Multimodal Complex Event Processing on Mobile Devices

Graubner, Pablo; Thelen, Christoph; Körber, Michael; Sterz, Artur; Salvaneschi, Guido; Mezini, Mira; Seeger, Bernhard; Freisleben, Bernd

doi:10.1145/3210284.3210289

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…There are several areas for future work. For example, extending INETCEP to run on the CCN-lite kernel module promises high performance benefits for our unified communication layer 9 when using the Linux kernel, similar to other work on CEP in the kernel [22]. Another promising area of future work is to generate an optimal operator graph, e.g., based on operator selectivity or by partitioning the operator graph by performing query optimization [12].…”

Section: Discussionmentioning

confidence: 99%

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INetCEP: In-Network Complex Event Processing for Information-Centric Networking

Luthra

Koldehofe

Höchst

et al. 2019

2019 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)

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Section: Discussionmentioning

confidence: 99%

“…Data Stream satisfies a query interest in PIT and it is a new packet (lines[22][23][24]. This means that the qname performs an operation on the data object contained in the Data Stream packet.…”

mentioning

confidence: 99%

INetCEP: In-Network Complex Event Processing for Information-Centric Networking

Luthra

Koldehofe

Höchst

et al. 2019

2019 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS)

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“…CEP systems address the pervasiveness of sensor networks by eiciently parsing streaming events from diferent sources against user-deined rules to detect desired patterns in the data. The work in [29] and [42] adapts CEP to leverage decentralized computing architectures. [12] proposed an CEP semantic structure that can operate in a distributed network.…”

Section: Cepmentioning

confidence: 99%

Towards Semantic Management of On-Device Applications in Industrial IoT

Ren

Anicic

Runkler

2022

ACM Trans. Internet Technol.

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The Internet of Things (IoT) is revolutionizing the industry. Powered by pervasive embedded devices, the Industrial IoT (IIoT) provides a unique solution for retrieving and analyzing data near the source in real-time. Many emerging techniques, such as Tiny Machine Learning (TinyML) and Complex Event Processing (CEP), are actively being developed to support decision making at the edge, shifting the paradigm from centralized processing to distributed computing. However, distributed computing presents management challenges, as IoT devices are diverse and constrained, and their number is growing exponentially. The situation is even more challenging when various on-device applications (so-called artifacts) are deployed across decentralized IoT networks. Questions to be addressed include how to discover an appropriate function, whether that function can be executed on a certain device, and how to orchestrate a cross-platform service. To tackle these challenges, we propose an approach for the scalable management of on-device applications among distributed IoT devices. By leveraging the W3C Web of Things (WoT), the capabilities of each IoT device, or more precisely, its interaction patterns, can be semantically expressed in a Thing Description (TD). In addition, we introduce semantic modeling of on-device applications to supplement an TD with additional information regarding applications on the device. Specifically, we demonstrate two examples of semantic modeling: neural networks (NN) and CEP rules. The ontologies are evaluated by answering a set of competency questions. By hosting the enriched semantic knowledge of the entire IoT system in a Knowledge Graph (KG), we can discover and interoperate edge devices and artifacts across the decentralized network. This can reduce fragmentation and increase the reusability of IoT components. We demonstrate the feasibility of our concept on an industrial workstation consisting of a conveyor belt and several IoT devices. Finally, the requirements for constructing an IoT semantic management system are discussed.

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“…While most CEP tools are designed for centralized cloud analysis, works like [16] try to extend the CEP to mobile devices to leverage decentralized architectures. In [24], a by-partitioning CEP pipeline is proposed to use both edge and cloud resources for stream processing.…”

Section: Related Workmentioning

confidence: 99%

The synergy of complex event processing and tiny machine learning in industrial IoT

Ren

Anicic

Runkler

2021

Proceedings of the 15th ACM International Conference on Distributed and Event-Based Systems

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Focusing on comprehensive networking, the Industrial Internetof-Things (IIoT) facilitates efficiency and robustness in factory operations. Various intelligent sensors play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. Complex event processing (CEP) and machine learning (ML) have been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts. In a traditional compute-centric paradigm, the raw field data are continuously sent to the cloud and processed centrally. As IIoT devices become increasingly pervasive, concerns are raised since transmitting such an amount of data is energy-intensive, vulnerable to be intercepted, and subjected to high latency. Decentralized on-device ML and CEP provide a solution where data is processed primarily on edge devices. Thus communications can be minimized. However, this is no mean feat because most IIoT edge devices are resource-constrained with low power consumption. This paper proposes a framework that exploits ML and CEP's synergy at the edge in distributed sensor networks. By leveraging tiny ML and µCEP, we now shift the computation from the cloud to the resource-constrained IIoT devices and allow users to adapt on-device ML models and CEP reasoning rules flexibly on the fly. Lastly, we demonstrate the proposed solution and show its effectiveness and feasibility using an industrial use case of machine safety monitoring. CCS CONCEPTS• Computing methodologies → Machine learning approaches; • Computer systems organization → Real-time systems; Sensor networks; • Information systems → Data streaming.

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Multimodal Complex Event Processing on Mobile Devices

Cited by 9 publications

References 21 publications

INetCEP: In-Network Complex Event Processing for Information-Centric Networking

INetCEP: In-Network Complex Event Processing for Information-Centric Networking

Towards Semantic Management of On-Device Applications in Industrial IoT

The synergy of complex event processing and tiny machine learning in industrial IoT

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