Guest Editorial Special Section on IoT - IEEE Xplore

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

1413

Guest Editorial Special Section on IoT

T

HE AGE of the Internet of Things (IoT) is almost here. According to forecasting, one trillion devices with sensors of all kinds are expected to be connected to the Internet by 2022. The concept of IoT was initially proposed in 1999 in which the IoT was referred to as uniquely identifiable interoperable connected objects with radio-frequency identification (RFID) technology. IoT is considered as the next generation of Internet in which the physical things can be identified and accessed through the Internet. Although the definition of the IoT can vary, depending on perspectives, and the unified definition of IoT is still in the forming process, IoT is a dynamic global network infrastructure with self-configuring capabilities based on interoperable communication protocols; as such, within an IoT, all things are able to exchange data, also processing data [1]. In general, IoT can be referred to as a superset of connecting devices that can be uniquely identifiable by existing near field communication (NFC) technologies. The words “Internet” and “Things” mean a world-wide inter-connected network based on sensory, communication, networking, and information processing technologies, which can be the new version of the information and communications technology (ICT) [1]. In the recent years, IoT technologies have been developed rapidly [2]; in particular, intelligent sensing and wireless communication technologies have become part of the IoT and new research frontiers have emerged. The International Telecommunication Union (ITU) has released their report on IoT-related enabling technologies, implications, potential markets, as well as future challenges [3]. IoT is started with the use of RFID technology, which is increasingly used in logistics, pharmaceutical industry, and many other diverse industries [4]–[6]. IoT enables information gathering, storing, and transmitting be available for things equipped with the tags or sensors. The tags have been widely used in manufacturing, healthcare, logistics industry, food industry, environmental monitoring, and many other areas. The emerging wirelessly sensory technologies have significantly enhanced the sensory capabilities of devices and therefore, the original concept of IoT hence is extending to ambient intelligence and autonomous control. Today, numerous technologies are involved in IoT, such as wireless sensor networks (WSNs), intelligent sensing, RFID, NFC, low energy wireless communications, cloud computing, and others [7]–[10]. The evolutions of these technologies are bringing new technologies to IoT [11], [12]. In the past few years, IoT has been developed rapidly and a large number of enabling technologies have been proposed. The IoT has been the trend of the next Internet and it received attentions from business and industries world-wide. To accelerate the applications of IoT, the development of IT infrastructure plays a key role [13]–[19]. It can be foreseen that applying IoT

Digital Object Identifier 10.1109/TII.2014.2316734

will greatly contribute to many industrial systems. IoT has currently already been deployed in many industrial sectors successfully. A large number of hardware and software components have been developed. In manufacturing sector, an increasing number of products are made with unique identification technologies. These identification technologies make products able to be monitored and tracked in their life cycles. IoT can increase the effectiveness of industrial operations through introducing new data exchange and processing techniques. The IoT is of high importance to economy and society [13]. Both developed and developing countries have recognized the importance and potential of IoT and proposed their national strategies in exploring enabling technologies. For example, the UK government has launched a £5 m project on IoT [1]. In European Union (EU), the IoT European Research Cluster (IERC) (http://www.rfid-in-action.eu/cerp/) has sponsored a number of projects on the fundamental research on IoT. The project of Internet of Things Architecture (IoT-A) was to develop the reference model and architecture of IoT to meet the specific needs in the applications. The European Telecommunications Standards Institute (ETSI) has been studying policies related to IoT. Japan has proposed “u-Japan x-ICT” and “i-Japan strategies.” In the United States, the Information Technology & Innovation Foundation (ITIF) indicated that new ICT can be an effective way to improve traditional physical and information technology infrastructure, and will have a great impact on productivity and innovation. South Korea conducted RFID/USN and “New IT Strategy” program to advance the IoT infrastructure development. In China, the “Sensing China” project was launched in 2010. The objective of the project was to develop the technologies so that any objects in an environment having identity tags, which are able to exchange data, and also be accessible through the Internet. It is our pleasure to present this Special Section on IoT of the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, which reports the state of the art on the subject. This Special Section is to introduce to the readers of industrial informatics the current development and future opportunities that exist in the exciting field of IoT, as the dialogue among researchers and practitioners in both industrial informatics and IoT areas are growing. In this Special Section, 20 papers with novel contributions in IoT and their industrial applications are presented. Selected papers can be categorized into nine IoT related clusters, i.e., WSNs, cloud computing, service computing, interoperability, quality and trust, manufacturing, healthcare, transportation, and environmental protection. The Special Section is started with the paper entitled “A reconfigurable smart sensor interface for industrial WSN in IoT environment” [20]. WSN have been employed to collect data in many industrial applications. As an emerging technology, IoT emerges as a result of rapid advances

