Continuous discovery of co-location contexts from Bluetooth data

The discovery of contexts is important for context-aware applications in pervasive computing. This is a challenging problem because of the stream nature of data, the complexity and changing nature of contexts. We propose a Bayesian nonparametric model for the detection of co-location contexts from Bluetooth signals. By using an Indian buffet process as the prior distribution, the model can discover the number of contexts automatically. We introduce a novel fixed-lag particle filter that processes data incrementally. This sampling scheme is especially suitable for pervasive computing as the computational requirements remain constant in spite of growing data. We examine our model on a synthetic dataset and two real world datasets. To verify the discovered contexts, we compare them to the communities detected by the Louvain method, showing a strong correlation between the results of the two methods. Fixed-lag particle filter is compared with Gibbs sampling in terms of the normalized factorization error that shows a close performance between the two inference methods. As fixed-lag particle filter processes a small chunk of data when it comes and does not need to be restarted, its execution time is significantly shorter than that of Gibbs sampling.

Wireless estimation of canine pose for search and rescue

In this paper we discuss the use of accelerometers and Bluetooth to monitor canine pose in the context of common poses observed in urban search and rescue dogs. We discuss the use of the canine pose system in a disaster environment, and propose techniques for determining canine pose. In addition we discuss the challenges with this approach in such environments. The paper presents the experimental results obtained from the heavy urban search and rescue disaster simulation, where experiments were conducted using multiple canines, which show that angles can be derived from acceleration readings. Our experiments show that similar angles were measured for each of the poses, even when measured on multiple USAR canines of varying size. We also found measurable and consistent differences between each of the poses, making them clearly distinguishable from one another, again even when comparing with different USAR canines.

Sensing and using social context

We present online algorithms to extract social context: Social spheres are labeled locations of significance, represented as convex hulls extracted from GPS traces. Colocation is determined from Bluetooth and GPS to extract social rhythms, patterns in time, duration, place, and people corresponding to real-world activities. Social ties are formulated from proximity and shared spheres and rhythms. Quantitative evaluation is performed for 10+ million samples over 45 man-months. Applications are presented with assessment of perceived utility: Socio-Graph, a video and photo browser with filters for social metadata, and Jive, a blog browser that uses rhythms to discover similarity between entries automatically.

High accuracy context recovery using clustering mechanisms

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point id and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing state-of-the-art probabilistic clustering technique, the Latent Dirichiet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

Wireless instantaneous neurotransmitter concentration system-based amperometric detection of dopamine, adenosine, and glutamate for intraoperative neurochemical monitoring - laboratory investigation

Object  In a companion study, the authors describe the development of a new instrument named the Wireless Instantaneous Neurotransmitter Concentration System (WINCS), which couples digital telemetry with fast-scan cyclic voltammetry (FSCV) to measure extracellular concentrations of dopamine. In the present study, the authors describe the extended capability of the WINCS to use fixed potential amperometry (FPA) to measure extracellular concentrations of dopamine, as well as glutamate and adenosine. Compared with other electrochemical techniques such as FSCV or high-speed chronoamperometry, FPA offers superior temporal resolution and, in combination with enzyme-linked biosensors, the potential to monitor nonelectroactive analytes in real time.

Methods  The WINCS design incorporated a transimpedance amplifier with associated analog circuitry for FPA; a microprocessor; a Bluetooth transceiver; and a single, battery-powered, multilayer, printed circuit board. The WINCS was tested with 3 distinct recording electrodes: 1) a carbon-fiber microelectrode (CFM) to measure dopamine; 2) a glutamate oxidase enzyme–linked electrode to measure glutamate; and 3) a multiple enzyme–linked electrode (adenosine deaminase, nucleoside phosphorylase, and xanthine oxidase) to measure adenosine. Proof-of-principle analyses included noise assessments and in vitro and in vivo measurements that were compared with similar analyses by using a commercial hardwired electrochemical system (EA161 Picostat, eDAQ; Pty Ltd). In urethane-anesthetized rats, dopamine release was monitored in the striatum following deep brain stimulation (DBS) of ascending dopaminergic fibers in the medial forebrain bundle (MFB). In separate rat experiments, DBS-evoked adenosine release was monitored in the ventrolateral thalamus. To test the WINCS in an operating room setting resembling human neurosurgery, cortical glutamate release in response to motor cortex stimulation (MCS) was monitored using a large-mammal animal model, the pig.

