Towards a Generalized Approach for Deep Neural Network Based Event Processing for the Internet of Multimedia Things (bibtex)
by Asra Aslam, Edward Curry
Abstract:
Event processing systems serve as a middleware between the Internet of Things (IoT) and the application layer by allowing users to subscribe to events of interest. Due to the increase of multimedia IoT devices (i.e. traffic camera), the types of events created are shifting more towards unstructured (multimedia) data. Therefore, there is a growing demand for efficient utilization of effective processing of streams of both structured events (i.e. sensors) and unstructured multimedia events (i.e. images, video, audio). However, current event processing engines have limited or no support for unstructured event types. In this paper, we described a generalized approach that can handle Internet of Multimedia Things (IoMT) events as a native event type in event processing engines with high efficiency. The proposed system extends event processing languages with the introduction of operators for multimedia analysis of unstructured events and leverages a deep convolutional neural network based event matcher for processing of image events to extract features. Furthermore, we show that neural network based object detection models can be further optimized by leveraging subscription constraints to reduce time complexity while maintaining competitive accuracy. Our initial results demonstrate the feasibility of a generalized approach towards IoMT-based event processing. Application areas for generalized event processing include traffic management, security, parking, supervision activities, and to enhance the quality of life within smart cities.
Reference:
Asra Aslam, Edward Curry, "Towards a Generalized Approach for Deep Neural Network Based Event Processing for the Internet of Multimedia Things", In IEEE Access, vol. 6, pp. 25573-25587, 2018.
Bibtex Entry:
@article{Aslam2018,
abstract = {Event processing systems serve as a middleware between the Internet of Things (IoT) and the application layer by allowing users to subscribe to events of interest. Due to the increase of multimedia IoT devices (i.e. traffic camera), the types of events created are shifting more towards unstructured (multimedia) data. Therefore, there is a growing demand for efficient utilization of effective processing of streams of both structured events (i.e. sensors) and unstructured multimedia events (i.e. images, video, audio). However, current event processing engines have limited or no support for unstructured event types. In this paper, we described a generalized approach that can handle Internet of Multimedia Things (IoMT) events as a native event type in event processing engines with high efficiency. The proposed system extends event processing languages with the introduction of operators for multimedia analysis of unstructured events and leverages a deep convolutional neural network based event matcher for processing of image events to extract features. Furthermore, we show that neural network based object detection models can be further optimized by leveraging subscription constraints to reduce time complexity while maintaining competitive accuracy. Our initial results demonstrate the feasibility of a generalized approach towards IoMT-based event processing. Application areas for generalized event processing include traffic management, security, parking, supervision activities, and to enhance the quality of life within smart cities.},
author = {Aslam, Asra and Curry, Edward},
doi = {10.1109/ACCESS.2018.2823590},
file = {:Users/ed/Dropbox/Work/Papers/publications/08331844.pdf:pdf},
issn = {2169-3536},
journal = {IEEE Access},
keywords = {Distributed Systems,Event-Based Systems,Internet of Multimedia Things,Internet of Things,Multimedia Stream Processing,Multimedia communication,Sensors,Smart Cities,Smart Environments,Smart cities,Streaming media},
pages = {25573--25587},
title = {{Towards a Generalized Approach for Deep Neural Network Based Event Processing for the Internet of Multimedia Things}},
url = {http://www.edwardcurry.org/publications/08331844.pdf},
volume = {6},
year = {2018}
}
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