1551-3203 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

1414

in WSN technology. WSN are well suited for industrial data acquisition. This paper proposes a new design method on a reconfigurable smart sensor interface for industrial WSN in IoT environment. The device integrates the newest CPLD technology and the standard of IEEE 1451.2 intelligent sensor specification. Cloud computing is an important component of the backbone of the IoT. In the paper entitled “IoT and cloud computing in automation of assembly modeling systems” [21], IoT and cloud computing are proposed to help a conventional assembly modeling system evolve into an advanced system, which is capable of deal with complexities. Assembly modeling for aircraft engines is used as an example to illustrate the effectiveness of this novel IoTbased system. The next paper in the cluster of cloud computing is the paper entitled “CCIoT-CMfg: Cloud computing and Internet of Things based cloud manufacturing service system” [22]. In this paper, a cloud computing and IoT-based cloud manufacturing service system called CCIoT-CMfg and its architecture are proposed, and the relationship among IoT, cloud computing, and CMfg is analyzed. The technology for realizing the CCIoT-CMfg is proposed. The third paper in this cluster is the paper entitled “An IoT oriented data storage framework in cloud computing platform” [23]. This paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework’s effectiveness. The emergence of the IoT not only refers to the ability to identify physical objects but also many types of virtual objects including services. Such identification plays a crucial role in service workflows. For a service workflow to succeed, it requires the composition of services in which requirements must be satisfied. In the cluster of service computing, the paper entitled “A new approach for compliance checking in service workflows” proposes a new algorithm based on matrix multiplication which significantly reduces the cost caused by time complexity [24]. Experiments are conducted to evaluate and compare the performance of these algorithms. In a service-oriented IoT deployment, it is difficult to make consensus decisions for services at different IoT edge nodes, where available information might be insufficient or overloaded. In the paper entitled “A distributed consensus algorithm for decision-making in service-oriented Internet of Things” [25], the authors discuss service composition in the IoT, through minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are first calculated locally and subsequently combined in an iterative fashion to reach global consensus. The distributed consensus method improves the robustness and trustiness of the decision process. The next paper is “Compliance checking for requirement-oriented service workflow interoperations” [26]. This paper presents the compliance checking algorithms for Service Workflow Specification (SWSpec) and Service Workflow Net (SWN) to support trustbased decisions for service workflow participation. The

