A System-Level Modeling and Design for Cyber-Physical-Social Systems

Zeng, Jing; Yang, Laurence T.; Ma, Jun

doi:10.1145/2834119

Cited by 33 publications

(11 citation statements)

References 29 publications

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“…Dao and Pongpaichet [ 28 ] proposed a real-time complex event discovery platform for CPSS. Gang and Fenghua [ 26 ] introduced ACP methodology (i.e. artificial societies, computational experiments and parallel execution), proposed by Wang, and discussed a CPSS-based intelligent transportation system (ITS).…”

Section: Related Workmentioning

confidence: 99%

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

Yin

Ming

Zhang

2020

Sensors

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The trend towards socialization, personalization and servitization in smart manufacturing has attracted the attention of researchers, practitioners and governments. Social manufacturing is a novel manufacturing paradigm responding to this trend. However, the current cyber–physical system (CPS) merges only cyber and physical space; social space is missing. A cyber–physical–social system (CPSS)-based smart manufacturing is in demand, which incorporates cyber space, physical space and social space. With the development of the Internet of Things and social networks, a large volume of data is generated. A data-driven view is necessary to link tri-space. However, there is a lack of systematical investigation on the integration of CPSS and the data-driven view in the context of social manufacturing. This article proposes a seven-layered framework for a data-driven CPSS (D-CPSS) along the data–information–knowledge–wisdom (DIKW) pyramid under a social manufacturing environment. The evolution, components, general model and framework of D-CPSS are illustrated. An illustrative example is provided to explain the proposed framework. Detailed discussion and future perspectives on implementation are also presented.

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Section: Related Workmentioning

confidence: 99%

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

Yin

Ming

Zhang

2020

Sensors

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“…CPSS is an acronym which may refer various proposals: from Cyber-Physical Social Sensing to Cyber-Physical Social Systems [ 36 ]. Furthermore, it is not clear if all these proposals refer to the same reality or present some differences [ 22 ]. However, in both cases, CPSS represents the deep integration of traditional technological systems (such as the Internet of Things or Cyber-Physical Systems) with human and social factors, such as people behavior, social opinions or the user mental world [ 20 ].…”

Section: State Of the Artmentioning

confidence: 99%

“…As a current technological paradigm, CPSS present some common characteristics with other recent proposals such as the Internet-of-Things (IoT). However, while IoT solutions are focused on the global interconnection of “things” (daily living objects provided with embedded computers), CPSS are primary designed to perceive human social information including the environment, transportation and social activities [ 22 ]. In both cases, a heterogeneous hardware platform with a high density of devices is considered.…”

Section: Introductionmentioning

confidence: 99%

A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators

Bordel

Sánchez-Picot

2017

Sensors

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Cyber-Physical Social Sensing (CPSS) is a new trend in the context of pervasive sensing. In these new systems, various domains coexist in time, evolve together and influence each other. Thus, application-specific tools are necessary for specifying and validating designs and simulating systems. However, nowadays, different tools are employed to simulate each domain independently. Mainly, the cause of the lack of co-simulation instruments to simulate all domains together is the extreme difficulty of combining and synchronizing various tools. In order to reduce that difficulty, an adequate architecture for the final co-simulator must be selected. Therefore, in this paper the authors investigate and propose a methodology for the design of CPSS co-simulation tools. The paper describes the four steps that software architects should follow in order to design the most adequate co-simulator for a certain application, considering the final users’ needs and requirements and various additional factors such as the development team’s experience. Moreover, the first practical use case of the proposed methodology is provided. An experimental validation is also included in order to evaluate the performing of the proposed co-simulator and to determine the correctness of the proposal.

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“…In recent years, there has been an emerging interest in the field of socially-aware computing through integrating social computing and pervasive computing [1,2]. Then, the cyber-physical social system could deeply integrate the cyber world and the physical world, as well as the social world [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

Xu¹,

Luo²,

Wang³

et al. 2019

Cyber-Enabled Intelligence

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Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes.In addition, the social sensing is a novel application of the cyber-physical social system. Additionally, a new paradigm, named cyber-physical social sensing (CPSS), was developed, in which the perception processes allow humans to participate. The wireless sensor network (WSN) with a simple architecture and cost-saving performance, plays a critical role for the sensor process and provides some important information to serve as a social network in the CPSS [4,5], where those sensors are the primary entities replacing humanity in the traditional social network [6]. Various research regarding the WSN has been undertaken [7][8][9][10][11].Generally, a critical step in constructing a sensor network is to precisely determine the node position through the process of localization. In other words, localization is an indispensable part of WSN [12]. With the purpose of locating sensor nodes precisely, ...

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A System-Level Modeling and Design for Cyber-Physical-Social Systems

Cited by 33 publications

References 29 publications

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

Understanding Data-Driven Cyber-Physical-Social System (D-CPSS) Using a 7C Framework in Social Manufacturing Context

A Methodology for the Design of Application-Specific Cyber-Physical Social Sensing Co-Simulators

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

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