On dependability of smart applications within edge-centric and fog computing paradigms

Korzun, Dmitry; Varfolomeyev, Aleksey; Shabaev, Anton; Kuznetsov, Vladimir

doi:10.1109/dessert.2018.8409185

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…This approach for automatic and scalable conformance testing is truly a key factor and must have mechanisms to improve IoT standardization and interoperability. As a future work, we consider to extend our configuration to fully distributed IoT environment [48], [49] and develop a testing solution to perform conformance testing in various places such as edge, fog and cloud.…”

Section: Discussionmentioning

confidence: 99%

AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications

et al. 2020

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Section: Discussionmentioning

confidence: 99%

AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications

et al. 2020

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“…Thus, applying lightweight filters allows filtering out many irrelevant frames, minimising the processing cost, and speeding up the query answering process. Edge-centric event analytics is another research area where data processing happens locally near the source [37]. Processing video data at IoT edge nodes can improve throughput, latency with reduced bandwidth consumption.…”

Section: B Visual Analyticsmentioning

confidence: 99%

Multimodal Event Processing: A Neural-Symbolic Paradigm for the Internet of Multimedia Things

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Salwala

Dhingra

et al. 2022

IEEE Internet Things J.

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Modern distributed computing infrastructure need to process vast quantities of data streams generated by a growing number of participants with information generated in multiple formats. With the Internet of Multimedia Things (IoMT) becoming a reality, new approaches are needed to process realtime multimodal event data streams. Existing approaches to event processing have limited consideration for the challenges of multimodal events, including the need for complex content extraction, increased computational and memory costs. The paper explores event processing as a basis for processing real-time IoMT data. The paper introduces the Multimodal Event Processing (MEP) paradigm, which provides a formal basis for native approaches to neural multimodal content analysis (i.e., computer vision, linguistics, and audition) with symbolic event processing rules to support real-time queries over multimodal data streams using the Multimodal Event Processing Language to express single, primitive multimodal, and complex multimodal event patterns. The content of multimodal streams is represented using Multimodal Event Knowledge Graphs to capture the semantic, spatial, and temporal content of the multimodal streams. The approach is implemented and evaluated within an MEP Engine using single and multimodal queries achieving near real-time performance with a throughput of ~30 fps and sub-second latency of 0.075-0.30 seconds for video streams of 30 fps input rate. Support for high input stream rates (45 fps) is achieved through content-aware load shedding techniques with a ~127X latency improvement resulting in only a minor decrease in accuracy.

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“…Korzun et al [6] consider two emerging IoT-enabled paradigms: Edge-centric Computing and Fog Computing. They are elaborating their potential for development of smart applications with focus on the dependability and using a mobile assistant for e-tourism as a reference application.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Graph theory methods for fog computing: A pseudo-random task graph model for evaluating mobile cloud, fog and edge computing systems

et al. 2022

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The development and efficient application of Fog Computing technologies necessitate complex tasks associated with the management and processing of large data sets, including the creation of low-level networks that guarantee the functioning of end devices within the Internet of Things (IoT) concept. This article presents the utilization of graph theory techniques to address these issues. The proposed graph model enables the determination of fundamental characteristics of systems, networks, and network devices in Fog Computing, including optimal features and methods to maintain them in a functioning condition. This work demonstrates how to create and personalize graph displays by adding labels or highlighting to the graph nodes and edges of pseudo-random task graphs. The task graphs, described and visualized in Matlab code, represent the computational work to perform and data transfer between tasks, expressed in Megacycles per second and kilobits/kilobytes of data, respectively. The task graphs can be applied in both single-user systems, where one mobile device accesses a remote server, and multi-user systems, where many users access a remote server through a wireless channel. This set can be utilized by researchers to evaluate cloud/fog/edge computing systems and computational offloading algorithms.

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On dependability of smart applications within edge-centric and fog computing paradigms

Cited by 13 publications

References 18 publications

AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications

AUTOCON-IoT: Automated and Scalable Online Conformance Testing for IoT Applications

Multimodal Event Processing: A Neural-Symbolic Paradigm for the Internet of Multimedia Things

Graph theory methods for fog computing: A pseudo-random task graph model for evaluating mobile cloud, fog and edge computing systems

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