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

application of SWSpec is illustrated using a Disaster Warning System. Finally, a prototype is developed to evaluate the performance of the proposed algorithms. In the paper entitled “A novel architecture for requirement-oriented participation decision in service workflows” [27], the authors present a new compliance checking algorithm to support trust-based decision for service workflow participation. The basic idea of IoT is the connectivity of things through Internet protocols in which things include devices such as RFID and sensors. Heterogeneous device services generated by various devices in different contexts may prevent users from using device services efficiently. This may hinder the development of IoT. In the paper entitled “User interoperability with heterogeneous IoT devices through transformation” [28], the authors address the problems appeared in device discovery and device interaction. It devises a user interoperability framework (UIF) to enable device users to interoperate with heterogeneous devices of different contexts with consistent syntax and semantics. In this framework, a new separation strategy is provided, a device representation method for real, common, and virtual devices is devised, and a device transformability model is proposed to guarantee the proper transformation of device syntax and semantics. To demonstrate the correctness of UIF, a UIF prototype is implemented and several experiment methods are compared to determine which one should be adopted as semantic relatedness computing tools in device discovery for device users and in common device publishing for device providers. IoT contains a large number of different devices and heterogeneous networks, which makes it difficult to satisfy different quality of service (QoS) requirements. In the paper entitled “QoS-aware scheduling of services-oriented Internet of Things” [29], the authors proposed a three-layer QoS scheduling model for service-oriented IoT. At the application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At the network layer, the model aims at dealing with scheduling in heterogeneous networks environment. At the sensing layer, the information acquisition and resource allocation scheduling for different services are handled. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs. In the paper entitled “Cloud service negotiation in IoT environment: A mixed approach” [30], the authors propose a mixed approach for cloud service negotiation, which is based on the “game of chicken.” In particular, if it is uncertain about the strategy of the counterpart, it is better to mix concession and tradeoff strategies in negotiation. The results show that a mixed negotiation approach can achieve a higher utility than a concession one, while causing fewer failures than a tradeoff one. In the paper entitled “An interactive trust model for application market of the Internet of Things” [31], an interactive trust model (ITM) is proposed based on interaction between application market and end users. In this model, application trustworthiness (AT) is quantitatively evaluated by the similarity between the application’s behavior and the behavior expected by the user. In the paper entitled “CLOUDQUAL: A quality model for cloud services” [32], the authors take a service perspective, and propose a quality model named CLOUDQUAL for cloud

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

services. It is a model with quality dimensions and metrics that target general cloud services. CLOUDQUAL contains six quality dimensions, i.e., usability, availability, reliability, responsiveness, security, and elasticity, of which usability is subjective, whereas the others are objective. The first paper in the cluster of IoT application in manufacturing, entitled “Internet of Things for enterprise systems of modern manufacturing” [33], the objective is to investigate the impact of emerging IoT on manufacturing. The future research directions in this area are discussed. In the paper entitled “IoT based intelligent perception and access of manufacturing resource towards cloud computing” [34], the applications of IoT technologies in CMfg have been investigated. The classification of manufacturing resources and services are presented, as well as their relationships. In the paper entitled “IoT-based configurable information service platform for product life cycle management” [35], a configurable information service platform is proposed for developing IoT-based applications. Based on the proposed model, information encapsulating, composing, discomposing, transferring, tracing, and interacting in product lifecycle management can be realized. Combining ontology and RESTful service, the platform provides support for both data integration and intelligent interaction. Advances in IoT present enormous potential for future healthcare. In the paper entitled “IoT based smart rehabilitation system” [36], the authors present an ontology-based automating design methodology for smart rehabilitation system in IoT. Ontology aids computers further understand the symptoms and medical resources, which is helpful for creating rehabilitation strategy and reconfigure medical resources quickly and automatically, according to patients’ specific requirements. Meanwhile, IoT provides an effective platform to interconnect all the resources and provides immediate information interaction. Experiments and clinical trial demonstrate the feasibility and effectiveness of the proposed method. In the paper entitled “Ubiquitous data accessing method in IoT-based information system for emergency medical services” [37], a semantic data model is proposed to store and interpret IoT data. Then, a resource-based data accessing method (UDA-IoT) is designed to acquire and process IoT data ubiquitously to improve the accessibility to IoT data resources. Finally, an IoT-based system for emergency medical services is presented to demonstrate how to collect, integrate, and interoperate IoT data in order to provide support to emergency medical services. The result shows that the resource-based IoT data accessing method is effective in distributed heterogeneous data environment for timely and ubiquitously supporting data accessing in cloud and mobile computing platform. The advances in cloud computing and IoT have provided a promising opportunity to resolve the challenges in transportation systems. In the paper entitled “Developing vehicular data cloud services in the IoT environment” [38], the authors present a novel multi-layered vehicular data cloud platform by using cloud computing and IoT technologies. Two innovative vehicular data cloud services for vehicle warranty analysis in the IoT environment are presented: 1) intelligent parking cloud service; and 2) vehicular data mining cloud service. Climate change and environmental monitoring and management have received much attention in recent years. In the paper entitled “An integrated

