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Guest Editorial: Business applications of Web of Things
DECSUP-12408; No of Pages 2 Decision Support Systems xxx (2013) xxx–xxx
Contents lists available at ScienceDirect
Decision Support Systems journal homepage: www.elsevier.com/locate/dss
Guest Editorial: Business applications of Web of Things
Internet of Things (IoT) refers to uniquely identifiable objects (things) and their virtual representations in an Internet-like structure. According to Gartner, over 50% of Internet connections are between IoT devices, whose number had reached over 15 billion in 2011 and are projected to be over 30 billion by 2020. The terms of Cyberphysical systems (CPS) and Web of Things (WoT) are also used interchangeably with IoT. However, WoT indicates a more advanced layer of networking, much like the difference between the Internet and the web. WoT is built upon technologies such as the Internet protocols, sensory technologies, sensors, and smart phones. Radio frequency identification (RFID) is often seen as a prerequisite for WoT. Once equipped with RFID tags, objects become identifiable and addressable, and can execute intelligent actions they are designed for, such as adaption to environments, self-configuration, self-maintenance, and self-repair. The massive amount of diverse and volatile data generated by such objects highlights the important role of WoT in the new computing era of big data and cloud. Although business applications of WoT have been built and tested in various areas such as the Internet refrigerator and the water pipe monitoring systems, many challenges remain in addressing not only technological aspects of WoT such as new communication protocols and RFID technologies but also issues such as trust, privacy, and security Thus, the need for more research on Web of Things is critical for wide adoption of WoT technologies in business. This special issue focuses on business applications and the enabling technologies of WoT. Out of a good number of submissions, four papers were eventually accepted after an initial screening and then two to three rounds of review and revisions. A brief overview of these papers is given next. In “A supply chain of things: The EAGLET Ontology for Highly Visible Supply Chains”, Guido Geerts and Daniel E. O'Leary emphasize that, RFID family techniques have facilitated identification of individual “things” and information sharing regarding their behavior through the supply chain, which generated the capabilities of a highly visible supply chain (HVSC), i.e., a supply chain where the location of arbitrary individual things can be determined at any point in time by appropriate supply chain partners. Their article further defines a critical component of HVSC, i.e., an ontology, to enhance visibility and interoperability of things along the supply chains, which is to leverage the availability of individual object identification information within the context of standard set of economic phenomena that supports multiple views in a range of data architectures. The authors also develop an EAGLET ontology (Event, AGent, Location, Equipment, and Things) to illustrate the primitives and structuring principles to facilitate management and
collaborative decision making about highly visible supply chain activities. In “Comparing the Cost-Efficiency of CoAP and HTTP in Web-ofThings Applications”, Tapio Levä, Oleksiy Mazhelis and Henna Suomi advocate that, with increasing volume of smart objects in the context of Web-of-Things, Constrained Application Protocol (CoAP) could be a possible alternative to HTTP. Since the cost-efficiency of CoAP will significantly impact its adoption, this article investigates the cost-benefits of CoAP and identifies the application scenarios where its use is likely to be economically justifiable. Furthermore, this article compares the costs of using CoAP and HTTP in the Web-of-Things applications, by identifying the components of the total cost of ownership (TCO) model for these applications and by studying the key factors affecting individual costs. The results suggest that the simpler hardware requirements of CoAP smart objects, as well as the lower communication overhead of the protocol and the resulting reduced power consumption lead to cost advantages in the Web-of-Things application scenarios. In “Human Mobility Discovering and Movement Intention Detection with GPS Trajectories”, Hua Yuan, Yu Qian, Rui Yang and Ming Ren propose a new approach to mine the interesting locations and the frequent travel sequences in a given geo-spatial region, in the context of Web-ofThings, by taking into account users' historic travel experiences as well as the correlation between locations. Moreover, not only a new partition method is proposed to divide trajectories into a set of line segments, but some clustering methods are conducted to aggregate the geographicalsimilar endpoints into groups for fixed territories detecting reason. Finally, an abstract path network is generated to show the link relations between the discovered fixed territories. This approach proposed in their article could be useful to help detect a user's frequent movement paths as well as fixed territories for better personalized geographical recommendation. In “Object Typicality for Effective Web of Things Recommendations,” by Yi Cai, Raymond Y.K. Lau, Stephen S.Y. Liao, Chunping Li, and Ho-Fung Leung studies the discovery and selection of smart things for improving the situation awareness of WoT applications. The authors argue that classical recommender systems are not adequate enough in handling the sparse recommendation space often seen in WoT recommendation applications. In addition, classical recommender systems have difficulty to scale up to cope with large number of things on the web and thus produce recommendations with big errors. In this paper, the authors propose a novel recommendation method based on the principle of object typicality verified in the field of cognitive psychology to address the aforementioned issues related to WoT recommendations. According to
