availability, integrity and confidentiality (AIC triad) is the priority (CIA triad for IT system) for the SG-CPS. 6 Conclusion The research on the SG-CPS and its implementation need further development and improvement. The following research gaps have been identified in this regards: • The proper choice of network technologies, standards, and protocols are still unavailable for generic SG-CPS applications. It varies depending upon the feature and scope of applications. To a certain extent, a concise framework to establish a correlation between communication technologies, standards and protocols for different SG-CPS applications was presented in this paper based on an extensive literature survey. This may be used as a reference in designing the generic SG-CPS. • The testbeds for SG-CPS emulating the real-world characteristics are yet to be developed. The various testbeds that have been discussed in this paper may be used as a reference to further explore in this regard. • Lack of appropriate algorithms, which can resolve security and safety issues. The existing works rarely provide quantitative or qualitative conflict resolution algorithms. This paper has given an overview of security and safety challenges. These challenges have been analyzed systematically. The security and safety standards and assessment methodologies from the existing research body have been comprehensively analyzed in this paper. This can help the researchers to further bridge the existing gaps in this direction for SGCPS. • The universal safety and security measurement technologies are still lacking. The researchers can design technologies to countermeasure security and safety issues, the efficacy of which can be evaluated based on the methodologies presented therein in this paper. • The reliability assessment methodology of an SG-CPS is not yet explored in the literature for different applications. Understanding the importance of reliability, we have introduced several approaches to assess the 123 reliability, providing some guidance to the researchers in this direction. • Resilience analysis methodology for SG-CPS needs to be explored to ensure its serviceability and availability under dynamic environmental conditions. The discussion presented in this work can be used to design resilient SG-CPS. The SG-CPS is a very complex system where cyber and physical systems are tightly integrated. Understanding SGCPS’s complexity, its various domains must be explored systematically. Moreover, one needs to address the gaps mentioned above to design such complex SG-CPSs. Moreover, this is the need of the hour since the cyber components and physical components must be intertwined more effectively and efficiently. References 1. Jirkovský, V., Obitko, M., Kadera, P., & Mařı́k, V. (2018). Toward plug play cyber-physical system components. IEEE Transactions on Industrial Informatics, 14(6), 2803–2811. 2. Zhou, Y., Yu, F. R., Chen, J., & Kuo, Y. (2020). Cyber-physicalsocial systems: A state-of-the-art survey, challenges and opportunities. IEEE Communications Surveys Tutorials, 22(1), 389–425. 3. Yu, X., & Xue, Y. (2016). Smart grids: A cyber-physical systems perspective. Proceedings of the IEEE, 104(5), 1058–1070. 4. Serpanos, D. (2018). The cyber-physical systems revolution. Computer, 51(3), 70–73. 5. Leitão, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T., & Colombo, A. W. (2016). Smart agents in industrial cyberphysical systems. Proceedings of the IEEE, 104(5), 1086–1101. 6. Harvey, M. J., Liu, X., & Chow, J. Y. J. (2016). A tablet-based surrogate system architecture for ‘‘in-situ’’ evaluation of cyberphysical transport technologies. IEEE Intelligent Transportation Systems Magazine, 8(4), 79–91. 7. Tang, J., Ibrahim, M., & Chakrabarty, K. (2019). Randomized checkpoints: A practical defense for cyber-physical microfluidic systems. IEEE Design Test, 36(1), 5–13. 8. Watteyne, T., Handziski, V., Vilajosana, X., Duquennoy, S., Hahm, O., Baccelli, E., & Wolisz, A. (2016). Industrial wireless ip-based cyber -physical systems. Proceedings of the IEEE, 104(5), 1025–1038. 9. Rajabi Shishvan, O., Zois, D., & Soyata, T. (2018). Machine intelligence in healthcare and medical cyber physical systems: A survey. IEEE Access, 6, 46-419-46–494. 