DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information

Xing, Tianwei; Vilamala, Marc Roig; García, Luis Antonio Ribot; Cerutti, Federico; Kaplan, Lance; Preece, Alun; Srivastava, Mani

doi:10.1109/smartcomp.2019.00034

Cited by 16 publications

(7 citation statements)

References 19 publications

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“…A CEP and ML-based approach to support fault-tolerance of IoT systems is proposed in work [30]. In [19], a framework based on CEP and deep learning is implemented, and an unattended bag computer vision task is illustrated to evaluate its feasibility. Unlike other works, CEP is used to schedule distributed ML training on Raspberry Pi in [35].…”

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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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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“…Several works [21,12,22] have shown that Deep Networks are well-suited for event detection and processing tasks in emerging IoT settings. This is of key importance because these models can fuse and process raw temporal data from distributed sensors over long periods of time to provide accurate inference [12]. In [22], Long Short Term Memory (LSTM) networks are proposed for predictive maintenance on distributed turbofan machines.…”

Section: Distributed Event Detectionmentioning

confidence: 99%

“…Their model is able to capture faulty conditions based on the history of the systems being monitored from large sets of sensor data. Work by [12] characterizes complex events, often made up of a series of primitive events and employ a Deep Network approach that is coupled with a state-based event detector. [21] propose RNN-based architecture in which sensor nodes communicate to solve distributed event detection tasks.…”

Section: Distributed Event Detectionmentioning

confidence: 99%

“…Deep Networks have achieved notable successes in domains characterized by massive amounts of data owing to their versatility, inference and predictive capacity [9]. In IoT applications, Deep Networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have achieved state-of-the-art performance on computer vision tasks, (i.e., image classification [10] and action recognition [11]) and timeseries prediction tasks, (i.e., event detection [12] and target tracking [13]).…”

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confidence: 99%

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Learning distributed communication and computation in the IoT

Abudu

Markham

2020

Computer Communications

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In distributed, cooperative Internet of Things (IoT) settings, sensing devices must communicate in a resource-aware fashion to achieve a diverse set of tasks, (i.e., event detection, image classification). In such settings, we continue to see a shift from reliance on cloud-centric to edge-centric architectures for data processing, inference and actuation. Distributed edge inference techniques address real-time, connectivity, network bandwidth and latency challenges in spatially distributed IoT applications. Achieving efficient, resource-aware communication in such systems is a longstanding challenge. Many current approaches require complex, hand-engineered communication protocols. In this paper, we present a novel scalable, data-driven and communication-efficient Convolutional Recurrent Neural Network (C-RNN) framework for distributed tasks. We provide empirical and systematic analyses of model convergence, node scalability and communication-cost based on dynamic network graphs. Further to this, we show that our framework is able to solve distributed image classification tasks via automatically learned communication.

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“…It has already been seen in [27] that context awareness has advantages for negotiation agents. Applications using probabilistic reasoning methods such as ProbLog [32] could incorporate information from various sensors, such as demonstrated by [39], to be incorporated in both bidding strategies and opponent modelling. This could provide potentially huge advantages to agents that are able to apply these concepts well.…”

Section: An Rau Implementation For Better Predictability Of Outcome A...mentioning

confidence: 99%

The current state of automated negotiation theory: a literature review

Vente,

Kimmig,

Preece

et al. 2020

Preprint

Self Cite

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Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting. Automated negotiation has already seen increased usage in fields such as e-commerce and power distribution in smart girds, and recent advancements in opponent modelling have proven to deliver better outcomes. However, significant barriers to more widespread adoption remain, such as lack of predictable outcome over time and user trust. Additionally, there have been many recent advancements in the field of reasoning about uncertainty, which could help alleviate both those problems. As there is no recent survey on these two fields, and specifically not on their possible intersection we aim to provide such a survey here.

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DeepCEP: Deep Complex Event Processing Using Distributed Multimodal Information

Cited by 16 publications

References 19 publications

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

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

Learning distributed communication and computation in the IoT

The current state of automated negotiation theory: a literature review

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