Proceedings of TMCE 2014, May 19-23, 2014, Budapest, Hungary, Edited by I. Horváth, Z. Rusák Organizing Committee of TMCE 2014, ISBN 978-94-6186-177-1
DEVELOPMENT OF A FRAMEWORK FOR INFORMATION ACQUISITION
AND PROCESSING IN CYBER-PHYSICAL SYSTEMS
Yongzhe Li
Faculty of Industrial Design Engineering Delft University of Technology and
State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology
Netherlands/China y.li-8@tudelft.nl
Yu Song Imre Horváth Eliab Z. Opiyo
Faculty of Industrial Design Engineering Delft University of Technology
The Netherlands
{ y.song, i.horvath, e.z.opiyo }@tudelft.nl Guangjun Zhang
Jun Xiong
State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology
China
zhanggj@hit.edu.cn, changfeng0007@163.com
ABSTRACT
In the designing and modeling of CPSs, the information acquisition and processing processes are often application dependent and process oriented. Those information management frameworks are simple and effective for small scale systems. However, many functions developed are not reusable or cannot be directly re-used, when a large number of details and relations need to be added. Aiming at designing a flexible and scalable system with “plug-and-play” components, a preliminary information acquisition and processing framework for CPSs is proposed in this paper based on the object oriented design (OOD) method. The concept of informational hierarchy within CPSs is identified first. Then it is further elaborated as instantaneous information, dynamic information and context information. Using these three types of information, together with the physical properties of a component in CPSs, the concept of hybrid object is proposed as the basic component of the proposed framework. By defining the inherent and update operation of hybrid objects, the proposed information acquisition and processing framework is formed with hierarchical hybrid objects. To verify the effectiveness and the efficiency of the proposed framework, a case study on designing and modeling a gas metal arc welding (GMAW) based rapid
manufacturing system is presented. Limitations of the proposed framework and future research directions are discussed as well.
KEYWORDS
Information acquisition and processing framework, object oriented design, hybrid object, plug-and-play, cyber-physical systems,
1. INTRODUCTION
Cyber-physical systems (CPSs) are physical and engineered systems whose operations are monitored, coordinated, controlled and integrated based on computing and communication technologies [1]. By merging computing and communication with physical processes, CPSs have been manifested from the nano-world to large-scale wide-area systems [2]. In the past several years, CPSs attracted enough attentions in many application domains by the potential of offering more effective and efficient solutions [3].
CPSs bridge the gap between the cyber world and the physical world [4] using the cyber technologies and physical technologies (Figure 1) [5], furthermore, in many cases, cyber technologies and physical technologies in CPSs are integrated and bonded
together to form synergic technologies [6] [7]. Using the cyber, physical and synergic technologies, a CPS is able to make virtual and physical interactions with the social-techno-economic environment. The interactions can be sensed, monitored and controlled via communicating a specific type(s) of signal, data, information or knowledge.
Development of generalized hardware, software, knowledgeware, and hybrid platforms is one of the major research challenges in the context of CPSs [3] [8]. A hybrid CPS platform may include a hardware architecture and a software framework, which enable execution of specific functions and managing different types of signals, data, information and/or knowledge. In the past several years, remarkable research efforts have been devoted to study different aspects of platforms. For instance, Song and Dyke developed an experimental platform for dynamic model updating [9]. Based on their platform, a CPS is able to capture nonlinear response of the system and update itself according to different scenarios. Wan et al. designed a general and low-priced test platform for unmanned vehicles using CPSs technologies [10]. An interesting feature of their platform that the wireless sensor network (WSN) and the unmanned vehicles are treated as separated components in the CPSs, and each unmanned vehicle is equipped with a standard kernel. This treatment reduces the complexity and improves the scalability of the system.
To facilitate the simulation of CPSs, Genge et al. developed an experimentation environment that can concurrently reproduce physical and cyber events in the process of security analysis of CPSs [11]. Recently, based on several case studies,
Al-Hammouri developed a co-simulation platform for CPSs [12]. In comparison with other application-oriented architectures, platforms not only simplify and accelerate the modeling and designing processes of CPSs, but also offer the advantages in many aspects, such as robustness, flexibility, safety, security and scalability [13]. Information acquisition and processing is a key issue for the development and building a CPS platform. The convergence between the cyber and the physical worlds poses new challenges for handling signals, data, information and knowledge in platforms for CPSs [14]. The challenges appear in different aspects, for instance, in terms of data interpretation [15], data storage [14], and heterogeneous information fusion [16].
