DPWSim: A Devices Profile for Web Services (DPWS) Simulator

Han, Son N.; Lee, Gyu Myoung; Crespi, Noël; Luong, Nguyen Van; Heo, Kyoungwoo; Brut, Mihaela; Gatellier, Patrick

doi:10.1109/jiot.2014.2388131

Cited by 17 publications

(12 citation statements)

References 11 publications

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“…Some IoT research has proposed solutions for service discovery, such as the use of P2P overlay [22], device profile for web services (DPWS) [23], social networking for devices [24], and middleware [10]. Service discovery has also been addressed in other IoT related fields, including pervasive computing [25], ambient intelligence [26], and machine-to-machine communication [27].…”

Section: B Cascading Failure In Iot Environmentmentioning

confidence: 99%

Analysis of Large-Scale Service Network Tolerance to Cascading Failure

Lhaksmana

Murakami

Ishida

2016

IEEE Internet Things J.

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The future Internet will be populated with a massive number of cooperating services due to the rapid growth of publicly available services and the adoption of service-oriented computing (SOC) into the Internet of Things (IoT). The adoption of SOC enables combining the functionalities of smart devices as combining services by means of service composition. These cooperating services form a large-scale service network where the nodes and the links represent services and the dependency between services, respectively. The dependency between services potentially causes cascading failure, where the failure of a service propagates to its dependent services. Due to the lack of research in this type of cascading failure, we analyzed cascading failure in service networks for different topology and different degree of service interdependency. We found that the number of cascading failure is somewhat linear to the average number of required services, and decays exponentially over the average number of alternate services. The latter suggests that cascading failure tolerance can be significantly improved by adding few alternate services to each required service if the average number of alternate services is currently low. In addition, we also found that scale-free topology provides better tolerance, subsequently followed by exponential and random topology.

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Section: B Cascading Failure In Iot Environmentmentioning

confidence: 99%

Analysis of Large-Scale Service Network Tolerance to Cascading Failure

Lhaksmana

Murakami

Ishida

2016

IEEE Internet Things J.

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“…In the case of [ 30 ], the information privacy during transmission is ensured by using the DPWS implementation [ 61 ] of the WS-Security standard. The system for hypertension management proposed in [ 31 ] has mobile base units (MBUs), in which the data taken by the sensors are processed.…”

Section: Analysis From the Perspective Of Security Implementationsmentioning

confidence: 99%

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

Avila

Sanmartin

Molinares

et al. 2017

Sensors

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This article makes a literature review of applications developed in the health industry which are focused on patient care from home and implement a service-oriented (SOA) design in architecture. Throughout this work, the applicability of the concept of Internet of Things (IoT) in the field of telemedicine and health care in general is evaluated. It also performs an introduction to the concept of SOA and its main features, making a small emphasis on safety aspects. As a central theme, the description of different solutions that can be found in the health industry is developed, especially those whose goal is health care at home; the main component of these solutions are body sensor networks. Finally, an analysis of the literature from the perspectives of functionalities, security implementation and semantic interoperability is made to have a better understanding of what has been done and which are probable research paths to be studied in the future.

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“…We provide an example to explain the reason. Suppose there are four service items and a user u with response time vector (2,4,3,9). We make an assumption that we do not know response time value 9 of the fourth service item, i.e., we treat it as a missing value and the new experimental response time vector of u is (2,4,3, null).…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…Every value of (4,8,6,18) is double of that of (2,4,3,9). Similarity between (2,4,3,9) and (4,8,6,18) is 1 and similarity between (2,4,3, null) and (4,8,6,18) is 0.478 by the calculation of formula (1). and .…”

Section: B the Densification Of User-item Matrixmentioning

confidence: 99%

“…To shield complexity of knowledge representation and communication interface, web service standards are implemented on relatively resource-constrained devices by simplification and optimization. For example, device profiles for web services (DPWS) [2], as a subset of web service standards, is implemented in many embedded devices for seamless service interaction. Second, in the aspect of IoT infrastructure, Service-Oriented-Architecture (SOA) is widely accepted as IoT middleware solutions to solve issues of abstracting device functionalities and communication capabilities [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

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In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

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DPWSim: A Devices Profile for Web Services (DPWS) Simulator

Cited by 17 publications

References 11 publications

Analysis of Large-Scale Service Network Tolerance to Cascading Failure

Analysis of Large-Scale Service Network Tolerance to Cascading Failure

Applications Based on Service-Oriented Architecture (SOA) in the Field of Home Healthcare

Response Time Prediction of IoT Service Based on Time Similarity

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