Kanchana Thilakarathna

MobiTribe: Cost Efficient Distributed User Generated Content Sharing on Smartphones

Distributed social networking services show promise to solve data ownership and privacy problems associated with centralized approaches. Smartphones could be used for hosting and sharing users data in a distributed manner, if the associated high communication costs and battery usage issues of the distributed systems could be mitigated. We propose a novel mechanism for reducing these costs to a level comparable with centralized systems by using a connectivity aware replication strategy. We develop an algorithm for grouping devices into tribes for content replication among intended content consumers and serve it using low-cost network connections. We evaluate the performance of the algorithm using three real world trace data sets. The results show that a persistent low-cost network availability can be achieved with an average of two replicas per content. Additionally, cellular bandwidth consumption and energy consumption of users are evaluated analytically using user content creation and consumption modeling. The results show that the proposed mechanism lowers monetary and energy costs for users compared to non-mobile-optimized distributed systems irrespective of the content demand model.

Performance of content replication in MobiTribe: A distributed architecture for mobile UGC sharing

An increasing portion of traffic in mobile networks comes from users creating content and uploading it to the Internet to share it. The capacity of mobile networks is a limited resource and uploading high resolution content consumes a large part of it. We introduce MobiTribe, a distributed storage cloud consisting of mobile devices for storing the content created on the phones. It can serve requests for content and take advantage of networks with spare capacity to deliver the content at a lower cost. We propose a content distribution and replication algorithm which achieves this goal. The performance of the algorithm is evaluated using empirical data traces of WLAN availability patterns of mobile devices, showing that it is possible to achieve 99.98% availability of a content via WLAN while minimising content distribution to an average of 2.69 replicas.

Enabling mobile distributed social networking on smartphones

Distributed social networking services show promise to solve data ownership and privacy problems associated with centralised approaches. Smartphones could be used for hosting and sharing users data in a distributed manner, if the associated high communication costs and battery usage issues of the distributed systems could be mitigated. We propose a novel mechanism for reducing these costs to a level comparable with centralised systems by using a connectivity aware replication strategy. To this end, we develop an algorithm based on a combination of bipartite b-matching and a greedy heuristics for grouping devices into tribes among intended content consumers. The tribes replicate content and serve it using low-cost network connections by exploiting time elasticity of user generated content sharing. The performance is evaluated using three real world trace data sets. The results show that a persistent low-cost network availability can be achieved with an average of two replicas per content. Additionally, a content creator can reduce 3G traffic by up to 43% and device energy use by up to 41% on average compared to content sharing in non-mobile-optimised distributed social networking approaches. Moreover, the results show that the proposed mechanism can provide the benefits of a distributed content sharing system for monetary and energy costs comparable to those of a centralised server based system.

User generated content dissemination in mobile social networks through infrastructure supported content replication

User generated content in mobile social networks such as news items, promotion coupons and traffic updates, are typically short-lived in time and space. Therefore, opportunistic communication as such may not be suitable for user generated content dissemination in mobile social networks due to high content delivery delays. To address the timeliness of the delivery, we propose a hybrid content dissemination strategy, which makes use of available networking infrastructure to replicate content on smartphones, and leverages these replicators to propagate the content to others via opportunistic communication. Using trace-driven simulations, we show that the content can be delivered to all users in a mobile social network within required time bounds by replicating content using infrastructure to only 10% of the consumers. Furthermore, due to the high rate of timely content delivery, the proposed hybrid content dissemination scheme ultimately saves approximately 60% of the infrastructure bandwidth usage compared to traditional opportunistic dissemination in general MSN environments. In addition, the replication strategies proposed in the paper are robust enough to deliver the same performance even with low collaboration of consumers for replication.

Demo: Crowd-cache -- popular content for free

Crowd-Cache is a novel crowd-sourced content caching system which provides cheap and convenient content access for mobile users. Our system exploits both transient colocation of devices and the spatial temporal correlation of content popularity, where users in a particular location and at specific times would be likely interested in similar content. We demonstrate the feasibility of Crowd-Cache system through a prototype implementation on Android smartphones.

Airborne Disease Propagation on Large Scale Social Contact Networks

Social sensing has received growing interest in a broad range of applications from business to health care. The potential benefits of modeling infectious disease spread through geo-tagged social sensing data has recently been demonstrated, yet it has not considered contagion events that can occur even when co-located individuals are no longer in physical contact, such as for capturing the dynamics of airborne diseases. In this study, we exploit the location updates made by 0.6 million users of the Momo social networking application to characterize airborne disease dynamics. Airborne diseases can transmit through infectious particles exhaled by the infected individuals. We introduce the concept of same-place different-time (SPDT) transmission to capture the persistent effect of airborne particles in their likelihood to spread a disease. Because the survival duration of these infectious particles is dependent on environmental conditions, we investigate through large-scale simulations the effects of three parameters on SPDT-based disease diffusion: the air exchange rate in the proximity of infected individuals, the infectivity decay rates of pathogen particles, and the infection probability of inhaled particles. Our results confirm a complex interplay between the underlying contact network dynamics and these parameters, and highlight the predictive potential of social sensing for epidemic outbreaks.

A deep dive into location-based communities in social discovery networks

Location-based social discovery networks (LBSD) is an emerging category of location-based social networks (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information.

AFV: enabling application function virtualization and scheduling in wearable networks

Smart wearable devices are widely available today and changing the way mobile applications are being developed. Applications can dynamically leverage the capabilities of wearable devices worn by the user for optimal resource usage and information accuracy, depending on the user/device context and application requirements. However, application developers are not yet taking advantage of these cross-device capabilities. We thus design AFV (Application Function Virtualization), a framework enabling automated dynamic function virtualization/scheduling across devices, simplifying context-aware application development. AFV provides a simple set of APIs hiding complex framework tasks and continuously monitors context/application requirements, to enable the dynamic invocation of functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency and quality of experience with relevant use cases.

Crowd-Cache

Mobile capped plans are being increasingly adopted by mobile operators due to an exponential data traffic growth. Users then often suffer high data consumption costs as well as poor quality of experience. In this paper, we introduce a novel content access scheme, Crowd-Cache, which enables mobile networking in proximity by exploiting the transient co-location of devices, the epidemic nature of content popularity, and the capabilities of smart mobile devices. Crowd-Cache provides mobile users access to popular content cheaply with low latency while improving the overall quality of experience. We model the Crowd-Cache system in a probabilistic framework using a real-life dataset of video content access. The simulation results show that, in a public transportation scenario, more than 80% of the passengers can save at least 40% on their cellular data usage during a typical average city bus commute of 10 minutes. Finally, we show the practical viability of the system by implementing and evaluating the system on Android devices.

uDrop: Pushing drop-box to the edge of mobile network

Content sharing and online collaboration have become extremely popular in recent years, in particular with the pervasiveness of cloud storage services such as Dropbox. However, these popular cloud services are not accessible when users are offline and do not take the geographical location of the users into account when distributing content. Despite years of research in opportunistic content dissemination, real users are yet to fully experience the advantages of content sharing in proximity. To this end, we propose uDrop which literally pushes the cloud storage services to the edge of the network whenever interested users are in proximity of each other. uDrop enables content sharing among users in the vicinity without relying on networking infrastructure while improving the user quality of experience and reducing the cost of bandwidth. In this demonstration, we show the practical feasibility of uDrop service on Android smartphones.