Recent decades, a number of information processing and management frameworks have been developed. The ParcTab [17] system was the earliest attempt on general context-aware framework [18]. Dey et al. identified five categories of components (context widgets, interpreters, aggregators, services execute, discoverers) that implement distinct functions in composing a context framework. A Context Toolkit was built to instantiate this conceptual framework and supports rapid prototyping of context-aware applications [19]. Mehrotra et al. proposed an event oriented model for CPS, which includes the physical layer, semantic abstraction layer and the high-level steam language layer [20]. Kim et al further addressed the problem of distributed sensing, optimization, and control in the networked CPS by developing an application framework based on partially ordered knowledge sharing for loosely coupled systems. The framework consists of hosts, engines, nodes, and cyber-applications [21].
Korpipaa [22] proposed an information managing framework for distributed context-aware computing in an event-based manner. With a hierarchical structure, the framework includes four main functional entities: context manager, resource servers, context recognition services and applications. Similar frameworks can be found in SOCAM [18], RCM [23] [24], etc. Based on those frameworks, examples of application oriented implementations can be found in medical care [25], traffic control [26], robotics [27], building automation [28] systems, etc. Current information management frameworks are typically process oriented, and address procedures such as information acquisition, processing, organization, and control [25]. Most of these Figure 1 A conceptualization of cyber-physical
frameworks are simple and effective in the case of small-scale system. However, a large number of details and relations should be considered in the implementation according to the requirements of the targeted application [28] [29] [30]. This restricts the safety, flexibility [31], security [32], and especially scalability of CPSs. In addition, the process oriented management frameworks are often application dependent. Many of the developed functions are not reusable, or cannot directly be re-used. For instance, in [19], an active badge location system consisted of a number of infrared sensors distributed throughout a building. If another sensor nodes consisting of a thermal sensor would be inserted into the existing sensing system, the information manage system should be redesigned regarding to the new scenario. Thus, application oriented information management frameworks cannot be used as a basis for a generic platform for CPSs.
The objective of the proposed research is to conceptualize and build a prototype framework for handling and managing signals, data, information and knowledge in CPSs. Differing from existing information management frameworks, the framework should address issues of generality, flexibility and scalability for various CPSs. The proposed information management framework applies the principles of object oriented design (OOD). The physical constituents and the related information constituents of a CPS are encapsulated as a hybrid
object. Structurally, a hybrid object comprises a
knowledge base and multiple interfaces. The knowledge base is able to acquire, process, and store information that is collected both in the cyber world and in the physical world. The interfaces of a hybrid object enable its interactions with other objects. In addition, several hybrid objects are able to join together through the interfaces to form a cluster of
hybrid objects, for instance, a WSN. Relying on a
hybrid object, the proposed framework is able to provide “plug-and-play” function, which accelerates the modeling and designing of CPSs and facilitates achieving generality, flexibility and scalability of the framework.
In the rest of this paper, an explanation on the applied informational hierarchy is first given. Aiming at a better understanding of the information acquisition and processing process in CPSs, the interrelationships between signals, data, information and knowledge are analyzed. In Section 3, the informational hierarchy is further elaborated. Information is categorized as instantaneous
information, dynamic information, and context information. Based on these three types of information, and using the hybrid object concept, an information acquisition and processing framework is proposed in Section 4. In Section 5, information processing technologies are reviewed with the goal to establish relations among instantaneous, dynamic and context information in different types of hybrid objects. In order to verify the effectiveness of the proposed framework, Section 6 provides a case study. It includes modeling and designing of gas metal arc welding (GMAW) rapid manufacturing system. Finally, the feasibility and effectiveness of the proposed framework is presented in the conclusion.