1415

system for regional environmental monitoring and management based on Internet of Things” [39], the authors introduced a novel system that combines IoT, cloud computing, geoinformatics (RS, GIS, and GPS), and e-science for environmental monitoring and management, with a case study on regional climate change and its ecological effects. Multi-sensors and web services were used to collect data and other information, both public networks and private networks were used to access and transport mass data and other information. The key technologies and tools mainly include IoT. We hope that this special section will serve our readers as an avenue to gain a new perspective on IoT. We are grateful to the many individual reviewers who worked with us so diligently. REFERENCES [1] S. Li, L. Xu, T. Tryfonas, and G. Oikonomou, “The Internet of Things: A survey,” Inf. Syst. Frontiers, to be published, doi: 10.1007/s10796-0149492-7. [2] S. Li, L. Xu, and X. Wang, “Compressed sensing signal and data acquisition in wireless sensor networks and Internet of Things,” IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2177–2186, Nov. 2013. [3] ITU, “The Internet of Things,” International Telecommunication Union (ITU) Internal Report, Geneva, Switzerland, 2005. [4] L. Xu, “Enterprise systems: State-of-the-art and future trends,” IEEE Trans. Ind. Informat., vol. 7, no. 4, pp. 630–640, Nov. 2011. [5] L. Li, R. Ge, S. Zhou, and R. Valerdi, “Guest editorial integrated healthcare information systems,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 4, pp. 515–517, Jul. 2012. [6] L. Li, “Technology designed to combat fakes in the global supply chain,” Bus. Horiz., vol. 56, no. 2, pp. 167–177, 2013. [7] M. Kataev, L. Bulysheva, A. Emelyanenko, and V. Emelyanenko, “Enterprise systems in Russia: 1992-2012,” Enterp. Inf. Syst., vol. 7, no. 2, pp. 169–186, 2013. [8] Z. Zhou, R. Valerdi, and S. Zhou, “Guest editorial special section on enterprise systems,” IEEE Trans. Ind. Informat., vol. 8, no. 3, p. 630, Aug. 2012. [9] Q. Li, Z. Wang, W. Li, J. Li, C. Wang, and R. Du, “Applications integration in a hybrid cloud computing environment: Modelling and platform,” Enterp. Inf. Syst., vol. 7, no. 3, pp. 237–271, 2013. [10] L. Ren, L. Zhang, F. Tao, X. Zhang, Y. Luo, and Y. Zhang, “A methodology towards virtualisation-based high performance simulation platform supporting multidisciplinary design of complex products,” Enterp. Inf. Syst., vol. 6, no. 3, pp. 267–290, 2012. [11] L. Li, “Effects of enterprise technology on supply chain collaboration: Analysis of China-linked supply chain,” Enterp. Inf. Syst., vol. 6, no. 1, pp. 55–77, 2012. [12] C. Wang, “Advances in information integration infrastructures supporting multidisciplinary design optimization,” Enterp. Inf. Syst., vol. 6, no. 3, pp. 265–285, 2012. [13] Y. Li, M. Hou, H. Liu, and Y. Liu, “Towards a theoretical framework of strategic decision, supporting capability and information sharing under the context of Internet of Things,” Inf. Technol. Manage., vol. 13, no. 4, pp. 205–216, 2012. [14] W. Viriyasitavat, L. Xu, and A. Martin, “SWSpec: The requirements specification language in service workflow environments,” IEEE Trans. Ind. Informat., vol. 8, no. 3, pp. 631–638, Aug. 2012. [15] S. Li, L. Xu, X. Wang, and J. Wang, “Integration of hybrid wireless networks in cloud services oriented enterprise information systems,” Enterp. Inf. Syst., vol. 6, no. 2, pp. 165–187, 2012. [16] L. Xu, W. Viriyasitavat, P. Ruchikachorn, and A. Martin, “Using propositional logic for requirements verification of service workflow,” IEEE Trans. Ind. Informat., vol. 8, no. 3, pp. 639–646, Aug. 2012. [17] N. Niu, L. Xu, and Z. Bi, “Enterprise information systems architectureanalysis and evaluation,” IEEE Trans. Ind. Informat., vol. 9, no. 4, pp. 2147– 2154, Nov. 2013. [18] S. Fang, L. Xu, H. Pei, and Y. Liu, “An integrated approach to snowmelt flood forecasting in water resource management,” IEEE Trans. Ind. Informat., vol. 10, no. 1, pp. 548–558, Feb. 2014. [19] J. Guo, L. Xu, G. Xiao, and Z. Gong, “Improving multilingual semantic interoperation in cross-organizational enterprise systems through concept disambiguration,” IEEE Trans. Ind. Informat., vol. 8, no. 3, pp. 647–658, Aug. 2012.