0167-9236/$ – see front matter © 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.dss.2013.09.011
Please cite this article as: G. Chen, et al., Guest Editorial: Business applications of Web of Things, Decision Support Systems (2013), http:// dx.doi.org/10.1016/j.dss.2013.09.011
2
G. Chen et al. / Decision Support Systems xxx (2013) xxx–xxx
the sound principle of object typicality, the proposed computational method recommends things to a user based on their typicality with respect to a specific user interest group and the typicality of items that the user group is most interested in. Since the proposed system exploits data generalization by operating at item group and user group level, it is shown to be more effective than other baseline systems given sparse training data in the MovieLens benchmark dataset. The proposed system also significantly outperforms state-of-the-art recommender systems in terms of Mean Absolute Error (MAE) using the Netflix benchmark dataset, which simulates the large WoT recommendation space. The guest editors would like to thank the many researchers who provided timely and constructive reviews for this special issue; without their dedication and expertise, this special issue would not be possible. We also want to thank Professors Andrew Whinston and James Marsden for their kind guidance and strong support in the process of developing this special issue. We hope that the papers included in this special issue will help propagate more and better research in areas related to WoT.
Guoqing Chen Tsinghua University, China Paulo Goes University of Arizona, USA J. Leon Zhao City University of Hong Kong, China Harry Jiannan Wang* University of Delaware, USA *Corresponding author. Qiang Wei Tsinghua University, China
Available online xxxx
Please cite this article as: G. Chen, et al., Guest Editorial: Business applications of Web of Things, Decision Support Systems (2013), http:// dx.doi.org/10.1016/j.dss.2013.09.011
Contents lists available at ScienceDirect
Decision Support Systems journal homepage: www.elsevier.com/locate/dss
Guest Editorial: Business applications of Web of Things
Internet of Things (IoT) refers to uniquely identifiable objects (things) and their virtual representations in an Internet-like structure. According to Gartner, over 50% of Internet connections are between IoT devices, whose number had reached over 15 billion in 2011 and are projected to be over 30 billion by 2020. The terms of Cyberphysical systems (CPS) and Web of Things (WoT) are also used interchangeably with IoT. However, WoT indicates a more advanced layer of networking, much like the difference between the Internet and the web. WoT is built upon technologies such as the Internet protocols, sensory technologies, sensors, and smart phones. Radio frequency identification (RFID) is often seen as a prerequisite for WoT. Once equipped with RFID tags, objects become identifiable and addressable, and can execute intelligent actions they are designed for, such as adaption to environments, self-configuration, self-maintenance, and self-repair. The massive amount of diverse and volatile data generated by such objects highlights the important role of WoT in the new computing era of big data and cloud. Although business applications of WoT have been built and tested in various areas such as the Internet refrigerator and the water pipe monitoring systems, many challenges remain in addressing not only technological aspects of WoT such as new communication protocols and RFID technologies but also issues such as trust, privacy, and security Thus, the need for more research on Web of Things is critical for wide adoption of WoT technologies in business. This special issue focuses on business applications and the enabling technologies of WoT. Out of a good number of submissions, four papers were eventually accepted after an initial screening and then two to three rounds of review and revisions. A brief overview of these papers is given next. In “A supply chain of things: The EAGLET Ontology for Highly Visible Supply Chains”, Guido Geerts and Daniel E. O'Leary emphasize that, RFID family techniques have facilitated identification of individual “things” and information sharing regarding their behavior through the supply chain, which generated the capabilities of a highly visible supply chain (HVSC), i.e., a supply chain where the location of arbitrary individual things can be determined at any point in time by appropriate supply chain partners. Their article further defines a critical component of HVSC, i.e., an ontology, to enhance visibility and interoperability of things along the supply chains, which is to leverage the availability of individual object identification information within the context of standard set of economic phenomena that supports multiple views in a range of data architectures. The authors also develop an EAGLET ontology (Event, AGent, Location, Equipment, and Things) to illustrate the primitives and structuring principles to facilitate management and
