Robust Dynamic Service Composition in Sensor Networks

Geyik, Sahin Cem; Szymanski, Boleslaw K.; Zerfos, Petros

doi:10.1109/tsc.2012.26

Cited by 43 publications

(28 citation statements)

References 27 publications

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“…In [42], the authors present a graph-based formulation for modeling sensor In [43], authors present a distributed composition service to automate the re-organization process that can occur due to the inherit limitation of a WSN. …”

Section: Wireless Sensor Networkmentioning

confidence: 99%

“…Dar et al [47] undertakes the scalability and dynamicity issues of very-large scale IoT systems to propose an orchestration/choreography for composing services of smart objects and business services. Graph-based modeling framework [42] formulates the process of sensor service composition to deal with the dynamicity of WSNs. Han 594 et al [50] propose a context-aware service composition to dynamically adapt the composite process to the changes in several types of context in a building environment.…”

Section: Service-oriented Middleware and Compositionmentioning

confidence: 99%

“…A number of standards have been developed 42 and are being used in real-world deployments to support the service composition. However, the characteristics of IoT systems, such as resource-constraints and data/event-driven devices render some of the techniques devised for traditional Web service composition inadequate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Han

Khan

Lee

et al. 2016

Computer Standards & Interfaces

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Section: Wireless Sensor Networkmentioning

confidence: 99%

Section: Service-oriented Middleware and Compositionmentioning

confidence: 99%

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Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Han

Khan

Lee

et al. 2016

Computer Standards & Interfaces

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“…Suppose a WSN is deployed as a support system for a disaster relief effort. A monitoring system configured in such a scenario might use audio and video feeds produced by other services to provide surveillance of the area 1 Research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government.…”

Section: Introductionmentioning

confidence: 99%

“…The service configuration in such a scenario should not only consider input/output portability [1] but also other factors, such as energy cost and spatial relevancy of services to the area of interest. In such a scenario, services that are more relevant (e.g., have a larger sensing range in the mission area) are more useful than services that provide the same outputs but with lower relevancy.…”

Section: Introductionmentioning

confidence: 99%

Autonomous configuration of spatially aware sensor services in service oriented WSNs

Shah

Szymanski

Zerfos

et al. 2013

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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Abstract-Service-oriented Architectures (SOA) for Wireless Sensor Networks (WSNs) are an active research topic. Yet, autonomous configuration of services for real life constraints (spatio-temporal, input/output interoperability, policies, security etc.) is still a challenging problem. In this demonstration we describe the results of our research into the automated and intelligent configuration and composition of services for complex tasks. We present a service-oriented system capable of performing service configuration under spatial and relevancy constraints. It can configure services in one of the three following modes: distributed, centralized and hybrid. It also supports automatic reconfiguration in the event of service failures. This system uses a generic cost representation for services that may include spatial coverage of the services in an area of interest along with other service configuration cost metrics. We demonstrate our system using state-of-the-art emulation frameworks with a real life scenario.

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QoS‐aware big service composition using distributed co‐evolutionary algorithm

Dutta

Jatoth

Gangadharan

et al. 2021

Concurrency and Computation

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Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a reasonable time. In this paper, we design an efficient quality of service-aware big service composition methodology using a distributed co-evolutionary algorithm. In our proposed model, we develop a distributed NSGA-III for finding the optimal Pareto front and a distributed multi-objective Jaya algorithm for enhancing the diversity of solutions. The distributed co-evolutionary algorithm finds the near-optimal solution in a fast and scalable way. K E Y W O R D Sbig services, distributed co-evolutionary algorithm, distributed Jaya, distributed NSGA-III, quality of service, service composition INTRODUCTIONWith the onset of cloud computing, Internet of Things (IoT), and machine learning/ deep learning, manufacturing industries are undergoing a drastic change. Modern industries are incorporating these new technologies to achieve better efficiency in manufacturing products and services, with an improvement in quality control and a reduction in production costs. The Fourth Industrial Revolution (or Industry 4.0) is the ongoing automation of traditional manufacturing and industrial practices, using these modern smart technologies. Big services are collections of interrelated web services across the virtual and physical domains of Industry 4.0, processing Big Data. 1,2 Big services are integrated from various domains to develop a composite service that addresses the requirements of a customer. 3 Consider a fictitious company that manufactures electric vehicles. The company uses IoT, cloud computing, and machine learning/ deep learning to test for and predict possible failures and for tweaking or designing new engine parts, suspension units, and many more. Let us consider that the company has two divisions: design analysis unit and production unit. The design analysis unit analyzes the performances of the electric vehicles and suggests new design models. The lessons learned from the design analysis unit are incorporated in the design and manufacturing of electric vehicles by the production unit. Assume that both the design analysis unit and the production unit use a similar workflow. We can imagine two separate sets of service composition requirements: The design analysis unit requires high performance in terms of response time and throughput, high security, and with no restrictions on cost. The production unit has to keep the cost low and maintain the desirable quality of service (QoS) parameters and changes with respect to supply and demand throughout the year.In this scenario, we need to execute the service composition process repeatedly whenever there is a change in the requirements, with certain changes in the fitness function. This becomes computationally expensive as the number of services grows, being classified as NP-hard. 4,5 Co-evolutionary approaches offer promising solutions in this case and man...

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Robust Dynamic Service Composition in Sensor Networks

Cited by 43 publications

References 27 publications

Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Autonomous configuration of spatially aware sensor services in service oriented WSNs

QoS‐aware big service composition using distributed co‐evolutionary algorithm

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