2. INFORMATION ACQUISITION AND
PROCESSING FRAMEWORK IN CPSs
The information processed in a CPS depends on its various constituents and interactions among them [13]. The constituents of a CPS are often referred to as the cyber world and the physical world, as shown in Figure 2(a) [14]. The physical environment has a local part and a remote part. Three types of components constitute a physical world, namely: (i) human, (ii) machines, and (iii) environment. Some of machines have connections with the cyber world, for instance, with mobile devices or portable computers. They can be treated as physical machines also acting as cyber terminals. In the cyber world, databases and algorithms are the main components that are organized by the controller and operate on network infrastructure. Compared to the local physical environment, the cyber world is much broader due to its effective communication range. This offers CPSs the advantages of linking different local physical environments together via communications in the cyber world.To characterize the status of components in CPSs and the interactions among them, the data-information-knowledge-wisdom (DIKW) hierarchy has been partly adopted to form the basis of the information structure [33]. Here, the phase informational
hierarchy is used to describe the relations among a
phenomenon, the produced physical signals, the descriptive data, the conveyed information, and the implied knowledge within CPSs.
In CPSs, the interactions among components lead to the changes of status of components. The status changes of the components always correspond to different types physical and/or cyber phenomena, e.g., to the network traffic [34]. The various phenomena
are captured by using sensors, and transferred as electro- magnetic signals [35]. The signals are then converted to digital forms, and represented as digitized data after scaling.
In the proposed DIKW informational hierarchy, data are defined as symbols that represent properties of objects, events and their environment. Data are products of observation and serve as the source of inferring information which has meaning for both humans and system components. Information is not only contained in descriptions, but it is also provided by answers to questions that begin with such words as who, what, when and how many [36]. In CPSs, information interpreted from data may contain features, relations, etc. For instance, the data expressing the value of the temperature of a given environment is 295. In the context of interpreting the temperature of the environment, 295 can be inferred as 295 degrees Kelvin. However, depending on the setup of the sensor, it can also be 22 degrees Centigrade. These are equal characterization of the information of the feature named temperature. Data can be stored in the databases and semantically processed as information by algorithms via the controller software components of CPSs. However, the algorithms for interpreting data can be constructed based on knowledge. This knowledge is actually know-how on the objectives, the context and the way of achieving the objectives, and makes it possible to transform information into instructions
[36]. In CPSs, knowledge can be obtained either by transferring it from some original owners, or by extracting it from collected information [37]. It can be represented as relations among different components in CPSs, the intelligence in the algorithms, or the intelligence in the controllers.
Based on collected information and the possessed knowledge, CPSs can make the desired interventions into the physical world in an intelligent manner. That is, instructions captured in the knowledge bases are transformed into signals, which are needed to operate the actuators that actually intervene with the physical world, either in a human controlled (manned) or autonomous (unmanned) way. Figure 2(b) shows a rough architecture of such a CPS and its relations with the informational hierarchy. In the left side of the figure, CPS components are categorized according to their functions as sensors, actuators controllers, database and algorithms. The informational hierarchy is presented in the right side of the figure. The concrete relations between the various components of CPSs and the informational hierarchy are indicated by solid lines. The possible relations are represented by dashed lines.
The modeling and designing of the components and the CPSs as a whole are based on the DIKW informational hierarchy. Our literature survey indicates that designing of the cyber world and physical world components of CPSs are often done independently, when conceptualization of the cyber-physical system has been completed [38]. That is, the interactions within and between the cyber world and physical world components are defined as cyber information flows and the physical information flows, respectively [13]. Due to the various geographical locations, the physical information flows are usually further decomposed into many local physical information flows, as shown in Figure 3. These function orientated designs are simple and effective when the complexity of the CPS is low. However, for large CPSs, limitations have been identified in the following aspects:
Firstly, the phenomenon addressed by the DIKW informational hierarchy typically pertains to the Figure 2 CPSs and its informational hierarchy
physical world (though it may pertain to the cyber world too). The analogue signal, which is converted into digital data, creates a bridge (informational link) between the physical world and the cyber world. The data undergo semantic interpretations in contexts before they can be used in the cyber world. This explains why different parts of the entire informational hierarchy belong to different information flows. On the other hand, this lends itself to an extra complexity in the information management of CPSs. For instance, if a physical component is removed from a system, then often extra operations are needed to identify and dump the unused data that belonged to that component.
Secondly, the sensor, controller, actuator, database and algorithm components belonging to the CPS are also separated. This implies that often specific functions are needed to interconnect them. For instance, this may be needed when one type of data have relations to many components in the system. In these cases, complexity of the relation management system may increase exponentially by the number of components.
