Simon Klakegg

A data hiding approach for sensitive smartphone data

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacy-sensitive data such as medical monitoring systems and digital forensics tools.

Instrumenting smartphones with portable NIRS

In this paper we propose a mobile sensing solution that uses Near Infrared Spectroscopy (NIRS) and discuss its potential in future everyday use cases. The proposed design enables novice end users to classify various objects using NIRS and without prior knowledge of the technology itself. We describe how an instrument that traditionally has been used solely by trained lab personnel, can be commoditized to be used by any end user with a mobile device. The preliminary results indicate that samples can be identified with high accuracy, but that a series of implementation and design challenges must be first accounted for.

Crowdsourcing situated & subjective knowledge for decision support

In this paper we present a study on crowdsourcing subjective knowledge. We introduce a mobile app that was built for this purpose, and compare results from two datasets collected using the app. One dataset was collected during a workshop and the other one during a one-week long field trial. We present interview findings on mobile knowledge collection. Further, we discuss the types of information that should optimally be collected on the go, and show how our data analysis supports the qualitative findings. This work directly continues our earlier efforts on creating a platform that encapsulates wisdom of the crowd for decision support.

Towards Commoditised Near Infrared Spectroscopy

Near Infrared Spectroscopy (NIRS) is a sensing technique in which near infrared light is transmitted into a sample, followed by light absorbance measurements at various wavelengths. This technique enables the inference of the inner chemical composition of the scanned sample, and therefore can be used to identify or classify objects. In this paper, we describe how to facilitate the use of NIRS by non- expert users in everyday settings. Our work highlights the key challenges of placing NIRS devices in the hands of non-experts. We develop a system to mitigate these challenges, and evaluate it in a user study. We show how NIRS technology can be successfully utilised by untrained users in an unsupervised manner through a special enclosure and an accompanying smartphone app. Finally, we discuss potential future developments of commoditised NIRS.

TestAWARE: A Laboratory-Oriented Testing Tool for Mobile Context-Aware Applications

Although mobile context instrumentation frameworks have simplified the development of mobile context-aware applications, it remains challenging to test such applications. In this paper, we present TestAWARE that enables developers to systematically test context-aware applications in laboratory settings. To achieve this, TestAWARE is able to download, replay and emulate contextual data on either physical devices or emulators. To support both white -box and black-box testing, TestAWARE has been implemented as a novel structure with a mobile client and code library. In blackbox testing scenarios, developers can manage data replay through the mobile client, without writing testing scripts or modifying the source code of the targeted application. In white-box testing scenarios, developers can manage data replay and test functional/non-functional properties of the targeted application by writing testing scripts using the code library. We evaluated TestAWARE by quantifying its maximal data replay speed, and by conducting a user study with 13 developers. We show that TestAWARE can overcome data synchronisation challenges, and found that PC-based emulators can replay data significantly faster than physical smartphones and tablets. The user study highlights the usefulness of TestAWARE in the systematic testing of mobile context-aware applications in laboratory settings.

Designing a context-aware assistive infrastructure for elderly care

We present an assistive healthcare platform, CARE, which aims to provide daily support for elderly caregivers with context-aware, unobtrusive, and actionable information. This information is collected through a plethora of IoT sensors installed strategically at an elderly care centre and is accessed through an Android tablet application. The applications goal is to empower nurses with a better understanding of elderly needs and ultimately, improve the care service. We investigate how IoT devices and sensors can enable a pervasive healthcare system, and discuss a wide-range of important parameters for integration of elderly care practices.

How to validate mobile crowdsourcing design? leveraging data integration in prototype testing

Mobile crowdsourcing applications often run in dynamic environments. Due to limited time and budget, developers of mobile crowdsourcing applications sometimes cannot completely test their prototypes in real world situations. We describe a data integration technique for developers to validate their design in prototype testing. Our approach constructs the intended context by combining real-time, historical and simulated data. With correct context-aware design, mobile crowdsourcing applications presenting crowdsourcing questions in relevant context to users are likely to obtain high response quality.

S3: environmental fingerprinting with a credit card-sized NFC powered sensor board

People have become more aware about their environment and pay more attention to conditions, e.g., air quality, and UV light exposure. Conventional technologies for reading environmental conditions are expensive, bulky, situated, and do not meet peoples need for a mobile and portable tool for environmental fingerprinting on demand. We present a mobile-enabled client-server system for personalized environmental fingerprinting and crowdsourced environmental fingerprint datasets using a smartphone and a portable credit card-sized NFC powered sensor board.

Facilitating Collocated Crowdsourcing on Situated Displays

Online crowdsourcing enables the distribution of work to a global labor force as small and often repetitive tasks. Recently, situated crowdsourcing has emerged as a complementary enabler to elicit labor in specific locations and from specific crowds. Teamwork in online crowdsourcing has been recently shown to increase the quality of output, but teamwork in situated crowdsourcing remains unexplored. We set out to fill this gap. We present a generic crowdsourcing platform that supports situated teamwork and provide experiences from a laboratory study that focused on comparing traditional online crowdsourcing to situated team-based crowdsourcing. We built a crowdsourcing desk that hosts three networked terminal displays. The displays run our custom team-driven crowdsourcing platform that was used to investigate collocated crowdsourcing in small teams. In addition to analyzing quantitative data, we provide findings based on questionnaires, interviews, and observations. We highlight 1) emerging differences between traditional and collocated crowdsourcing, 2) the collaboration strategies that teams exhibited in collocated crowdsourcing, and 3) that a priori team familiarity does not significantly affect collocated interaction in crowdsourcing. The approach we introduce is a novel multi-display crowdsourcing setup that supports collocated labor teams and along with the reported study makes specific contributions to situated crowdsourcing research.

Rapid clock synchronisation for ubiquitous sensing services involving multiple smartphones

This paper investigates the precision of rapid clock synchronisation for ubiquitous sensing services which consist of multiple smartphones. Specifically, we consider scenarios where multiple smartphones are used to sense physical phenomena, and subsequently the sensor data from multiple distributed devices is aggregated. We observe that the accumulated clock drift for smartphones can be more than 150ms per day in the worst case. We show that solutions using the public Network Time Protocol (NTP) can be noisy with errors up to 1800ms in one request. We describe a rapid clock synchronisation technique that reduces drift to 10ms on average (measured by linear regression) and achieves pair-wise synchronisation between smartphones with an average of 27ms (measured by accelerometer), following a Gaussian-like distribution. Our results provide a lower bound for rapid clock synchronisation as a guide when developing ubiquitous sensing services using multiple smartphones.