collaborative decision making about highly visible supply chain activities. In “Comparing the Cost-Efficiency of CoAP and HTTP in Web-ofThings Applications”, Tapio Levä, Oleksiy Mazhelis and Henna Suomi advocate that, with increasing volume of smart objects in the context of Web-of-Things, Constrained Application Protocol (CoAP) could be a possible alternative to HTTP. Since the cost-efficiency of CoAP will significantly impact its adoption, this article investigates the cost-benefits of CoAP and identifies the application scenarios where its use is likely to be economically justifiable. Furthermore, this article compares the costs of using CoAP and HTTP in the Web-of-Things applications, by identifying the components of the total cost of ownership (TCO) model for these applications and by studying the key factors affecting individual costs. The results suggest that the simpler hardware requirements of CoAP smart objects, as well as the lower communication overhead of the protocol and the resulting reduced power consumption lead to cost advantages in the Web-of-Things application scenarios. In “Human Mobility Discovering and Movement Intention Detection with GPS Trajectories”, Hua Yuan, Yu Qian, Rui Yang and Ming Ren propose a new approach to mine the interesting locations and the frequent travel sequences in a given geo-spatial region, in the context of Web-ofThings, by taking into account users' historic travel experiences as well as the correlation between locations. Moreover, not only a new partition method is proposed to divide trajectories into a set of line segments, but some clustering methods are conducted to aggregate the geographicalsimilar endpoints into groups for fixed territories detecting reason. Finally, an abstract path network is generated to show the link relations between the discovered fixed territories. This approach proposed in their article could be useful to help detect a user's frequent movement paths as well as fixed territories for better personalized geographical recommendation. In “Object Typicality for Effective Web of Things Recommendations,” by Yi Cai, Raymond Y.K. Lau, Stephen S.Y. Liao, Chunping Li, and Ho-Fung Leung studies the discovery and selection of smart things for improving the situation awareness of WoT applications. The authors argue that classical recommender systems are not adequate enough in handling the sparse recommendation space often seen in WoT recommendation applications. In addition, classical recommender systems have difficulty to scale up to cope with large number of things on the web and thus produce recommendations with big errors. In this paper, the authors propose a novel recommendation method based on the principle of object typicality verified in the field of cognitive psychology to address the aforementioned issues related to WoT recommendations. According to
0167-9236/$ – see front matter © 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.dss.2013.09.011
Please cite this article as: G. Chen, et al., Guest Editorial: Business applications of Web of Things, Decision Support Systems (2013), http:// dx.doi.org/10.1016/j.dss.2013.09.011
2
G. Chen et al. / Decision Support Systems xxx (2013) xxx–xxx
the sound principle of object typicality, the proposed computational method recommends things to a user based on their typicality with respect to a specific user interest group and the typicality of items that the user group is most interested in. Since the proposed system exploits data generalization by operating at item group and user group level, it is shown to be more effective than other baseline systems given sparse training data in the MovieLens benchmark dataset. The proposed system also significantly outperforms state-of-the-art recommender systems in terms of Mean Absolute Error (MAE) using the Netflix benchmark dataset, which simulates the large WoT recommendation space. The guest editors would like to thank the many researchers who provided timely and constructive reviews for this special issue; without their dedication and expertise, this special issue would not be possible. We also want to thank Professors Andrew Whinston and James Marsden for their kind guidance and strong support in the process of developing this special issue. We hope that the papers included in this special issue will help propagate more and better research in areas related to WoT.
Guoqing Chen Tsinghua University, China Paulo Goes University of Arizona, USA J. Leon Zhao City University of Hong Kong, China Harry Jiannan Wang* University of Delaware, USA *Corresponding author. Qiang Wei Tsinghua University, China
Available online xxxx
Please cite this article as: G. Chen, et al., Guest Editorial: Business applications of Web of Things, Decision Support Systems (2013), http:// dx.doi.org/10.1016/j.dss.2013.09.011