Thirdly, as part of the increase of the complexity of the CPS, larger number of local physical environments will be introduced and integrated into the system. This also poses challenges for the
knowledge management system of the CPS, and for the implementation of “plug-and-play” functions in terms of the local physical environments.
3. A DEEPER
CONTEMPLATION OF
INFORMATIONAL
HIERACHY IN CPSs
Designing an information acquisition and processing framework that is capable to address all challenges needs a better understanding of information hierarchy in CPSs. The above-introduced DIKW information hierarchy framework provides an opportunity for a clear description of the relations among phenomena, signals, data, information and knowledge within CPSs. It can serve as the basis of abstraction-based modeling and functional, structural and behavioral designing of CPSs. However, further specialization of this informational hierarchy seems to be necessary according to the properties of CPSs. For instance, existing knowledge can be adopted to understand a phenomenon in the CPSs, but it can also be built during the operation of CPSs. It is hard to identify the best origin of specific bodies of knowledge for a CPS. On the other hand, the issue of obtaining knowledge from various sources causes difficulties in the management of knowledge. In the proposed framework, time and context are used as two extra dimensions for managing information. In the DIKW informational hierarchy, information is categorized and treated as: (i) instantaneousinformation, (ii) dynamic information, and (iii) context information. They are graphically shown in
Figure 4.
Instantaneous information
Instantaneous information is a derivative of the signals collected by a sensor regarding a particular aspect of a physical-phenomenon or a cyber-phenomenon at a given moment [39]. As shown in Figure 4, for instance, signals collected by a sensor at time a can be scaled as data. With the context information of the system, the environment, and the Figure 3 A typical information acquisition and processing framework of
component (yellow blocks at the bottom in Figure 4), the information contents of data are explored and interpreted, or even further abstracted and aggregated as knowledge (circles in the vertical plane at time a). Instantaneous information also includes all basic information that can be derived and extracted from signals collected by an arrangement of independent sensors [40]. Typical examples of instantaneous multi-source information include temperature, displacement, force, network traffic, etc. Some sensors can also collect more than one type of signals for instantaneous information. For example, Poghossian et al. utilized an ion-sensitive field-effect transistor (ISFET) in a hybrid sensor module for detecting four physical signal values: (i) flow velocity, (ii) flow direction, (iii) diffusion coefficient of ions, and (iv) liquid level [41]. Another sensor developed by Yang et al is also able to detect four physical parameters, including: (i) plant diameter, (ii) head diameter, (iii) plant weight, and (iv) head weight from aerial photographs and field reflectance spectra [42].
Dynamic information
In the real-world scenario of CPS applications, complex operation domains and dynamic environments are to be counted for [43~45]. Dynamic information represents the changes of a set of instantaneous information in a given time span. For instance, as shown in Figure 4, dynamic information contains a set of pieces of instantaneous information and their reflective or objective changes at times a, b, c, d (see blue dashed line in Figure 4). Capturing dynamic information is important in the
case of many CPS applications, especially for the intelligent control algorithms of CPSs. For instance, Wu et al. analyzed model-based control of serial and parallel robotic system and proposed a method to identify dynamic parameters [46]. Our literature research indicates that dynamic information also plays an important role in system modeling of large-scale CPSs applications with respect to their dynamical uncertainties [47] [48]. Dubey and Crowder proposed a dynamic tactile CPS, implementation of which is capable to detect slip, as well as to provide normal force effect [49].