10. Kim, S., Won, Y., Park, I., Eun, Y., & Park, K. (2019). Cyberphysical vulnerability analysis of communication-based train control. IEEE Internet of Things Journal, 6(4), 6353–6362. 11. Ernst, R. (2018). Automated driving: The cyber-physical perspective. Computer, 51(9), 76–79. 12. Moness, M., & Moustafa, A. M. (2016). A survey of cyberphysical advances and challenges of wind energy conversion systems: Prospects for internet of energy. IEEE Internet of Things Journal, 3(2), 134–145. 13. Jia, D., Lu, K., Wang, J., Zhang, X., & Shen, X. (2016). A survey on platoon-based vehicular cyber-physical systems. IEEE Communications Surveys Tutorials, 18(1), 263–284. Wireless Networks (2021) 27:2595–2613 14. Atat, R., Liu, L., Wu, J., Li, G., Ye, C., & Yang, Y. (2018). Big data meet cyber-physical systems: A panoramic survey. IEEE Access, 6, 73 603-73 636. 15. Rossi, B., & Chren, S. (2020). Smart grids data analysis: A systematic mapping study. IEEE Transactions on Industrial Informatics, 16(6), 3619–3639. 16. Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018). A survey on industrial internet of things: A cyber-physical systems perspective. IEEE Access, 6, 78 238-78 259. 17. Yang, C., Zhabelova, G., Yang, C., & Vyatkin, V. (2013). Cosimulation environment for event-driven distributed controls of smart grid. IEEE Transactions on Industrial Informatics, 9(3), 1423–1435. 18. He, H., & Yan, J. (2016). Cyber-physical attacks and defences in the smart grid: a survey. IET Cyber-Physical Systems: Theory Applications, 1(1), 13–27. 19. Wang, W., Xu, Y., & Khanna, M. (2011). A survey on the communication architectures in smart grid. Computer Networks, 55(15), 3604–3629. 20. Yan, Y., Qian, Y., Sharif, H., & Tipper, D. (2013). A survey on smart grid communication infrastructures: Motivations, requirements and challenges. IEEE Communications Surveys Tutorials, 15(1), 5–20. 21. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2013). A survey on smart grid potential applications and communication requirements. IEEE Transactions on Industrial Informatics, 9(1), 28–42. 22. Rohjans, S., Uslar, M., Bleiker, R., González, J., Specht, M., Suding, T., & Weidelt, T. (2010). Survey of smart grid standardization studies and recommendations. First IEEE International Conference on Smart Grid Communications, 2010, 583–588. 23. Bian, D., Kuzlu, M., Pipattanasomporn, M., Rahman, S., & Shi, D. (2019). Performance evaluation of communication technologies and network structure for smart grid applications. IET Communications, 13(8), 1025–1033. 24. Palensky, P., Widl, E., & Elsheikh, A. (2014). Simulating cyberphysical energy systems: Challenges, tools and methods. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(3), 318–326. 25. Poudel, S., Ni, Z., & Malla, N. (2017). Real-time cyber physical system testbed for power system security and control. International Journal of Electrical Power & Energy Systems, 90, 124–133. 26. Holm, H., Karresand, M., Vidström, A., & Westring, E. (2015). A survey of industrial control system testbeds. In S. Buchegger & M. Dam (Eds.), Secure IT systems (pp. 11–26). Cham: Springer International Publishing. 27. Hossain, E., Kabalci, E., Bayindir, R., & Perez, R. (2014). Microgrid testbeds around the world: State of art. Energy Conversion and Management, 86, 132–153. 28. Sun, C.-C., Liu, C.-C., & Xie, J. (2016). Cyber-physical system security of a power grid: State-of-the-art. Electronics, 5, 2–18. 29. Li, F., Shi, Y., Shinde, A., Ye, J., & Song, W. (2019). Enhanced cyber-physical security in internet of things through energy auditing. IEEE Internet of Things Journal, 6(3), 5224–5231. 30. Liu, J., Xiao, Y., Li, S., Liang, W., & Chen, C. L. P. (2012). Cyber security and privacy issues in smart grids. IEEE Communications Surveys Tutorials, 14(4), 981–997. 31. Giraldo, J., Sarkar, E., Cardenas, A. A., Maniatakos, M., & Kantarcioglu, M. (2017). Security and privacy in cyber-physical systems: A survey of surveys. IEEE Design & Test, 34(4), 7–17. 32. Tan, S., De, D., Song, W., Yang, J., & Das, S. K. (2017). Survey of security advances in smart grid: A data driven approach. IEEE Communications Surveys Tutorials, 19(1), 397–422. 