Fog Networking for Machine Health Prognosis: A Deep Learning Perspective

  • Saad Bin Qaisar
  • Muhammad UsmanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10408)


Ensuring machine health and predicting failures beforehand is of utmost importance. Fog networking is an emerging paradigm of computing. Advances in neural networks research and their benefits in accurate classification make them best candidate for application to prognostics. Ubiquity of smartphones, wearables and sensors is giving rise to tremendous amount of data necessitating intelligent data analytics at the data center. Various researches have focused on compression of large deep models for inferencing on smart devices in order to seek answers to issues like reducing the enormity of computation, decreasing the power and memory requirements on these devices. At this point in time, state of the art in inference such as recurrent neural networks (RNN) seem impossible to be implemented on resource constrained devices. Cloud based inference incurs cost in terms of power as more energy is depleted to access infrastructure via technologies such as 3g/4g networks while also witnessing latency issues due to network congestion. A few real world situations offload inferencing to network edge as it is more beneficial to solve many issues like security, privacy of users, redundancy rather than uploading enormous volume of data on cloud. The paradigm, known as Fog Computing, is a hot topic in research facilitating edge analytics by overcoming constraints and shortcoming of cloud enabling quality of service (QoS) required in some real world applications such as Industrial Internet of Things (IoT). Fog acts as a medium between edge device and cloud. In this paper, we propose a hybrid approach of using Fog in conjunction with deep neural nets for inferencing. We propose how neural nets exploited by Fog/Cloudlet can provide computational and storage services to nearby devices and it will also put a barrier by filtering the voluminous data to a reduced form for cloud.


CNN RNN LSTM Fog computing 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.National University of Sciences and TechnologyIslamabadPakistan

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