Context information
Context information is related to: (i) the external characterization of individual components, (ii) their relationships with other components, and (iii) their operating environments. There have been many definitions of context information published in the literature. In our research, we relied on the definition given by Debes et al. concerning their CPS application [50]. Regarding a particular component of a CPS, the context information describes four main aspects of existence:
Identity
As context information, an explicit and unique identifier is given to components;
Location
Context information includes position data and orientation data, as well as information about regional relations to other components. In our research, the abovementioned relations comprise spatial relations and cyber relations as well. These Figure 4 The instantaneous information, the dynamic information, and the context information of a
relations are continuously updated by the instantaneous and dynamic information;
Status
Context information also contains properties, which can be perceived by a user. Status information has a close link with instantaneous information and dynamic information. For instance, as status information, the temperature of a system at a given moment can be updated by instantaneous information and the average temperature of a system in a given time span can be updated by dynamic information;
Time
As context information, time registers the moment when a signal (information) is recorded or provided. In case of many CPSs, it is assumed that the basic context information can be defined based on the knowledge adopted from third party (as indicated by the yellow block in Figure 4). Part of the context information, for instance, Identity, does not change with respect to time. However, Location and Status may change based on the derived instantaneous information and dynamic information (as indicated by the yellow vertical planes in Figure 4). As time elapses, part of the context information will be updated. The richness of the context information may be increased (as indicated by the heights of the vertical planes in Figure 4). In our research, richness of context information is represented for the amount of context information, neglecting the method of measurement.
Using context information in CPSs enables the context-awareness of individual components, thus improves the “smartness” of the system as a whole. For instance, Lee et al. proposed a medical application system that is not only able to detect the physiological parameters, but also offers the context information related to the patient [51]. Using this system, care-givers were brought into the control loop around the patient and they can offer a better service. They can analyze information in context and use delivery devices to initiate treatment. Though it seems to be promising, we have to encounter further issues. For example, in the case of an automotive application of a CPS, information should be provided not only regarding the vehicle itself, but also concerning the engine temperature or the fatigue of the components [52]. However, the deployed CPSs have limited ability to provide high-level contextualized functions, such as safety assessment, optimal route planning, or location-based services.
Relations within a component
As mentioned above, instantaneous information, dynamic information, and context information are closely related. Both instantaneous information and dynamic information necessitates context information in order to achieve a proper (meaningful) interpretation of data (as indicated by the solid arrows in Figure 5). Dynamic information has been defined as a set of instantaneous information related to a particular phenomenon in a given time span. The context may also be changing if we face a longer time span. The changes will cause dynamic context information (which is influenced by the instantaneous information, and even more by the dynamic information), as indicated by the dashed arrows in Figure 5. We note that varying context information can also have an influence on the elicitation and interpretation of instantaneous and dynamic information (as shown by the yellow vertical planes in Figure 4).
4. INTRODUCING THE PROPOSED
INFORMATION ACQUISITION AND
PROCESSING FRAMEWORK
Below we provide information on the conceptualized element and architecture of the proposed framework. The objective of our research was to support the development of heterogeneous and distributed CPSs, which work according to complex application scenarios, by a multi-resource platform. The
Figure 5 Relations among three types of information
informational framework is supposed to serve it with a purposeful information processing workflow by which multiple elements, components and sub-systems are co-working in synergy. Thus, the framework should be able to take care of information acquisition and processing, scalability and flexibility. Technically, the most important is the “plug-and-play” ability, that is, the ability by which a system can automatically configure and operationalize new components [53]. Towards this end, the concept of hybrid object has been developed.
Hybrid objects
Hybrid object is a conceptual entity that compounds both the cyber part and physical part in CPSs when an object is used for modelling purpose. The concept of hybrid object has been proposed based on the understanding of the natural informational hierarchy that underpins the information processing of the different physical components and cyber components of CPSs. It has been used as the basic constituent in our research. Hybrid object is inspired by the concept of object in object oriented design (OOD) [54]. In OOD, an object contains encapsulated data and procedures grouped together to represent an entity of the system. In our research, the notion of ‘object’ from the cyber world has been extended to the physical world and these two worlds are bridged together by software techniques. Thus, from an overall view, hybrid object has logical, functional, structural and operational characters from both internal view and external view
Internal view of a hybrid object
A typical hybrid object contains five major parts: hardware entities, software entities, cyberware entities, a database and an interface, as shown in Figure 6. Hardware entities (HE) are the collection of physical elements of the object that exist in the real world. Cyberware entities (CE) are the virtual entities that exist in the cyber world which reflect the information of physical world as a knowledge repository in CPSs. Software entities (SE) bridge the cyberware part and hardware part of the hybrid object. In addition, SE can decide on the desired interactions and receive information from both the hardware part and cyberware part, and handle the information by a number of managing functions.
In a hybrid object, the database can be seen as a collection of data that can be stored, collected, organized, shared, searched and utilized in both software entities and cyberware entities [55] [56]. Interfaces are used to represent the relationships between hardware, software and cyberware entities of this hybrid object to other objects by sensing, actuating and communication methods.
