Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A survey on deep learning for big data

Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.

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E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures

As smartphone penetration saturates, we are witnessing a new trend in personal mobile devices—wearable mobile devices or simply wearables as it is often called. Wearables come in many different forms and flavors targeting different accessories and clothing that people wear. Although small in size, they are often expected to continuously sense, collect, and upload various physiological data to improve quality of life. These requirements put significant demand on improving communication security and reducing power consumption of the system, fueling new research in these areas. In this paper, we first provide a comprehensive survey and classification of commercially available wearables and research prototypes. We then examine the communication security issues facing the popular wearables followed by a survey of solutions studied in the literature. We also categorize and explain the techniques for improving the power efficiency of wearables. Next, we survey the research literature in wearable computing. We conclude with future directions in wearable market and research.

A Survey of Wearable Devices and Challenges

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.

/pdf/demo-how-long-to-wait-predicting-bus-arrival-time-with-cml9hw1w3l.pdf

Demo: how long to wait?: predicting bus arrival time with mobile phone based participatory sensing

The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.

/pdf/pbn-towards-practical-activity-recognition-using-smartphone-5bhhl5gmn9.pdf

PBN: towards practical activity recognition using smartphone-based body sensor networks

In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.

/pdf/adasense-adapting-sampling-rates-for-activity-recognition-in-jhc4zjm19g.pdf

AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

Effective WiFi power management can strongly impact the energy consumption on Smartphones. Through controlled experiments, we find that WiFi power management on a wide variety of Smartphones is a largely autonomous process that is processed completely at the driver level. Driver level implementations suffer from the limitation that important power management decisions can be made only by observing packets at the MAC layer. This approach has the unfortunate side effect that each application has equal opportunity to impact WiFi power management to consume more energy, since distinguishing between applications is not feasible at the MAC layer. The power cost difference between WiFi power modes is high (a factor of 20 times when idle), therefore determining which applications are permitted to impact WiFi power management is an important and relevant problem. In this paper we propose SAPSM: Smart Adaptive Power Save Mode. SAPSM labels each application with a priority with the assistance of a machine learning classifier. Only high priority applications affect the client's behavior to switch to CAM or Active mode, while low priority traffic is optimized for energy efficiency. Our implementation on an Android Smartphone improves energy savings by up to 56% under typical usage patterns.

/pdf/sapsm-smart-adaptive-802-11-psm-for-smartphones-2br10g5d1e.pdf

SAPSM: Smart adaptive 802.11 PSM for smartphones

In this paper, we present an optimal packet size solution that optimizes the communication energy consumption in the heterogeneous wireless networks. More specifically, we consider a heterogeneous network system composed of a body sensor network (BSN) and a WiFi network. Then, based on the analysis of data communication in the BSN and WiFi (BSN-WiFi) network, we formulate a communication energy consumption optimization model with the constraints of throughput and time delay. Mathematically, we convert this model into a geometric programming problem, which is then numerically solved. The optimal solution can be applied in both BSN and WiFi network to dynamically select packet payload sizes according to real-time packet delivery ratios (PDRs). Since PDRs are time-varying, we tabulate a packet payload size lookup table for online packet size selection using PDRs as indices. Finally, we collect PDRs from a deployed BSN-WiFi network and evaluate the energy optimization model. The performance evaluation results show that, in comparison with fixed packet size solutions, our optimal solutions achieve up to 70 percent energy savings in a BSN(TDMA)-WiFi network and 68 percent in a BSN(CSMA)-WiFi network.

Communication Energy Modeling and Optimization through Joint Packet Size Analysis of BSN and WiFi Networks

Many mission-critical applications such as military surveillance, human health monitoring, and obstacle detection in autonomous vehicles impose stringent requirements for event detection accuracy and demand long system lifetimes. Through quantitative study, we show that traditional approaches to event detection have difficulty meeting such requirements. Specifically, they cannot explore the detection capability of a deployed system and choose the right sensors, homogeneous or heterogeneous, to meet user specified detection accuracy. They also cannot dynamically adapt the detection capability to runtime observations to save energy. Therefore, we are motivated to propose Watchdog, a modality-agnostic event detection framework that clusters the right sensors to meet user specified detection accuracy during runtime while significantly reducing energy consumption. Through evaluation with vehicle detection trace data and a building traffic monitoring testbed of IRIS motes, we demonstrate the superior performance of Watchdog over existing solutions in terms of meeting user specified detection accuracy and energy savings.

/pdf/watchdog-confident-event-detection-in-heterogeneous-sensor-3ghuafo49a.pdf

Matthew Keally

Papers

PBN: towards practical activity recognition using smartphone-based body sensor networks

AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

SAPSM: Smart adaptive 802.11 PSM for smartphones

Communication Energy Modeling and Optimization through Joint Packet Size Analysis of BSN and WiFi Networks

Watchdog: Confident Event Detection in Heterogeneous Sensor Networks