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 reader's 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 reader's channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet.

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 work's 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 code's low-frequency components are robust to common channel impairments. A receiver with a good channel can decode all data from the same code.

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 one's 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 (SpotMe) 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 SpotMe 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.

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.

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 city's 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.

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.