Next generation analytics for open pervasive display networks

Public displays and digital signs are becoming increasingly widely deployed as many spaces move towards becoming highly interactive and augmented environments. Market trends suggest further significant increases in the number of digital signs and both researchers and commercial entities are working on designing and developing novel uses for this technology. Given the level of investment, it is increasingly important to be able to understand the effectiveness of public displays. Current state-of-the-art analytics technology is limited in the extent to which it addresses the challenges that arise from display deployments becoming open (increasing numbers of stakeholders), networked (viewer engagement across devices and locations) and pervasive (high density of displays and sensing technology leading to potential privacy threats for viewers). In this thesis, we provide the first exploration into achieving next generation display analytics in the context of open pervasive display networks. In particular, we investigated three areas of challenge: analytics data capture, reporting and automated use of analytics data. Drawing on the increasing number of stakeholders, we conducted an extensive review of related work to identify data that can be captured by individual stakeholders of a display network, and highlighted the opportunities for gaining insights by combining datasets owned by different stakeholders. Additionally, we identified the importance of viewer-centric analytics that use traditional display-oriented analytics data combined with viewer mobility patterns to produce entirely new sets of analytics reports. We explored a range of approaches to generating viewer-centric analytics including the use of mobility models as a way to create 'synthetic analytics' - an approach that provides highly detailed analytics whilst preserving viewer privacy. We created a collection of novel viewer-centric analytics reports providing insights into how viewers experience a large network of pervasive displays including reports regarding the effectiveness of displays, the visibility of content across the display network, and the visibility of content to viewers. We further identified additional reports specific to those display networks that support the delivery of personalised content to viewers. Additionally, we highlighted the similarities between digital signage and Web analytics and introduced novel forms of digital signage analytics reports created by leveraging existing Web analytics engines. Whilst the majority of analytics systems focus solely on the capture and reporting of analytics insights, we additionally explored the automated use of analytics data. One of the challenges in open pervasive display networks is accommodating potentially competing content scheduling constraints and requirements that originate from the large number of stakeholders - in addition to contextual changes that may originate from analytics insights. To address these challenges, we designed and developed the first lottery scheduling approach for digital signage providing a means to accommodate potentially conflicting scheduling constraints, and supporting context- and event-based scheduling based on analytics data fed back into the digital sign. In order to evaluate the set of systems and approaches presented in this thesis, we conducted large-scale, long-term trials allowing us to show both the technical feasibility of the systems developed and provide insights into the accuracy and performance of different analytics capture technologies. Our work provides a set of tools and techniques for next generation digital signage analytics and lays the foundation for more general people-centric analytics that go beyond the domain of digital signs and enable unique analytical insights and understanding into how users interact across the physical and digital world

MobiSys 2016

The 14th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2016) spanned a range of themes and domains, from smart environments to security and privacy. The highlights presented here cover the keynotes, paper sessions, and first Asian Students Symposium on Emerging Technologies

IoT Maps : Charting the Internet of Things

Internet of Things (IoT) devices are becoming increasingly ubiquitous in our everyday environments. While the number of devices and the degree of connectivity is growing, it is striking that as a society we are increasingly unaware of the locations and purposes of such devices. Indeed, much of the IoT technology being deployed is invisible and does not communicate its presence or purpose to the inhabitants of the spaces within which it is deployed. In this paper, we explore the potential benefits and challenges of constructing IoT maps that record the location of IoT devices. To illustrate the need for such maps, we draw on our experiences from multiple deployments of IoT systems.Peer reviewe

Memorability of cued-recall graphical passwords with saliency masks

Cued-recall graphical passwords have a lot of potential for secure user authentication, particularly if combined with saliency masks to prevent users from selecting weak passwords. Saliency masks were shown to significantly improve password security by excluding those areas of the image that are most likely to lead to hotspots. In this paper we investigate the impact of such saliency masks on the memorability of cued-recall graphical passwords. We first conduct two pre-studies (N=52) to obtain a set of images with three different image complexities as well as real passwords. A month-long user study (N=26) revealed that there is a strong learning effect for graphical passwords, in particular if defined on images with a saliency mask. While for complex images, the learning curve is steeper than for less complex ones, they best supported memorability in the long term, most likely because they provided users more alternatives to select memorable password points. These results complement prior work on the security of such passwords and underline the potential of saliency masks as both a secure and usable improvement to cued-recall gaze-based graphical passwords

Poster: Understanding Mobile User Interactions with the IoT

The increasing reach of the Internet of Things (IoT) is leading to a world rich in sensors [3] that can be used to support physical analytics -- analogous to web analytics but targeted at user interactions with physical devices in the real-world (e.g. [2]). In contrast to web analytics, physical analytics systems typically only provide data relating to sensors and objects without consideration of individual users. This is mainly a consequence of an inability to track individual mobile user interactions across multiple physical objects (or across sessions of interaction with a single object) using, for example, an analogue of a web cookie. Indeed, such a "physical analytics cookie" could raise significant privacy concerns. However, in many cases a more "human-centric" approach to analytics would enable us to provide new and interesting insights into interactions between mobile users and the physical world [1]. In our work we endeavour to leverage synthetic user traces of human mobility, and data from real IoT systems, to provide such insights

Design Considerations for Multi-Stakeholder Display Analytics

Measuring viewer interactions through detailed analytics will be crucial to improving the overall performance of future open display networks. However, in contrast to traditional sign and web analytics systems, such display networks are likely to feature multiple stakeholders each with the ability to collect a subset of the required analytics information. Combining analytics data from multiple stakeholders could lead to new insights, but stakeholders may have limited willingness to share information due to privacy concerns or commercial sensitivities. In this paper, we provide a comprehensive overview of analytics data that might be captured by different stakeholders in a display network, make the case for the synthesis of analytics data in such display networks, present design considerations for future architectures designed to enable the sharing of display analytics information, and offer an example of how such systems might be implemented

Next generation physical analytics for digital signage

Traditional digital signage analytics are based on a display-centric view of the world, reporting data on the content shown augmented with frequency of views and possibly classification of the audience demographics. What these systems are unable to provide, are insights into viewers' overall experience of content. This is problematic if we want to understand where, for example, to place content in a network of physically distributed digital signs to optimise content exposure. In this paper we propose a new approach that combines mobility simulations with comprehensive signage analytics data to provide viewer-centric physical analytics. Our approach enables us to ask questions of the analytics from the viewer's perspective for the first time, including estimating the exposure of different user groups to specific content across the entire signage network. We describe a proof of concept implementation that demonstrates the feasibility of our approach, and provide an overview of potential applications and analytics reports

Repurposing web analytics to support the IoT

Internet of Things analytics engines are complex to use and often optimized for a single domain or limited to proprietary data. A prototype system shows that existing Web analytics technologies can successfully be repurposed for IoT applications including sensor monitoring and user engagement tracking