1416

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 10, NO. 2, MAY 2014

[20] Q. Chi, H. Yan, C. Zhang, Z. Pang, and L. Xu, “A reconfigurable smart sensor interface for industrial WSN in IoT environment,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1417–1425, 2014. [21] C. Wang, Z. Bi, and L. Xu, “IoT and cloud computing in automation of assembly modeling system,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1426–1434, 2014. [22] F. Tao, Y. Cheng, L. Xu, L. Zhang, and B. Li, “CCIoT-CMfg: Cloud computing and Internet of Things based cloud manufacturing service system,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1435–1442, 2014. [23] L. Jiang, L. Xu, H. Cai, Z. Jiang, F. Bu, and B. Xu, “An IoT oriented data storage framework in cloud computing platform,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1443–1451, 2014. [24] W. Viriyasitavat, L. Xu, and W. Viriyasitavat, “A new approach for compliance checking in service workflows,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1452–1460, 2014. [25] S. Li, G. Oikonomou, T. Tryfonas, T. Chen, and L. Xu, “A distributed consensus algorithm for decision-making in service-oriented Internet of Things,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1461–1468, 2014. [26] W. Viriyasitavat, L. Xu, and W. Viriyasitavat, “Compliance checking for requirement-oriented service workflow interoperations,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1469–1477, 2014. [27] L. Xu and W. Viriyasitavat, “A novel architecture for requirement-oriented participation decision in service workflows,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1478–1485, 2014. [28] G. Xiao, J. Guo, L. Xu, and Z. Gong, “User interoperability with heterogeneous IoT devices through transformation,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1486–1496, 2014. [29] L. Li, S. Li, and S. Zhao, “QoS-aware scheduling of services-oriented Internet of Things,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1497– 1505, 2014. [30] X. Zheng, P. Martin, K. Brohman, and L. Xu, “Cloud service negotiation in IoT environment: A mixed approach,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1506–1515, 2014. [31] K. Kang, Z. Pang, L. Xu, L. Ma, and C. Wang, “An interactive trust model for application market of the Internet of Things,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1516–1526, 2014. [32] X. Zheng, P. Martin, K. Brohman, and L. Xu, “CLOUDQUAL: A quality model for cloud services,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1527–1536, 2014. [33] Z. Bi, L. Xu, and C. Wang, “Internet of Things for enterprise systems of modern manufacturing,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1537–1546, 2014. [34] F. Tao, Y. Zuo, L. Xu, and L. Zhang, “IoT based intelligent perception and access of manufacturing resource towards cloud manufacturing,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1547–1557, 2014. [35] H. Cai, L. Xu, B. Xu, C. Xie, S. Qin, and L. Jiang, “IoT-based configurable information service platform for product lifecycle management,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1558–1567, 2014. [36] Y. Fan, Y. Yin, L. Xu, Y. Zeng, and F. Wu, “IoT based smart rehabilitation system,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1568–1577, 2014. [37] B. Xu, L. Xu, H. Cai, C. Xie, J. Hu, and F. Bu, “Ubiquitous data accessing method in IoT-based information system for emergency medical services,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1578–1586, 2014. [38] W. He, G. Yan, and L. Xu, “Developing vehicular data cloud services in the IoT environment,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1587–1595, 2014. [39] S. Fang, L. Xu, Y. Zhu, J. Ahati, H. Pei, J. Yan et al., “An integrated system for regional environmental monitoring and management based on Internet of Things,” IEEE Trans. Ind. Informat., vol. 10, no. 2, pp. 1596–1605, 2014.

