Location-sharing services such as Facebook and Foursquare/Swarm have become increasingly popular, due to the ease at which users can share their locations, and participate in services, games and other applications that leverage these locations. But it is important for people who use these services to configure appropriate location-privacy preferences so that they can control to whom they want to share their location information. Manually configuring these preferences may be burdensome and confusing, and so location-privacy preference recommenders based on crowdsourcing preferences from other users have been proposed. Whether people will accept the recommended preferences acquired from other users, who they may not know or trust, has not, however, been investigated. In this paper, we present a user experiment (n=99) to explore what factors influence people's acceptance of location-privacy preference recommenders. We find that 44% of our participants have privacy concerns about such recommenders. These concerns are shown to have a negative effect (p<0.001) on their acceptance of the recommendations and their satisfaction about their choices. Furthermore, users' acceptance of recommenders varies according to both context and recommendations being made. Our findings are potentially useful to designers of location-sharing services and privacy recommenders.

Location-Based Services have become increasingly popular due to the prevalence of smart devices and location-sharing applications such as Facebook and Foursquare. The protection of people's sensitive location data in such applications is an important requirement. Conventional location privacy protec

Social network sites, location-sharing services and, more recently, applications enabling the quantified self, mean that people are generating and sharing more data than ever before. It is important to understand the potential privacy impacts when such personal data are commercialised, to ensure that expectations of privacy are preserved. This paper presents the first user study of incentivised location sharing, where people are given a direct monetary incentive to share their location with a business or their social network. We use Nissenbaum's framework of contextual integrity in a preliminary user study (n=22) to investigate potential privacy risks with such services. We find that monetisation changes why people share their data, but not the frequency of disclosures. Our results motivate further study and are useful for designers of location-sharing systems and researchers who wish to leverage the diverse range of personal data that are available in a privacy-sensitive manner.

Opportunistic routing protocols can enable message delivery in disconnected networks of mobile devices. To conserve energy in mobile environments, such routing protocols must minimise unnecessary message-forwarding. This paper presents an opportunistic routing protocol that leverages social role information. We compute node roles from a social network graph to identify nodes with similar contact relationships, and use these roles to determine routing decisions. By using pre-existing social network information, such as online social network friends, to determine roles, we show that our protocol can bootstrap a new opportunistic network without the delay incurred by encounter-history-based routing protocols such as SimbetTS. Simulations with four real-world datasets show improved performance over SimbetTS, with performance approaching Epidemic routing in some scenarios.

Opportunistic networking involves forwarding messages between proximate users, who may or may not know one another. This assumes that users are willing to forward messages to each other. This assumption may not hold if users are concerned about using the opportunistic network service. One such concern may be due to privacy; for instance, users' locations may be leaked. A privacy-concerned user may therefore disable their mobile device's opportunistic-networking features at various times, to preserve their privacy. This paper studies the impact of location privacy concerns on the performance of an opportunistic network. Using data from a real-world location-aware user study to develop a privacy model, we conduct trace-based simulations of various opportunistic routing protocols with two real-world traces. We find that users' location privacy preferences may potentially reduce the delivery performance of an opportunistic network to zero.

Many new transport protocols are being defined, including, for example, variants of the Transmission Control Protocol (TCP), to better match the requirements of new applications. A key issue in the evaluation of protocol flows, in terms of their performance, is how fair they are to other flows. Specifically, it is important to understand how a mix of existing and/or new protocols will interact with each other when using the same network resources. Such observations help to inform protocol design, and allow an assessment of potential impacts on users. We present a simple, yet effective, methodology for examining a specific case of inter-flow fairness based solely on measurements of flow performance. As well as using an existing fairness metric, we propose a new metric which provides a richer information summary for the evaluation of fairness.

Delay-tolerant network architectures exploit mobile devices carried by users to enable new networked applications. Efficiently routing information through these DTNs faces new challenges such as mobility and the dynamic nature of the network. Previous work has looked at using encountered nodes to build a social network for routing. In this work we construct routing tables from users' self-reported social networks. Initial experiments indicate that this significantly reduces the delivery cost of transmitting messages through a DTN.

Mobile, delay-tolerant, ad hoc and pocket-switched networks may form an important part of future ubiquitous computing environments. Understanding how to efficiently and effectively route information through such networks is an important research challenge, and much recent work has looked at detecting communities and cliques to determine forwarding paths. Such detected communities, however, may miss important aspects. For instance, a user may have strong social ties to another user that they seldom encounter; a detected social network may omit this tie and so produce sub-optimal forwarding paths. Moreover, the delay in detecting communities may slow the bootstrapping of a new delay-tolerant network. This paper explores the use of self-reported social networks for routing in mobile networks in comparison with detected social networks discovered through encounters. Using encounter records from a group of participants carrying sensor motes, we generate detected social networks from these records. We use these networks for routing, and compare these to the social networks which the users have self-reported on a popular social networking website. Using techniques from social network analysis, we find that the two social networks are different. These differences, however, do not lead to a significant impact on delivery ratio, while the self-reported social network leads to a significantly lower cost.

Wireless Local Area Networks (WLANs) are now commonplace on many academic and corporate campuses. As "Wi-Fi" technology becomes ubiquitous, it is increasingly important to understand trends in the usage of these networks.This paper analyzes an extensive network trace from a mature 802.11 WLAN, including more than 550 access points and 7000 users over seventeen weeks. We employ several measurement techniques, including syslogs, telephone records, SNMP polling and tcpdump packet sniffing. This is the largest WLAN study to date, and the first to look at a large, mature WLAN and consider geographic mobility. We compare this trace to a trace taken after the network's initial deployment two years ago.We found that the applications used on the WLAN changed dramatically. Initial WLAN usage was dominated by Web traffic; our new trace shows significant increases in peer-to-peer, streaming multimedia, and voice over IP (VoIP) traffic. On-campus traffic now exceeds off-campus traffic, a reversal of the situation at the WLAN's initial deployment. Our study indicates that VoIP has been used little on the wireless network thus far, and most VoIP calls are made on the wired network. Most calls last less than a minute.We saw greater heterogeneity in the types of clients used, with more embedded wireless devices such as PDAs and mobile VoIP clients. We define a new metric for mobility, the "session diameter." We use this metric to show that embedded devices have different mobility characteristics than laptops, and travel further and roam to more access points. Overall, users were surprisingly non-mobile, with half remaining close to home about 98% of the time.

In this paper we attempt to gain an understanding of the behaviour of users in a multipoint, interactive communication scenario. In particular, we wish to understand the dynamics of user participation at a session level. We present wide-area session level traces of the popular multiplayer networked games Quake and Half-Life. These traces were gathered by regularly polling 2256 game servers located all over the Internet, and querying the number of players present at each server and how long they had been playing. We analyse three specific features of the data: the number of players in a game, the interarrival times between players and the length of a player's session. We find significant time-of-day and network externality effects in the number of players. Player duration times fit an exponential distribution, while interarrival times fit a heavy-tailed distribution. The implications of our findings are discussed in the context of provisioning and charging for network quality of service for multipoint and multicast transmission. This work is ongoing.