2609 33. Kumar, P., Lin, Y., Bai, G., Paverd, A., Dong, J. S., & Martin, A. (2019). Smart grid metering networks: A survey on security, privacy and open research issues. IEEE Communications Surveys Tutorials, 21(3), 2886–2927. 34. Jawurek, M., Kerschbaum, F., & Danezis, G. (2012). SoK: Privacy technologies for smart grids-a survey of options. Cambridge: Microsoft Res. 35. Tuinema, B. W., Rueda Torres, J. L., Stefanov, A. I., GonzalezLongatt, F. M., & van der Meijden, M. A. M. M. (2020). Cyberphysical system modeling for assessment and enhancement of power grid cyber security, resilience, and reliability (pp. 237–270). Cham: Springer International Publishing. 36. Zhu, Q. (2019). Multilayer cyber-physical security and resilience for smart grid (pp. 225–239). Cham: Springer International Publishing. 37. Xu, S., Qian, Y., & Hu, R. Q. (2015). On reliability of smart grid neighborhood area networks. IEEE Access, 3, 2352–2365. 38. Ye, F., Qian, Y., Hu, R. Q., & Das, S. K. (2015). Reliable energy-efficient uplink transmission for neighborhood area networks in smart grid. IEEE Transactions on Smart Grid, 6(5), 2179–2188. 39. Li, Y., Yin, X., Wang, Z., Yao, J., Shi, X., Wu, J., et al. (2019). A survey on network verification and testing with formal methods: Approaches and challenges. IEEE Communications Surveys Tutorials, 21(1), 940–969. 40. Haggi, H., nejad, R. R., Song, M., & Sun, W. (2019). ‘‘A review of smart grid restoration to enhance cyber-physical system resilience,’’ In 2019 IEEE innovative smart grid technologies Asia (ISGT Asia), pp. 4008–4013. 41. Cheng, Z., & Chow, M. (2020). ‘‘Resilient collaborative distributed energy management system framework for cyberphysical dc microgrids,’’ IEEE transactions on smart Grid, pp. 1. 42. Monostori, L. (2014). ‘‘Cyber-physical production systems: Roots, expectations and R&D challenges,’’ Procedia CIRP, vol. 17, pp. 9 – 13, variety Management in Manufacturing. 43. Karnouskos, S., Colombo, A. W., Bangemann, T., Manninen, K., Camp, R., Tilly, M., Stluka, P. Jammes, F., Delsing, J., & Eliasson, J. (2012). ‘‘A SOA-based architecture for empowering future collaborative cloud-based industrial automation,’’ In IECON 2012 - 38th annual conference on IEEE industrial electronics society, pp. 5766–5772. 44. Zhao, J., Wen, F., Xue, Y., Li, X., & Dong, Z. (2010). Cyberphysical power systems: Architecture, implementation techniques and challenges. Chinese Automation of Electric Power Systems, 34(16), 1–7. 45. Deep Singh, K., & Sood, K. (2020). 5g ready optical fog-assisted cyber-physical system for iot applications. IET CyberPhysical Systems: Theory Applications, 5(2), 137–144. 46. Shahid, A. (2016). Cyber-physical modeling and control of smart grids - a new paradigm. IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2016, 1–5. 47. Karnouskos, S. (2011). ‘‘Cyber-physical systems in the smartgrid,’’ In 2011 9th IEEE international conference on industrial informatics, pp. 20–23. 48. Lee, E. A. (2008). ‘‘Cyber physical systems: Design challenges,’’ In 2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC), pp. 363–369. 49. Wan, Y., Cao, J., Zhang, S., Tu, G., Lu, C., Xu, X., & Li, K. (2014). An integrated cyber-physical simulation environment for smart grid applications. Tsinghua Science and Technology, 19(2), 133–143. 123 2610 50. Lin, Hua, Sambamoorthy, S., Shukla, S., Thorp, J., & Mili, L. (2011). Power system and communication network co-simulation for smart grid applications. ISGT, 2011, 1–6. 51. Li, H., Lai, L., & Poor, H. V. (2012). Multicast routing for decentralized control of cyber physical systems with an application in smart grid. IEEE Journal on Selected Areas in Communications, 30(6), 1097–1107. 52. Ilić, M. D., Xie, L., Khan, U. A., & Moura, J. M. F. (2010). Modeling of future cyber-physical energy systems for distributed sensing and control. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(4), 825–838. 53. Su, Z., Xu, L., Xin, S., Li, W., Shi, Z., & Guo, Q. (2017). ‘‘A future outlook for cyber-physical power system,’’ In 2017 IEEE conference on energy internet and energy system integration (EI2), pp. 1–4. 