ZU DE ZHOU, Guest Editor China Hubei Key Laboratory of Digital Manufacturing Wuhan 430070, China

LI WANG, Guest Editor Beijing University of Aeronautics and Astronautics Beijing 100191, China

Zu De Zhou is the Founding Director of China Hubei Key Laboratory of Digital Manufacturing, Hubei, China. He was the Past President of the Wuhan University of Technology, Wuhan, China. He held Visiting Professor Appointment at the National University of Singapore, Singapore, and the University of Hong Kong, Hong Kong, China. His research interests include computer numerical control, intelligent control, digital manufacturing, reliability and fault diagnosis of manufacturing systems, and enterprise systems. He has published extensively in journals including the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS. He is the author of the book entitled Fundamentals of Digital Manufacturing Science (Springer Series in Advanced Manufacturing) in English and Dutch translation editions. Dr. Zhou has been serving as the Guest Associate Editor for journals including the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS and Computers & Operations Research. Ricardo Valerdi (M’95) received the B.S. degree in electrical engineering from the University of San Diego, San Diego, CA, USA, in 1995, and the M.S. and Ph.D. degrees in industrial and systems engineering from the University of Southern California (USC), Los Angeles, CA, in 2002 and 2005, respectively. He is an Associate Professor with the University of Arizona, Tucson, AZ, USA, and was a Research Associate at the Massachusetts Institute of Technology (MIT), Cambridge, Cambridge, MA, USA; a Visiting Associate at the Center for Systems and Software Engineering at USC; and a Senior Member of the Technical Staff at the Aerospace Corporation. Formerly, he was a Systems Engineer at Motorola and at General Instrument Corporation. Dr. Valerdi is a Member of the International Council on Systems Engineering (INCOSE) and served on its Board of Directors. He is the founding Co-Editor-inChief of the Journal of Enterprise Transformation published by Taylor & Francis. Shang-Ming Zhou (M’01) received the B.Sc. degree in mathematics from Liaocheng University, Shandong, China, in 1989; the M.Sc. degree in applied mathematics from Beijing Normal University, Beijing, China, in 1992; and the Ph.D. degree in computer science from the University of Essex, Colchester, U.K., in 2006. He is currently a Senior Lecturer with the College of Medicine, Swansea University, Swansea, U.K. His research interests include industrial informatics, intelligent control, and computational intelligence and applications. He has published extensively in journals including the IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING and the IEEE TRANSACTIONS ON FUZZY SYSTEMS. Dr. Zhou has served as a Member of Program Committees for more than 40 international conferences and general chairs of numerous international conferences, and as a Guest Editor for the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, and others. Li Wang received the Ph.D. degree in management science from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 2005. Currently, he is an Associate Professor with the School of Economics and Management, Beijing University of Aeronautics and Astronautics. His research interests include heuristic algorithms, decision support systems, knowledge management, systems simulation, project management, logistics, and enterprise systems. He has published extensively in journals including the IEEE TRANSACTIONS ON

RICARDO VALERDI, Guest Editor Department of Systems and Industrial Engineering The University of Arizona Tucson, AZ 85721-0020 USA SHANG-MING ZHOU, Guest Editor Swansea University Swansea SA2 8PP, U.K.

INDUSTRIAL INFORMATICS.