Mobile Computations with Surrounding Devices: Proximity Sensing and MultiLayered Work Stealing

With the proliferation of mobile devices, and their increasingly powerful embedded processors and storage, vast resources increasingly surround users. We have been investigating the concept of on-demand ad hoc forming of groups of nearby mobile devices in the midst of crowds to cooperatively perform computationally intensive tasks as a service to local mobile users, or what we call mobile crowd computing. As devices can vary in processing power and some can leave a group unexpectedly or new devices join in, there is a need for algorithms that can distribute work in a flexible manner and still work with different arrangements of devices that can arise in an ad hoc fashion. In this article, we first argue for the feasibility of such use of crowd-embedded computations using theoretical justifications and reporting on our experiments on Bluetooth-based proximity sensing. We then present a multilayered work-stealing style algorithm for distributing work efficiently among mobile devices and compare speedups attainable for different topologies of devices networked with Bluetooth, justifying a topology-flexible opportunistic approach. While our experiments are with Bluetooth and mobile devices, the approach is applicable to ecosystems of various embedded devices with powerful processors, networking technologies, and storage that will increasingly surround users.