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Enabling Edge Intelligence for Activity Recognition in Smart Homes

Lookup NU author(s): Professor Paul Watson

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This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2018.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

In recent years, Edge computing has emerged as a new paradigm that can reduce communication delays over the Internet by moving computation power from far-end cloud servers to be closer to data sources. It is natural to shift the design of cloud-based IoT applications to Edge-based ones. Activity recognition in smart homes is one of the IoT applications that can benefit significantly from such a shift. In this work, we propose an Edge-based solution for addressing the activity recognition problem in smart homes from multiple perspectives, including: architecture, algorithm design and system implementation. First, the Edge computing architecture is introduced and several critical management tasks are also investigated. Second, a realization of the Edge computing system is presented by using open source software and low-cost hardware. The consistency and scalability of running jobs on Edge devices are also addressed in our approach. Last, we propose a convolutional neural network model to perform activity recognition tasks on Edge devices. Preliminary experiments are conducted to compare our model with existing machine learning methods, and the results demonstrate that the performance of our model is promising.


Publication metadata

Author(s): Shaojun Z, Li W, Wu Y, Watson P, Zomaya A

Editor(s): Byrav Ramamurthy and Kui Ren

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 15th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS)

Year of Conference: 2018

Online publication date: 09/10/2018

Acceptance date: 21/08/2018

Date deposited: 10/10/2018

Publisher: IEEE

URL: http://www.scse.uestc.edu.cn/webs/mass2018/


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