54. Xin, S., Guo, Q., Sun, H., Zhang, B., Wang, J., & Chen, C. (2015). Cyber-physical modeling and cyber-contingency assessment of hierarchical control systems. IEEE Transactions on Smart Grid, 6(5), 2375–2385. 55. Li, W., Ferdowsi, M., Stevic, M., Monti, A., & Ponci, F. (2014). Co-simulation for smart grid communications. IEEE Transactions on Industrial Informatics, 10(4), 2374–2384. 56. Kosek, A. M., Lünsdorf, O., Scherfke, S., Gehrke, O., & Rohjans, S. (2014). Evaluation of smart grid control strategies in cosimulation-integration of ipsys and mosaik. Power Systems Computation Conference, 2014, 1–7. 57. Cintuglu, M. H., Mohammed, O. A., Akkaya, K., & Uluagac, A. S. (2017). A survey on smart grid cyber-physical system testbeds. IEEE Communications Surveys Tutorials, 19(1), 446–464. 58. Locke, G., & Gallagher, P. D. (2010). ‘‘NIST framework and roadmap for smart grid interoperability standards,’’ The National Institute of Standards and Technology, vol. Release 10, Gaithersburg, MD, USA. 59. Bu, S., & Yu, F. R. (2013). A game-theoretical scheme in the smart grid with demand-side management: Towards a smart cyber-physical power infrastructure. IEEE Transactions on Emerging Topics in Computing, 1(1), 22–32. 60. Thompson, L. (2002). Industrial data communications, ser. Resources for measurement and control series. ISA–The instrumentation, systems, and automation society. [Online]. Available: https://books.google.co.in/books?id= uu3iAAAAMAAJ 61. Matveev, A. S., & Savkin, A. V. (2009). Estimation and control over communication networks. Switzerland: Birkhäuser Basel. 62. Li, H., Dimitrovski, A., Song, J. B., Han, Z., & Qian, L. (2014). Communication infrastructure design in cyber physical systems with applications in smart grids: A hybrid system framework. IEEE Communications Surveys Tutorials, 16(3), 1689–1708. 63. Appasani, B., & Mohanta, D. K. (2018). A review on synchrophasor communication system: Communication technologies, standards and applications. Protection and Control of Modern Power Systems, 3(37), 1–7. 64. Gungor, V. C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., & Hancke, G. P. (2011). Smart grid technologies: Communication technologies and standards. IEEE Transactions on Industrial Informatics, 7(4), 529–539. 65. Naduvathuparambil, B., Valenti, M. C., & Feliachi, A. (2002). ‘‘Communication delays in wide area measurement systems,’’ In Proceedings of the Thirty-Fourth Southeastern Symposium on System Theory (Cat. No.02EX540), pp. 118–122. 66. Hassan, H. A. H., Pelov, A., & Nuaymi, L. (2015). Integrating cellular networks, smart grid, and renewable energy: Analysis, architecture, and challenges. IEEE Access, 3, 2755–2770. 123 Wireless Networks (2021) 27:2595–2613 67. Kalalas, C., Thrybom, L., & Alonso-Zarate, J. (2016). Cellular communications for smart grid neighborhood area networks: A survey. IEEE Access, 4, 1469–1493. 68. Meloni, A., & Atzori, L. (2017). The role of satellite communications in the smart grid. IEEE Wireless Communications, 24(2), 50–56. 69. IEEE Standard for Synchrophasers for Power Systems. (1995). IEEE Std 1344-1995(R2001) (p. 1). https://doi.org/10.1109/ IEEESTD.1995.93278. 70. IEEE Standard for Synchrophasor Data Transfer for Power Systems. (2011). IEEE Std C37.118.2-2011 (Revision of IEEE Std C37.118-2005) (pp. 1–53). https://doi.org/10.1109/ IEEESTD.2011.6111222. 71. IEEE Standard for Synchrophasor Measurements for Power Systems. (2011). IEEE Std C37.118.1-2011 (Revision of IEEE Std C37.118-2005) (pp. 1–61). https://doi.org/10.1109/ IEEESTD.2011.6111219. 72. Martin, K. E. (2015). Synchrophasor measurements under the ieee standard c37.118.1-2011 with amendment c37.118.1a. IEEE Transactions on Power Delivery, 30(3), 1514–1522. 73. Martin, K. E., Brunello, G., Adamiak, M. G., Antonova, G., Begovic, M., Benmouyal, G., et al. (2014). An overview of the ieee standard c37.118.2-synchrophasor data transfer for power systems. IEEE Transactions on Smart Grid, 5(4), 1980–1984. 74. Ustun, T. S., Farooq, S. M., & Hussain, S. M. S. (2019). A novel approach for mitigation of replay and masquerade attacks in smartgrids using iec 61850 standard. IEEE Access, 7, 15 6044-15 6053. 75. ‘‘[online] available: http://tissues.iec61850.com/parts.mspx.’’ [Online]. Available: http://tissues.iec61850.com/parts.mspx 76. ‘‘IEC, IEC 62056-1-0,’’ Electricity metering data exchange The DLMS/COSEM suite - Part 1-0: Smart metering standardisation framework, (International Electrotechnical Commission, 2014). 77. ‘‘IEC, IEC 62056-8-4,’’ Electricity metering data exchange The DLMS/COSEM suite - Part 8-4: Communication profiles for narrow-band OFDM PLC PRIME neighbourhood networks, (International Electrotechnical Commission, 2018). 78. ‘‘Narrowband orthogonal frequency division multiplexing power line communication transceivers for PRIME networks, ITU-T, recommendation G.9904,’’ Oct. 2012. [Online]. Available: https://www.itu.int/rec/T-REC-G.9904-201210-I/en 79. R. Y. et al., (2010). ‘‘The research on communication standard framework of smart grid,’’ In CICED 2010 proceedings, pp. 1–6. 80. Hoga, C. (2007). New ethernet technologies for substation automation. IEEE Lausanne Power Technology, 2007, 707–712. 81. Appasani, B., Maddikara, J., & Mohanta, D. (2019). Standards and communication systems in smart grid. In E. Kabalci & Y. Kabalci (Eds.), Smart grids and their communication systems. energy systems in electrical engineering. Singapore: Springer. 82. Mishra, S., Li, X., Pan, T., Kuhnle, A., Thai, M. T., & Seo, J. (2017). Price modification attack and protection scheme in smart grid. IEEE Transactions on Smart Grid, 8(4), 1864–1875. 83. Sridhar, S., Hahn, A., & Govindarasu, M. (2012). Cyber attackresilient control for smart grid. IEEE PES Innovative Smart Grid Technologies (ISGT), 2012, 1–3. 84. Clements, S., & Kirkham, H. (2010). Cyber-security considerations for the smart grid. IEEE PES General Meeting, 1–5. 85. ISA-62443-2-1-2009 Security for Industrial Automation and Control Systems: Establishing an Industrial Automation and Control Systems Security Program. (2009). Available at: https:// www.isa.org/products/isa-62443-2-1-2009-security-for-indus trial-automat. Accessed 19 Mar 2021. Wireless Networks (2021) 27:2595–2613 86. Lyu, X., Ding, Y., & Yang, S. (2019). Safety and security risk assessment in cyber-physical systems. IET Cyber-Physical Systems: Theory Applications, 4(3), 221–232. 87. Li, X., Liang, X., Lu, R., Shen, X., Lin, X., & Zhu, H. (2012). Securing smart grid: cyber attacks, countermeasures, and challenges. IEEE Communications Magazine, 50(8), 38–45. 88. Peng, Y., Lu, T., Liu, J., Gao, Y., Guo, X., & Xie, F. (2013). Cyber-physical system risk assessment. Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013, 442–447. 89. Stouffer, K., Falco, J., & Scarfone, K. (2011). Guide to industrial control systems (ICS) security. NIST Special Publications, 800(82), 29–32. 90. NIST Cybersecurity Framework: Framework for Improving Critical Infrastructure Cybersecurity. (2014). Available at: https://www.nist.gov/system/files/documents/cyberframework/ cybersecurity-framework-021214.pdf. Accessed 19 Mar 2021. 91. IEC 62351 Security Standards for the Power System Information Infrastructure. (2012). Available at: http://iectc57.ucaiug. org/wg15public/Public%20Documents/White%20Paper% 20on%20Security%20Standards%20in%20IEC%20TC57.pdf. Accessed 19 Mar 2021. 92. Moussa, B., Debbabi, M., & Assi, C. (2018). A detection and mitigation model for PTP delay attack in an IEC 61850 substation. IEEE Transactions on Smart Grid, 9(5), 3954–3965. 93. Roy, A., Kim, D. S., & Trivedi, K. S. (2012). ‘‘Scalable optimal countermeasure selection using implicit enumeration on attack countermeasure trees,’’ In Proceedings of IEEE/IFIP international conference on dependable systems and networks (DSN 2012), pp. 1–12. 94. Ten, C., Liu, C., & Govindarasu, M. (2007). Vulnerability assessment of cybersecurity for scada systems using attack trees. Proceedings of IEEE Power Engineering Society General Meeting, 2007, 1–8. 95. Sun, M., Mohan, L., & Sha, L. et al., (2009). ‘‘Addressing safety and security contradictions in cyber-physical systems,’’ In Proceedings of 1st workshop. future directions in cyber-physical systems security (CPSSW’09), Newark, New Jersey. 96. Young, W., & Leveson, N. (2013). ‘‘Systems thinking for safety and security,’’ In Proceedings of 29th annual computer security applications Conference (ACSAC), New Orleans, Louisiana, USA, pp. 1–8. 