Types of hybrid objects in the framework
Two types of hybrid objects, namely: (i) the hybrid system object, and (ii) the hybrid component object, are used in the framework, as shown in Figure 7. These two types of hybrid objects are generated based on a basic template of the hybrid object, and each of them has its unique attributes. Generally, the hybrid component object represents a particular real component when it is “plugged in” the system. Hybrid system object is the basic functional unit to handle the hybrid component objects through interfaces within a local world (LW). LW means the functional range (both sensing range and working range) of the system object when a task is handled. It has to be mentioned that a component object can also be seen as a system object when its child components need to be handed. And a system object can also be described as a component object when the system as a whole is managed by another system object of upper level. This kind of property could increase the flexibility of the system when modelling at different levels.
Figure 7 Hybrid system object and hybrid component object
Furthermore, the hybrid system object describes the basic context information, as shown in Figure. 8. It is formed when a local physical environment is plugged or designated in the CPSs. Actually, the context information is the only information that it has and the context information is formed when a system is established. Differently of the system object, the component objects have their own instantaneous information and dynamic information.
Two important operations, called inheritance operation and update operation, have been defined to establish relations between the information associated with the abovementioned two objects, as shown in Figure 8. The component object inherits from the system object. The update function reflects the information collected from the component object onto the system object. That is, changes in the inherited context information will also be reflected by these objects. This way of operation guarantees that the system object always has the latest (“fresh”) context information. Architecture for two types of objects A simple architecture of a CPS, which is based on the two types of hybrid objects, is presented in Figure 9. There are two system objects at the superlative level, named hybrid system object A and B, included in this CPS. In case of the hybrid system object B, there is only one component object, named B1, which inherits from the object B, as indicated by the arrow. In case of the system object A, two component objects, named A1 and A2, are plugged in. As shown in Figure 9, hybrid object A1 is used to simulate a WSN, which has three nodes. This hybrid object can also be seen as another system object and has three child component objects, named A1-1, A1-2 and A1-3, which correspond to three respective nodes of the WSN. The black arrow in Figure 9 indicates the inherence operations and the blue dashed arrows represent the update operations. External Interactions among hybrid objects In the proposed framework, hybrid objects are connected to each other through their interfaces. Figure 10 illustrates the interactions of hybrid system object A with other objects in a CPS through the respective interfaces. In Figure 10, the hybrid system object A has a cyber-sensor, two physical sensors (sensor 1 and 2), a cyber-actuator and two physical actuators (actuator 1 and 2). For a typical cyber interaction, for instance, the hybrid object A tries to acquire information from hybrid object G, as the arrow indicates in Figure 10, object A acquires information via its cyber sensor, and object G delivers the Figure 8 Information exchange in the system object and component object
information through its cyber actuator.
Object A is able to sense information within its sensing range 1 by its physical sensor 1, i.e., it is able to acquire a type of physical information regarding objects B, C and D. The information of objects B, D and E can be sensed within the sensing range 2 using physical sensor 2. Here, an interesting phenomena is that objects B and D are located in the overlapped sensing ranges 1 and 2. That is, information regarding objects B and D is acquired twice from different perspectives corresponding to sensor 1 and
2, respectively. These may help object A to get a
better image of objects B and D, by combining the information by information fusion. Details of information fusion will be discussed in the next Section. Concerning the physical actuators of object
A, two working ranges are defined with regards to
physical actuator 1 and 2, respectively. Each physical actuator is able to perform certain actions on the objects that are located in its working range.
5. INFORMATION PROCESSING WITHIN
THE FRAMEWORK
In a hybrid component object, instantaneous information is acquired by processing raw data collected by sensor(s). In the past decades, many sensors had been developed, together with their information processing algorithms, and adopted in CPSs [57-59]. The known information processing algorithms can perform a wide spectrum of tasks, ranging from simple threshold detection to the execution of rather complex mathematical algorithms.
For instance, Ye et al. developed a type of highly sensitive, surface acoustic wave-based blood pressure sensor relying on finite element simulation results, [60]. Using Kalman filter, Yim et al. was able to track the local position of the user based on the strength and directions of WiFi signals [61].
