A Notification Management Architecture for Service Co-evolution in the Internet of Things

Tran, Huu Tam; Baraki, Harun; Kuppili, Ramaprasad; Taherkordi, Amir; Geihs, Kurt

doi:10.1109/mesoca.2016.8

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…This section puts existing approaches that address adaptation of IoT systems in perspective of our proposed architecture. Evolution Agents (EVAs) [14] consist of a notification management architecture exploiting a semantic service registry based on ontologies. Services are described and registered in the registry using semantic web ontologies.…”

Section: Related Workmentioning

confidence: 99%

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A Research Agenda for IOT Adaptive Architectures

et al. 2018

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Adaptation is very important in IOT systems, due to their continuously changing environments. Changes may come from different elements of the architecture underlying an IOT system. Existing literature pays special attention to changes in the Service layer using evolution agents or context aware approaches to manage adaptations to said changes. In this paper, we elaborate on eight challenges that developers face when building adaptive IOT systems. Such challenges take into account changes at the Services layer, but also in the Middleware and Physical layers. These challenges serve us as a research agenda to foster IOT technology. As a starting point, we design an architecture to deal with the posited challenges. Various of the architectural components are inspired on a reference architecture, and complemented by new components to manage dynamic adaptations in response to the identified challenges. Preliminary experiments provide an initial insight about the feasibility and/or impact of our adaptive architecture.

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Section: Related Workmentioning

confidence: 99%

“…Whenever a service changes, the EVA associated with the service reports the change to all dependent services; triggering their EVAs to evolve accordingly. [14] describe a scenario where several IoT devices from different suppliers provide different services. Each service in the environment is equipped with an EVA.…”

Section: Related Workmentioning

confidence: 99%

A Research Agenda for IOT Adaptive Architectures

et al. 2018

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Self‐adaptation and distributed knowledge‐based service ecosystem evolution

Wang

Feng

Huang

et al. 2021

Concurrency and Computation

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Web services (or Web APIs) on the Internet tends to encounter various unexpected runtime failures because of their dynamicity and distribution. Self-adaptation technologies for the service-based business process can effectively repair runtime failures and improve its success rate. However, the same failures may occur on subsequent invocations because relevant processes do not evolve after failures. This makes the response time of the business processes too long. We proposed a self-adaptation and distributed knowledge-based evolution model (SDKEM) to guarantee business processes' stabilities, that is, low failure rates and stable response time. SDKEM adopts a service knowledge base (SKB) to organize services from a provider and uses bridge rules to eliminate semantic conflicts among multiple distributed SKBs. It can automatically trigger the evolution of a service ecosystem through the designed self-adaptation mechanism. We adopt the "survival of the fittest" principle for crucial elements in the ecosystem during evolution so that ultimately, service-based processes and services with high stability remain. Experiments show that, with the developed evolution mechanism, runtime failures of business processes significantly reduce. In most cases, their response time and success rates are comparable to those under the running situation where no runtime failure occurs, meaning the runtime failures within a service-based process are automatically repaired.

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