Liam McNamara

Media sharing based on colocation prediction in urban transport

People living in urban areas spend a considerable amount of time on public transport, for example, commuting to/from work. During these periods, opportunities for inter-personal networking present themselves, as many members of the public now carry electronic devices equipped with Bluetooth or other wireless technology. Using these devices, individuals can share content (e.g., music, news and video clips) with fellow travellers that are on the same train or bus. Transferring media content takes time; in order to maximise the chances of successful downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this paper, we propose a user-centric prediction scheme that collects historical colocation information to determine the best content sources. The scheme works on the assumption that people have a high degree of regularity in their movements. We first validate this assumption on a real dataset, that consists of traces of people moving in a large citys mass transit system. We then demonstrate experimentally on these traces that our prediction scheme significantly improves communication efficiency, when compared to a memory(history)-less source selection scheme.

SpotME If You Can: Randomized Responses for Location Obfuscation on Mobile Phones

Nowadays companies increasingly aggregate location data from different sources on the Internet to offer location-based services such as estimating current road traffic conditions, and finding the best nightlife locations in a city. However, these services have also caused outcries over privacy issues. As the volume of location data being aggregated expands, the comfort of sharing ones whereabouts with the public at large will unavoidably decrease. Existing ways of aggregating location data in the privacy literature are largely centralized in that they rely on a trusted location-based service. Instead, we propose a piece of software (Spot Me) that can run on a mobile phone and is able to estimate the number of people in geographic locations in a privacy-preserving way: accurate estimations are made possible in the presence of privacy-conscious users who report, in addition to their actual locations, a very large number of erroneous locations. The erroneous locations are selected by a randomized response algorithm. We evaluate the accuracy of Spot Me in estimating the number of people upon two very different realistic mobility traces: the mobility of vehicles in urban, suburban and rural areas, and the mobility of subway train passengers in Greater London. We find that erroneous locations have little effect on the estimations (in both traces, the error is below 18% for a situation in which more than 99% of the locations are erroneous), yet they guarantee that users cannot be localized with high probability. Also, the computational and storage overheads for a mobile phone running Spot Me are negligible, and the communication overhead is limited.

Content Source Selection in Bluetooth Networks

Large scale market penetration of electronic devices equipped with Bluetooth technology now gives the ability to share content (such as music or video clips) between members of the public in a decentralised manner. Achieved using opportunistic connections, formed when they are colocated, in environments where Internet connectivity is expensive or unreliable, such as urban buses, train rides and coffee shops. Most people have a high degree of regularity in their movements (such as a daily commute), including repeated contacts with others possessing similar seasonal movement patterns. We argue that this behaviour can be exploited in connection selection, and outline a system for the identification of long-term companions and sources that have previously provided quality content, in order to maximise the successful receipt of content flies. We utilise actual traces and existing mobility models to validate our approach, and show how consideration of the colocation history and the quality of previous data transfers leads to more successful sharing of content in realistic scenarios.

All Is Not Lost: Understanding and Exploiting Packet Corruption in Outdoor Sensor Networks

During phases of transient connectivity, sensor nodes receive a substantial number of corrupt packets. These corrupt packets are generally discarded, losing the sent information and wasting the energy put into transmitting and receiving. Our analysis of one years data from an outdoor sensor network deployment shows that packet corruption follows a distinct pattern that is observed on all links. We explain the patterns core features by considering implementation aspects of low-cost 802.15.4 transceivers and independent transmission errors. Based on the insight into the corruption pattern, we propose a probabilistic approach to recover information about the original content of a corrupt packet. Our approach vastly reduces the uncertainty about the original content, as measured by a manifold reduction in entropy.We conclude that the practice of discarding all corrupt packets in an outdoor sensor network may be unnecessarily wasteful, given that a considerable amount of information can be extracted from them.

Diversity decay in opportunistic content sharing systems

As content that users access on their mobile devices becomes bulkier, opportunistic networking is becoming a potential complement to centralised and infrastructure based downloads. We study how users share items of mutual interest with each other with a simple model based on a ‘networked urn process’. We investigate the effect of different content sharing policies upon a multi-category set of items. We find that the process of sharing mutual interests inherently disproportionately reinforces category replication disparity, i.e., the most popular categories become proportionally even more numerous. These findings uncover a major hurdle in the creation of automatic opportunistic file sharing between users. Even if users altruistically sacrifice battery power and network resources to share content not relevant to them, overall, the system may not be able to fairly distribute items that belong to niche categories.

FOCUS: Robust Visual Codes for Everyone

Visual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen/camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present FOCUS, a new code design that does not require this explicit trade-off between code capacity and the readers channel quality. Instead, FOCUS builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of FOCUS devices and evaluate it experimentally. Our results show that FOCUS gracefully adapts to the readers channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.

Demo: Scalable Visual Codes for Embedding Digital Data in the Physical World

Visual codes, such as QR codes, offer a low-cost alternative to RF technology when digital data needs to be embedded in objects in the physical world. However, in order to support receivers with a poor visual channel, e.g. low-resolution cameras, most visual codes are designed for low data capacity and short reading distances. We present our work on Focus, a visual code that avoids earlier works explicit trade-off between code capacity and channel quality. Rather than encoding the payload directly into individual pixels, Focus encodes the payload over a range of spatial frequencies. As a result, even a receiver with a very poor visual channel (e.g., a low-resolution camera, or a camera experiencing motion blur) can still partly decode a Focus code, because the codes low-frequency components are robust to common channel impairments. A receiver with a good channel can decode all data from the same code. In our demo, we will present a prototype of Focus for smartphones and showcase how it deals with common impairments of the visual channel.

Poster abstract: supporting heterogeneous LCD/camera links

Visible light communication over LCD/camera links offers a potential complement to traditional RF communication technology such as WiFi or cellular networks. However, the heterogeneity in receivers (e.g., mobile phone cameras) presents a challenge because the receivers differ widely in resolution, distance to the transmitter (LCD), and other factors, and therefore they differ in channel quality. We are researching a communication scheme in which each receiver can decode as much data from an LCDs transmission as the receivers channel supports. The core idea is to encode the payload into an images frequency representation rather than directly into pixels. We have successfully transmitted data using a prototype implementation and are currently investigating appropriate channel models.