Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.

Fog and IoT: An Overview of Research Opportunities

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

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Deep Learning for Health Informatics.

Deep learning for sensor-based activity recognition: A survey

What is Twitter

Internet of Things (IoT) brings more than an explosive proliferation of endpoints. It is disruptive in several ways. In this chapter we examine those disruptions, and propose a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing. In particular, we pay attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.

Fog Computing: A Platform for Internet of Things and Analytics

A variety of real-life mobile sensing applications are becoming available, especially in the life-logging, fitness tracking and health monitoring domains. These applications use mobile sensors embedded in smart phones to recognize human activities in order to get a better understanding of human behavior. While progress has been made, human activity recognition remains a challenging task. This is partly due to the broad range of human activities as well as the rich variation in how a given activity can be performed. Using features that clearly separate between activities is crucial. In this paper, we propose an approach to automatically extract discriminative features for activity recognition. Specifically, we develop a method based on Convolutional Neural Networks (CNN), which can capture local dependency and scale invariance of a signal as it has been shown in speech recognition and image recognition domains. In addition, a modified weight sharing technique, called partial weight sharing, is proposed and applied to accelerometer signals to get further improvements. The experimental results on three public datasets, Skoda (assembly line activities), Opportunity (activities in kitchen), Actitracker (jogging, walking, etc.), indicate that our novel CNN-based approach is practical and achieves higher accuracy than existing state-of-the-art methods.

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Convolutional Neural Networks for human activity recognition using mobile sensors

Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with more traditional Web personalization techniques such as collaborative or content-based filtering. These problems include lack of scalability, reliance on subjective user ratings or static profiles, and the inability to capture a richer set of semantic relationships among objects (in content-based systems). Yet, usage-based personalization can be problematic when little usage data is available pertaining to some objects or when the site content changes regularly. For more effective personalization, both usage and content attributes of a site must be integrated into a Web mining framework and used by the recommendation engine in a uniform manner. In this paper we present such a framework, distinguishing between the offine tasks of data preparation and mining, and the online process of customizing Web pages based on a user's active session. We describe effective techniques based on clustering to obtain a uniform representation for both site usage and site content profiles, and we show how these profiles can be used to perform real-time personalization.

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Integrating Web Usage and Content Mining for More Effective Personalization

An information diffusion model based on retweeting mechanism for online social media

We introduce a new mobile system framework, SenSec, which uses passive sensory data to ensure the security of applications and data on mobile devices SenSec constantly collects sensory data from accelerometers, gyroscopes and magnetometers and constructs the gesture model of how a user uses the device SenSec calculates the sureness that the mobile device is being used by its owner Based on the sureness score, mobile devices can dynamically request the user to provide active authentication (such as a strong password), or disable certain features of the mobile devices to protect user's privacy and information security In this paper, we model such gesture patterns through a continuous n-gram language model using a set of features constructed from these sensors We built mobile application prototype based on this model and use it to perform both user classification and user authentication experiments User studies show that SenSec can achieve 75% accuracy in identifying the users and 713% accuracy in detecting the non-owners with only 131% false alarms

Jiang Zhu

Papers

Fog Computing: A Platform for Internet of Things and Analytics

Convolutional Neural Networks for human activity recognition using mobile sensors

Integrating Web Usage and Content Mining for More Effective Personalization

An information diffusion model based on retweeting mechanism for online social media

SenSec: Mobile security through passive sensing