Dynamic information is constructed in the framework based on instantaneous information. Dynamic information provides information about the changes of instantaneous information. This allows a CPS to monitor, observe, record and respond to phenomena according to complicated scenarios. Based on the generated dynamic information and using the advanced control algorithms, system controllers are able to achieve a better performance [62]. For instance, Vivas and Poignet applied predictive control algorithms in a parallel robot operation scenario. Using predictive control strategy, together with a proportional-integral-derivative (PID) controller as a low-level controller, the system offered advantages compared with the two other types of controller [63].
In real-life situations, context information represents a broad range of information, including the information about the location and the status of the components. Using user context information can add a lot to an aware operation of systems. For instance, Göker and Myrhaug used mobile applications to evaluate the effectiveness of context information among tourists [64]. Kim et al. developed a web-based application to adapt to the user context. According to their experiences, the system which used context information offered more convenience for the user, helped user save time, and reached a higher satisfaction level, when it was evaluated in comparison with other current systems [65].
Sensors are hardly used alone in CPSs. Due to functional requirements and technical limitations, in many applications, typically multiple sensors are used in order to provide those pieces of information that the system exactly needs. CPSs offer the advantage of using multi-sensor data elicitation across the system based on information fusion technology. Information fusion is a technology that enables combining data from sensors or sensor nodes of WSNs [66]. There are many potentials and advantages of using information fusion, such as availability and accuracy, comparing to the outcome of a single sensor [67-69]. Data collected from sensors may be imperfect, correlated, inconsistent and disparate [70] - information fusion algorithms Figure 10 Interactions among hybrid objects in a
can help solving these problems. They are capable to integrate data not only from the same type of sources, but also from different type of sources. Furthermore, they are also capable to enrich and improve the quality of the static, dynamic and context information in CPSs [71]. Recently, sensor information fusion has received a large attention in a many application fields, including complex data processing [72], remote sensing [73], machine intelligence [74], medical instruments [75] and machine vision [76]. In the proposed framework, information fusion is also exploited. Figure 11 describes the applied procedure of improving the quality of context information by information fusion. The descriptive data or the signals produced by a physical or cyber phenomenon are captured by the hybrid
object A through its interface. Within the hybrid object A, dynamic and context information is generated based on information processing techniques such as the ones indicated by the blue arrows in Figure 11. We may also assume that the phenomenon is also captured by hybrid objects B and C from different perspectives. To improve the quality of context information included in object A, information related to the same phenomenon is taken over from objects B and C through the shared interfaces, as shown by the green arrows in Figure 11. This aggregated information is the subject of
information fusion. The information fusion algorithm, which is operated by the controller, can improve the context information possessed by object A by incorporating information acquired from objects B and C related to the phenomenon.
6. AN APPLICATION CASE STUDY
The main objective of this section is to provide evidence on the practical relevance and utility of the proposed informational framework. Toward this goal we analyze an application case. We have applied the proposed framework in abstraction-based modeling and designing a Gas Metal Arc Welding (GMAW) system as a part of a cyber-physical system. As reflected by the literatures, GMAW is currently often used for rapid manufacturing (RM) of fully-functional 3D metal objects directly from CAD models [77]. GMAW uses the welding process for deposition of melted metal in a target object. The scalability of current rapid manufacturing system is not strong enough that an existing system can only produce a category of parts with similar physical parameters. Aiming at realizing the “plug-and-play” function for the welding system, the principles and technologies of CPSs are adopted in the development of the GMAW system. The manifestation of the experimental GMAW system as a CPS is largely influenced by its application domain and procedure [78].The major constituents of a GMAW system include: (i) the welding power supply, (ii) a welding gun held by a motion platform, and (iii) a work piece. For a better control of the GMAW system, two CCD Figure 11 Information fusion in the proposed framework
(charge-coupled devices) cameras are used to monitor the welding bead in the welding process (see Figure 12).
The architecture of the experimental GMAW system is presented in Figure 13. This architecture resembles the generic CPS system presented in Figure 2. In the experimental system: (i) the height of the welding bead, (ii) the width of the welding bead, and (iii) the height of the welding gun are monitored by the sensors. The signals produced by or on the physical phenomena are captured, translated, interpreted and saved in the database via the controller.