97. Shapiro, S. S. (2016). Privacy risk analysis based on system control structures: Adapting system-theoretic process analysis for privacy engineering. IEEE Security and Privacy Workshops (SPW), 2016, 17–24. 98. Huang, K., Zhou, C., Tian, Y. Tu, W., & Peng, Y. (2017). ‘‘Application of bayesian network to data-driven cyber-security risk assessment in scada networks,’’ In Proceedings of 2017 27th international telecommunication networks and applications conference (ITNAC), pp. 1–6. 99. Zhang, Q., Zhou, C., Tian, Y., Xiong, N., Qin, Y., & Hu, B. (2018). A fuzzy probability bayesian network approach for dynamic cybersecurity risk assessment in industrial control systems. IEEE Transactions on Industrial Informatics, 14(6), 2497–2506. 100. Zhang, Q., Zhou, C., Xiong, N., Qin, Y., Li, X., & Huang, S. (2016). Multimodel-based incident prediction and risk assessment in dynamic cybersecurity protection for industrial control systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(10), 1429–1444. 101. Ding, Q., Wang, X., Zhu, J., et al. (2018). Information security framework based on blockchain for cyber-physics system. Computer Science, 45(2), 32–39. 102. Veeramany, A., Coles, G. A., Unwin, S. D., Nguyen, T. B., & Dagle, J. E. (2018). Trial implementation of a multihazard risk 2611 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. assessment framework for high-impact low-frequency power grid events. IEEE Systems Journal, 12(4), 3807–3815. Yoshimura, I., & Sato, Y. (2008). Safety achieved by the safe failure fraction (sff) in iec 61508. IEEE Transactions on Reliability, 57(4), 662–669. IEC 62443 Security for Industrial Automation and Control System. (2015). Available at: https://webstore.iec.ch/preview/ info_iec62443-2-3%7Bed1.0%7Den.pdf. Accessed 19 Mar 2021. Pan, D., Liu, F., Zhou, X., & Li, T. (2008). ‘‘Functional safety in building automation and control systems,’’ In Proceedings of 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 467–470. Sabaliauskaite, G., & Mathur, A. (2015). ‘‘Aligning cyberphysical system safety and security,’’ In Proceedings of 1st Asia - Pacific Conference on Complex Systems Design & Management, Singapore, pp. 41–53. Nourian, A., & Madnick, S. (2018). A systems theoretic approach to the security threats in cyber physical systems applied to stuxnet. IEEE Transactions on Dependable and Secure Computing, 15(1), 2–13. Grunske, L., Colvin, R., & Winter, K. (2007). ‘‘Probabilistic model-checking support for fmea,’’ In Fourth International Conference on the Quantitative Evaluation of Systems (QEST 2007), pp. 119–128. Ebeling, C. (2009). An Introduction to Reliability and Maintainability Engineering. Long Grove, Illinois: Waveland Press. (1997). Dunjó, J., Fthenakis, V., Vı́lchez, J., et al. (2010). Hazard and operability (HAZOP) analysis, a literature review. Journal of Hazardous Materials, 173(1–3), 19–32. Kennedy, R., & Kirwan, B. (1998). Development of a hazard and operability-based method for identifying safety management vulnerabilities in high risk systems. Safety Science, 30(3), 249–274. Rausand, M. (2013). Risk assessment: theory, methods, and applications. Hoboken, New Jersey: Wiley. Banerjee, A., Venkatasubramanian, K. K., Mukherjee, T., & Gupta, S. K. S. (2012). Ensuring safety, security, and sustainability of mission-critical cyber-physical systems. Proceedings of the IEEE, 100(1), 283–299. Modarres, M., & Cheon, S. (1999). Function-centered modeling of engineering systems using the goal tree-success tree technique and functional primitives. Reliability Engineering and System Safety, 64(2), 181–200. Brissaud, F., Barros, A., & Bérenguer, C. et al. (2009). ‘‘Reliability study of an intelligent transmitter,’’ In Proceedings of 15th ISSAT international conference reliability and quality in design, San Francisco, United States, pp. 224–233. Lee, D., Lee, J., & Cheon, S. et al. (2013). ‘‘Application of system-theoretic process analysis to engineered safety featurescomponent control system,’’ In Proceedings of 37th enlarged halden programme group (EHPG) meeting, Storefjell, Norway. Sterbenz, J. P., Hutchison, D., Çetinkaya, E. K., Jabbar, A., Rohrer, J. P., Schöller, M., & Smith, P. (2010). Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines. Computer Networks, 54(8), 1245–1265. (Resilient and Survivable networks.). Mohanta, D. K., Cherukuri, M., & Roy, D. S. (2016). A brief review of phasor measurement units as sensors for smart grid. Electric Power Components & Systems, 44(4), 411–425. Liu, W., Liu, N., Fan, Y., Zhang, L., & Zhang, x. (2009).‘‘Reliability analysis of wide area measurement system based on the centralized distributed model,’’ In 2009 IEEE/PES power systems conference and exposition, pp. 1–6. 123 2612 Wireless Networks (2021) 27:2595–2613 120. Zhao, X., Lu, J., Wang, Y., Peng, J., He, F., & Wei, H. (2009). Reliability assessment of wams based on a combined hardware and software probability model of phasor measurement units. Dianli Xitong Zidonghua Automation of Electric Power Systems, 33(16), 19–23. 121. Goutard, E., Rudolph, T., & Mesbah, M. (2010). ‘‘Impact of communication network impairments on wide area monitoring, control and protection applications in the IEC61850 environment,’’ In Proceedings of 43rd international conference on large high voltage electric systems 2010, CIGRE 2010. 122. Asprou, M., Hadjiantonis, A. M., Ciornei, I., Milis, G., & Kyriakides, E. (2012). ‘‘On the complexities of interdependent infrastructures for wide area monitoring systems,’’ In 2012 complexity in engineering (COMPENG). proceedings, pp. 1–6. 123. Menike, S., Yahampath, P., Rajapakse, A., & Alfa, A. (2013). Queuing-theoretic modeling of a pmu communication network. IEEE Power Energy Society General Meeting, 2013, 1–5. 124. Rana, A. S., Thomas, M. S., & Senroy, N. (2017). Reliability evaluation of wams using markov-based graph theory approach. IET Generation, Transmission Distribution, 11(11), 2930–2937. 125. Li, J., Zhang, A., Zhang, H., Liu, X., Geng, Y., & Wei, Y. (2015). Reliability evaluation of the wide area protect system. Diangong Jishu Xuebao Transactions of China Electrotechnical Society, 30(12), 344–350. 126. Sodhi, R., & Sharieff, M. I. (2015). Phasor measurement unit placement framework for enhanced wide-area situational awareness. IET Generation, Transmission Distribution, 9(2), 172–182. 127. Castello, P., Ferrari, P., Flammini, A., Muscas, C., Pegoraro, P. A., & Rinaldi, S. (2015). A distributed pmu for electrical substations with wireless redundant process bus. IEEE Transactions on Instrumentation and Measurement, 64(5), 1149–1157. 128. Sterbenz, J., Cetinkaya, E. K., Hameed, M., Jabbar, A., Qian, S., & Rohrer, J. (2013). Evaluation of network resilience, survivability, and disruption tolerance: analysis, topology generation, simulation, and experimentation. Telecommunication Systems, 52(2), 705–736. 129. Rak, J. (2015). Principles of communication networks resilience. In Resilient routing in communication networks. computer communications and networks, Springer, Cham. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. A. V. Jha has completed his Bachelor of Engineering in 2012 from University of Pune, India and Master of Technology in 2015 from IIT Guwahati, India. Currently, he is pursuing PhD from KIIT-DU, India. His research interest includes cyber physical systems and Internet of Things. He has authored many publications in his area of interests. 123 B. Appasani received his B.E, M.E and PhD degree from Birla Institute of Technology, Ranchi, India. He is currently working as an Assistant Professor in School of Electronics Engineering, KIIT-DU, India. His research interests include Smart Grid communication system and Evolutionary Computation. A. N. Ghazali received his PhD degree from Birla Institute of Technology, Ranchi, India. He is currently working as an Assistant Professor in School of Electronics Engineering, KIIT University, India. His research interests include design of ultrawide band filters for space applications. P. Pattanayak received the Ph.D degree in 2017 from Indian Institute of Technology Patna, India. He was awarded M.Tech degree in Electronics and Communication Engineering (Wireless Communication Technology) and B.Tech degree in Electronics and Telecommunication Engineering from Biju Patnaik University of Technology, Rourkela, India,in 2012 and 2007 respectively. He worked as Lead Engineer at HCL