Theses Doctoral

Multi-Mobile Computing

AlDuaij, Naser Y. A. A.

With mobile systems evermore ubiquitous, individual users often own multiple mobile systems and groups of users often have many mobile systems at their disposal. As a result, there is a growing demand for multi-mobile computing, the ability to combine the functionality of multiple mobile systems into a more capable one. However, there are several key challenges. First, mobile systems are highly heterogeneous with different software and hardware, each with their own interfaces and data formats. Second, there are no effective ways to allow users to easily and dynamically compose together multiple mobile systems for the quick interactions that typically take place with mobile systems. Finally, there is a lack of system infrastructure to allow existing apps to make use of multiple mobile systems, or to enable developers to write new multi-mobile aware apps. My thesis is that higher-level abstractions of mobile operating systems can be reused to combine heterogeneous mobile systems into a more capable one and enable existing and new apps to provide new functionality across multiple mobile systems.

First, we present M2, a system for multi-mobile computing that enables existing unmodified mobile apps to share and combine multiple devices, including cameras, displays, speakers, microphones, sensors, GPS, and input. To support heterogeneous devices, M2 introduces a new data-centric approach that leverages higher-level device abstractions and hardware acceleration to efficiently share device data, not API calls. M2 introduces device transformation, a new technique to mix and match heterogeneous devices, enabling, for example, existing apps to leverage a single larger display fused from multiple displays for better viewing, or use a Nintendo Wii-like gaming experience by translating accelerometer to touchscreen input. We have implemented M2 and show that it operates across heterogeneous systems, including multiple versions of Android and iOS, and can run existing apps across mobile systems with modest overhead and qualitative performance indistinguishable from using local device hardware.

Second, we present Tap, a framework that leverages M2’s data-centric architecture to make it easy for users to dynamically compose collections of mobile systems and developers to write new multi-mobile apps that make use of those impromptu collections. Tap allows users to simply tap systems together to compose them into a collection without the need for users to register or connect to any cloud infrastructure. Tap makes it possible for apps to use existing mobile platform APIs across multiple mobile systems by virtualizing data sources so that local and remote data sources can be combined together upon tapping. Virtualized data sources can be hardware or software features, including media, clipboard, calendar events, and devices such as cameras and microphones. Leveraging existing mobile platform APIs make it easy for developers to write apps that use hard- ware and software features across dynamically composed collections of mobile systems. We have implemented Tap and show that it provides good usability for dynamically composing multiple mobile systems and good performance for sharing hardware devices and software features across multiple mobile systems.

Finally, using M2 and Tap, we present various apps that show how existing apps can provide useful functionality across multiple mobile systems and how new apps can be easily developed to provide new multi-mobile functionality. Examples include panoramic video recording using cameras from multiple mobile systems, surround sound music player app that configures itself based on automatically detecting the location of multiple mobile systems, and an added feature to the Snapchat app that allows multiple users to share a live Snap, using their own cameras and filters. Our user studies with these apps show that multi-mobile computing offers a richer and more enhanced experience for users and a much simpler development effort for developers.


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More About This Work

Academic Units
Computer Science
Thesis Advisors
Nieh, Jason
Ph.D., Columbia University
Published Here
July 1, 2020