Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Matrix Factorization Techniques for Recommender Systems

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform membership inference against a target model, we make adversarial use of machine learning and train our own inference model to recognize differences in the target model's predictions on the inputs that it trained on versus the inputs that it did not train on. We empirically evaluate our inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon. Using realistic datasets and classification tasks, including a hospital discharge dataset whose membership is sensitive from the privacy perspective, we show that these models can be vulnerable to membership inference attacks. We then investigate the factors that influence this leakage and evaluate mitigation strategies.

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Membership Inference Attacks Against Machine Learning Models

The past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target This sensing range constraint greatly limits the application of the proposed approaches in real life 
This paper presents FarSense--the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100% We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications

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FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

Despite its importance, walking direction is still a key context lacking a cost-effective and continuous solution that people can access in indoor environments. Recently, device-free sensing has attracted great attention because these techniques do not require the user to carry any device and hence could enable many applications in smart homes and offices. In this paper, we present WiDir, the first system that leverages WiFi wireless signals to estimate a human's walking direction, in a device-free manner. Human motion changes the multipath distribution and thus WiFi Channel State Information at the receiver end. WiDir analyzes the phase change dynamics from multiple WiFi subcarriers based on Fresnel zone model and infers the walking direction. We implement a proof-of-concept prototype using commercial WiFi devices and evaluate it in both home and office environments. Experimental results show that WiDir can estimate human walking direction with a median error of less than 10 degrees.

WiDir: walking direction estimation using wireless signals

With the rapid proliferation of sensor-rich smartphones, mobile crowd sensing has become a popular research field. In this article, we propose a four-stage life cycle (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and use 4W1H (i.e., what, when, where, who, and how) to sort out the research problems in the mobile crowd sensing domain. Furthermore, we attempt to foresee some new research directions in future mobile crowd sensing research.

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4W1H in Mobile Crowd Sensing

Trajectories obtained from Global Position System (GPS)-enabled taxis grant us an opportunity not only to extract meaningful statistics, dynamics, and behaviors about certain urban road users but also to monitor adverse and/or malicious events. In this paper, we focus on the problem of detecting anomalous routes by comparing the latter against time-dependent historically “normal” routes. We propose an online method that is able to detect anomalous trajectories “on-the-fly” and to identify which parts of the trajectory are responsible for its anomalousness. Furthermore, we perform an in-depth analysis on around 43 800 anomalous trajectories that are detected out from the trajectories of 7600 taxis for a month, revealing that most of the anomalous trips are the result of conscious decisions of greedy taxi drivers to commit fraud. We evaluate our proposed isolation-based online anomalous trajectory (iBOAT) through extensive experiments on large-scale taxi data, and it shows that iBOAT achieves state-of-the-art performance, with a remarkable performance of the area under a curve (AUC) ≥ 0.99.

iBOAT: Isolation-Based Online Anomalous Trajectory Detection

By allowing centimeter-scale human activity sensing with Wi-Fi signals, the Fresnel zone model could revolutionize wireless sensing and Internet of Things applications. The web extra at www.youtube.com/watch?v=R_vR6O8706g demonstrates how the Fresnel zone model can be leveraged for device-free sensing of human activities such as respiration and walking.

Daqing Zhang

Papers

FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas

WiDir: walking direction estimation using wireless signals

4W1H in Mobile Crowd Sensing

iBOAT: Isolation-Based Online Anomalous Trajectory Detection

Toward Centimeter-Scale Human Activity Sensing with Wi-Fi Signals