In recent years, two major trends have changed the way mobile phones are used: smartphones have become a platform for applications, and 3G connectivity has turned them into ubiquitous Internet clients. Increasingly, applications on smartphones (such as document sharing, media players and map browsers) interact with the cloud as a backend for data storage and computation. We observe that, for many mobile applications, the specific data that is accessed depends on the current location of the user. For example, a restaurant recommendation application is often used to get information about nearby restaurants. In this paper, we present WhereStore, a location-based data store for smart-phones interacting with the cloud. It uses filtered replication along with each device's location history to distribute items between smartphones and the cloud. We discuss the challenges of designing such a system, relevant applications, and a specific design and prototype implementation.

The advent of smart phones, along with the paradigm shift towards cloud-based services, presents new challenges to the cellular backbone infrastructure. Cisco predicts that mobile data traffic will double every year through 2014, with a CAGR of 108% from 2009 to 2014, reaching 3.6 exabytes per month. We propose to exploit the potential of smart phones in proximity cooperatively, using their resources to reduce the demand on the cellular infrastructure, through a decision framework called RACE (Resource Aware Collaborative Execution). RACE enables the use of other mobile devices in the promixity as mobile data relays. RACE is a Markov Decision Process (MDP) optimization framework that takes user profiles and user preferences to determine the degree of collaboration. Both centralized and decentralized policies are developed and validated through simulation using real mobile usage traces. We implemented a simple prototype on a network of HTC G1 phones running the Android 1.5 operating system to demonstrate the viability of the system.

A number of mobile online social networking (OSN) services appear in the market. Majority of mobile systems can strongly benefit from services offered by cloud. However, centralized servers and communication infrastructures may not always be available. Further their location-based services are not offered to low-end mobile devices without GPS modules.

To build a system that can take advantage of cloud, and also can address these potential problems that could hinder OSN usage, we design and construct a multi-hop networking system named MoNet based on WiFi, and on top of which we design and implement WiFace, a privacy-aware geosocial networking service. For the situation without any infrastructure, we design a distributed content sharing protocol which can significantly shorten the relay path, reduce conflicts and improve data persistence and availability. A role strategy is designed to encourage users to collaborate in the network. Furthermore, a key management and an authorization mechanism are developed to prevent some attacks and protect privacy. We conduct comprehensive experiments to evaluate the performance of our mobile platform MoNet and application WiFace. The results show that MoNet is more than sufficient to support social networking, and even audio and video applications.

Our vision is to achieve faster and near real time detection and prediction of the emergence and spread of an influenza epidemic, through sophisticated data collection and analysis of Online Social Networks (OSNs) such as Facebook, MySpace, and Twitter. In particular, we present the design of a system called SNEFT (Social Network Enabled Flu Trends), which will be developed in a 12-month SBIR (Small Business Innovation Research) project funded by the National Institutes of Health (NIH). We describe the innovative technologies that will be developed in this project for collecting and aggregating OSN data, extracting information from it, and integrating it with mathematical models of influenza. One of the monitoring tools used by the Centers for Disease Control and Prevention (CDC) is reports of Influenza-Like Illness (ILI) cases; these reports are authoritative but typically have a delay of one to two weeks due to the largely manual process. We describe the SNEFT prototype in the context of predicting ILI cases well in advance of the CDC reports. We observe that OSN data is individually noisy but collectively revealing, and speculate on other applications that can potentially be enabled by OSN data collection and analysis.

"Information at your fingertips anywhere, anytime" has been the driving vision of mobile computing for the past two decades. Through relentless pursuit of this vision, spurring innovations in wireless technology, energy-efficient portable hardware and adaptive software, we have now largely attained this goal. Ubiquitous email and Web access is a reality that is experienced by millions of users worldwide through their BlackBerries, iPhones, Windows Mobile, and other portable devices. Continuing on this road, mobile Web-based services and location-aware advertising opportunities have begun to appear, triggering large commercial investments. Mobile computing has arrived as a lucrative business proposition.

A mobile device like a smart phone is becoming one of main information processing devices for users these days. Using it, a user not only receives and makes calls, but also performs information tasks. However, a mobile device is still resource constrained, and some applications, especially work related ones, usually demand more resources than a mobile device can afford. To alleviate this, a mobile device should get resources from an external source. One of such sources is cloud computing platforms. Nevertheless an access to these platforms is not always guaranteed to be available and/or is too expensive to access them. We envision a way to overcome this issue by creating a virtual cloud computing platform using mobile phones. We argue that due to the pervasiveness of mobile phones and the enhancement in their capabilities this idea is feasible. We show prior evaluation results to support our concept and discuss future developments.

Mobile cloud computing applications run diverse workloads under diverse device platforms, networks, and clouds. Traditionally these applications are statically partitioned between weak devices and clouds, thus may be significantly inefficient in heterogeneous environments and workloads. We introduce the notion of dynamic partitioning of applications between weak devices and clouds and argue that this is key to addressing heterogeneity problems. We formulate the dynamic partitioning problem and discuss major research challenges around system support for dynamic partitioning.

This paper presents PrPl, a decentralized infrastructure that lets users participate in online social networking without loss of data ownership. PrPl, short for private-public, has a person-centric architecture--each individual uses a Personal-Cloud Butler service that provides a safe haven for one's personal digital assets and supports sharing with fine-grain access control. A user can choose to run the Butler on a home server, or use a paid or ad-supported vendor of his choice. Each Butler provides a federation of data storage; it keeps a semantic index to data that can reside, possibly encrypted, in other storage services. It uses the standard, decentralized OpenID management system, so users can use their established personas in accessing the data.

One pre-requisite to the success of a platform is the availability of applications, which means that ease of application development is essential. We have developed a language called SociaLite, based on Datalog, that allows developers to use a simple declarative database query to access the large collection of private data served up by the Butlers in our social circle running under different administrative domains.

We have developed a prototype of the PrPl infrastructure and implemented a number of simple social applications on the system. We found that the applications can be written in a small number of lines of code using SociaLite. Preliminary experimental results suggest that it is viable to enable sharing of private social data between close friends with a decentralized architecture.