Results   The WINCS, which is designed in compliance with FDA-recognized consensus standards for medical electrical device safety, successfully measured dopamine, glutamate, and adenosine, both in vitro and in vivo. The WINCS detected striatal dopamine release at the implanted CFM during DBS of the MFB. The DBS-evoked adenosine release in the rat thalamus and MCS-evoked glutamate release in the pig cortex were also successfully measured. Overall, in vitro and in vivo testing demonstrated signals comparable to a commercial hardwired electrochemical system for FPA.

Conclusions  By incorporating FPA, the chemical repertoire of WINCS-measurable neurotransmitters is expanded to include glutamate and other nonelectroactive species for which the evolving field of enzyme-linked biosensors exists. Because many neurotransmitters are not electrochemically active, FPA in combination with enzyme-linked microelectrodes represents a powerful intraoperative tool for rapid and selective neurochemical sampling in important anatomical targets during functional neurosurgery.

Emerging wireless personal area networks(WPANs) : - An analysis of techniques, tools and threats

Wireless Personal Area Networks provide a pivotal role in local area network technology complementing traditional Wireless Local Area Network technologies. Bluetooth, ZigBee and NFC (Near Field Communications) have emerged as key WPAN technologies with UWB (Ultra Wide Band) standards currently evolving. They are however subject to the usual range of security vulnerabilities found in wireless LANs such as spoofing, snooping, man-in-the-middle, denial of service and other attacks. However security in WPANs is not as mature as it is in Wireless LANs and further work is needed in order to provide comparable protection. This paper examines a range of WPAN technologies and security issues and proposes protection mechanisms that can mitigate risk in each case. © 2012 IEEE.

Smart Interactive Advertising Board (SIAB)

A Smart Interactive Advertising Board (SIAB) has been designed and developed. This board is capable of interacting with humans in close proximity. A number of sensor devices are used in the board where sensors inputs (Bluetooth IDs and distance sensor readings) create an innovative form of user interaction with the board. The SIAB display is determined by user position, location, and movements. In this paper, the authors investigate how the user inputs are mapped to the advertising board and its behavior. A prototype of SIAB is implemented.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Smartphone malware and its propagation modeling : A survey

Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we enumerate the possible damage caused by smartphone malware. In the second part, we focus on smartphone malware propagation modeling. In order to understand the propagation behavior of smartphone malware, we recall generic epidemic models as a foundation for further exploration. We then extensively survey the smartphone malware propagation models. At the end of this paper, we highlight issues of the current smartphone malware propagation models and discuss possible future trends based on our understanding of this topic. © © 2014 IEEE.

Security and trust in opportunistic networks - a survey

Opportunistic networks or OppNets refer to a number of wireless nodes opportunistically communicating with each other in a form of “Store–Carry–Forward”. This occurs when they come into contact with each other without proper network infrastructure. OppNets use wireless technologies, such as IEEE 802.11, WiMAX, Bluetooth, and other short-range radio communication. In OppNets, there is no end-to-end connection between the source and the destination nodes, and the nodes usually have high mobility, low density, limited power, short radio range, and often subject to different kinds of attacks by malicious nodes. Due to these characteristics and features, OppNets are subject to serious security challenges. OppNets strongly depend on human interaction; therefore, the success of securing such networks is based on trust between people. This survey includes the security approaches in OppNets and techniques used to increase their security levels.