Currently, we are focusing on the introduction of more robots and sensors to enlarge the working envelope and improve the accuracy and efficiency of the GMAW system in the further phases of the research and development. Traditionally, the model of a mechanical device-robot cooperation system for RM is quite complicate to create and there are many related challenges and many technical problems may occur in the process of designing the new system. In the current conceptualization phase of the double agents GMAW system, the increased complexity of control and information processing have been identified as major technical issues. The main reasons are as follows.
First, the multi-agent configuration makes the management of the data and knowledge of the system non-trivial. It implies a new abstraction in modeling. In addition, additional parameters are needed for the
correct process and artifact representation. For instance, the geometric shape of the work piece is changing in the welding process. This change necessitates the introduction of a global parameter. Similarly, the other changes in the phenomena require new parameters. Consequently, an extra layer of parameters, named global parameters, has been considered.
Secondly, with the increasing scale of the system, the number of relations among components grows exponentially. This poses challenges for function and information management of CPSs. For example, adding a temperature sensor may lead to the demand that the function of “get temperature” need to be added to every component of the system.
Thirdly, it is very hard to foresee and plan any adaptation to the dynamic scenario of RM in conceptualization of the CPS-augmented GMAW system. Flexibility is one of the key features of an RM system. Therefore, it is assumed that, in the case of prototyping a small and simple work piece, the system will be able to identify the optimal working scenario and adapt to it, for instance, by using only one motion agent. When a larger and more complex work piece is fabricated, a robot will be switched on and added to the system automatically. However, the above mentioned switching on and off a robot actions bring in the needs for an open-system type of operation, but also supposes a series of additional operations regarding every component in the system.
Using the proposed information acquisition and processing framework, the system has been redesigned as shown Figure 14. In the new system design, there are six hybrid objects: (i) a welding system object, (ii) the work piece, (iii) two power supplies, (iv) a motion platform, (v) a robot, and (vi) sensors.
By the blue color used in the graphical representation of the interface, we wanted to indicate that that object is virtually connected. By the blue-green gradient color in the interfaces, we wanted to indicate that the objects are connected both virtually and Figure 13 The design of the experimental system
physically. The key information regarding each hybrid object is listed adjacent to the database. Significantly, the database of sensors records the information of the work piece and the work piece is a hybrid object which only contains a hardware entity. Furthermore, sensors can be seen as another system object and each sensor in it can be considered as a hybrid component object. The design presented in Figure 14 is more scalable and flexible, compared with the previous design. For instance, a robot can be easily switched off by removing the hybrid object (indicated in yellow in Figure 14).
7. CONCLUSION
In this paper, a novel framework is proposed for information acquisition and processing in CPSs. The main findings of the presented research can be summarized as follows:
1. It has proved to be advantageous to categorize and process the kinds of information used in CPSs as instantaneous, dynamic, and context information. They represent interrelated pieces or bodies of information;
2. Hybrid objects, which combines the physical and cyber aspects of a component or subsystems of CPS, can be used as the basis of abstraction-based modeling and designing CPSs;
3. By using the incorporated information processing and information fusion algorithms, the proposed
framework is suitable for heterogeneous and distributed CPSs;
4. Using the proposed framework, a GMAW-based RM system has been redesigned. The application case was able to demonstrate the scalability and the flexibility of the proposed framework.
Besides the above positive findings, some limitations of the framework were also identified:
1. The proposed framework maybe too sophisticated and/or complicated for a small scale CPS;
2. In the current implementation, the system level and environmental level context information is rather application dependent. More case studies are needed to synthesize some sort of general description schemes of the context information. Our follow up research focuses on further consolidation and empirical testing of the proposed informational framework for a more complicated implementation. This will be based on a WSN subsystem and context sensitive reasoning. In addition, our research will also consider the development of an effective information fusion algorithm, which may lead to a better performance even in the case of CPSs with high complexity and non-linear behavior.
ACKNOWLEDGMENTS
The presented research work is conducted at the Faculty of Industrial Design Engineering of the Delft University of Technology. The implementation of the experimental GMAW system has been done in China State Key Laboratory of Advanced Welding and Joining. This part of the project has been supported by the National Natural Science Foundation of China under Grant No. 51175119. The work is also sponsored by Chinese Scholarship Council (CSC).
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