Technologies Ltd, India from 2007 to 2010. He was with the National Institute of Science & Technology from 2010 to 2013. He has served as Assistant Professor in the School of Electronics Engineering of Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar from 2017 to 2018. He is serving as Assistant Professor in the department of Electronics and Communication Engineering at National Institute of Technology Silchar, India from 2018 to till date. He received the Gold Medal for M.Tech and best poster presentation in the Research Scholar Day at IIT Patna. He is the recipient of High Value Ph.D Scholarship during his Ph.D tenure. His current research interests include multiuser MIMO communications, multi carrier MIMO communications, soft computing techniques, massive MIMO, NOMA, cross-layer scheduling, and Smart Grid Communication Systems. He is fellow of IETE India, a senior member of IEEE and member of IEEE Communications Society, the IEEE Information Theory Society, and the IEEE Vehicular Technology Society. He is an active reviewer in various international journals. Wireless Networks (2021) 27:2595–2613 D. S. Gurjar received the B. Tech. degree in electronics and communications engineering from Uttar Pradesh Technical University, Lucknow, India, in 2011, the M. Tech. degree in wireless communications and computing from the Indian Institute of Information Technology Allahabad, India, in 2013. He received the Ph.D. degree in electrical engineering from the Indian Institute of Technology Indore, India, in 2017. He was with the department of electrical and computer engineering, University of Saskatchewan, Canada, as a Postdoctoral Research Fellow. Currently, he is working as an Assistant Professor in the department of electronics and communication engineering, National Institute of Technology Silchar, Assam, India. He is recipient of Alain Bensoussan Fellowship-2019 from European Research Consortium for Informatics and Mathematics (ERCIM). He has numerous publications in peer-reviewed journals and conferences. His research interests include MIMO communication systems, cooperative relaying, device-to-device communications, smart grid communications, physical layer security, and simultaneous wireless information and power transfer. He is a member of the IEEE Communications Society and the IEEE Vehicular Technology Society. E. Kabalci (M’09, SM’18) received the B.S. and M.S. degrees in Electronics and Computer Education from Gazi University, Ankara, Turkey, in 2003 and in 2006 respectively. He received the Ph.D. degree from Gazi University, Ankara, Turkey, in 2010 with the thesis on implementing an enhanced modulation scheme for multilevel inverters. From 2005 to 2007, he was with Gazi University as a lecturer. He is currently with the Department of Electrical and Electronics Engineering Department since 2010, 2613 Nevsehir HBV University, where he became an Assistant Professor in 2011; an Associate Professor in 2013, and full Professor in 2019 on power plants and power electronics-drives; and is Head of Department. Dr. Kabalci is an Associate Editor of several international indexed journal on Power Electronics and Renewable energy sources. His current research interests include power electronic applications and drives for renewable energy sources, microgrids, distributed generation, power line communication, and smart grid applications. He is IEEE Member since 2009, and senior-member since 2018. D. K. Mohanta received the Ph.D. (Engg.) degree from Jadavpur University, Kolkata, India. He was an Electrical Engineer with the Captive Power Plant, National Aluminium Company (NALCO), Angul, India from 1991-1998. He is currently a Professor with the Department of Electrical and Electronics Engineering, BIT, Mesra, Ranchi. He has more than 20 years of teaching experience in addition to his industrial experience of 8 years. He has been a Senior Member of IEEE(USA), Member of IEEE PES RRPA subcommittee, Life Member of ISTE and a Fellow of Institutions of Engineers (India). He is an Editor of Power Components & Systems (Taylor & Francis Publications); Associate Editor of IEEE Access as well as of IET Proceedings on Generation, Transmission & Distribution. 123