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Mobile Systems / Ubiquitous Computing

With the advent of smartphones and tablets, mobile devices became ubiquitous in our everyday life. Moreover, the development of wearable devices such as smart glasses, smart watches, and fitness trackers, as well as the trend towards including substantial computing capabilities into mobile objects like cars will further contribute to increase the population of mobile devices, which already today includes billions of devices.

In our research, we investigate concepts to support and enable mobile and ubiquitous computing systems and applications.

 

Sensing and Control

Many mobile devices feature sensors to capture the state of the environment surrounding the mobile user or object (e.g., car). This sensor information is valuable for many applications including, for instance, traffic monitoring, urban planning, environmental monitoring, or the efficient management of resources like electricity. Utilizing the crowd of mobile devices for “public” sensing, we can implement a huge sensor network with great coverage without the cost of installing a dedicated, fixed sensor network.

In our research, we investigate concepts to enable the public sensing paradigm:

  • saving energy of battery-operated mobile devices involved in sensing

  • delivering a certain quality of sensor data through a crowd of unreliable mobile devices of private users

  • managing a huge crowd of mobile devices, e.g., to track their locations   

  • protecting the location privacy of mobile users

Besides these generic challenges, we also target concepts to support a specific public sensing applications for automatically building detailed 2D and 3D indoor models from crowd-sensed data such as movement trajectories, photos, or 3D point clouds collected by mobile users.

Mobile Cloud Computing

Mobile cloud computing aims for supporting the implementation of mobile systems by utilizing the virtually unlimited resources of cloud computing. In our research, we target two specific goals of mobile cloud computing:

  • Increasing the energy-efficiency of battery-operated mobile devices by dynamically offloading computations from the resource-constrained device to the remote cloud or a nearby edge cloud computing infrastructure (“fog”).

  • Utilizing non-trusted third-party cloud infrastructures to manage privacy-sensitive user information. In particular, we investigate concepts to protect the location privacy of users storing their location data in the cloud.

Mobile Simulation

Novel mobile devices including, for instance, augmented-reality headsets such as Microsoft’s HoloLens, allow for visualizing simulation results in an intuitive manner as an overlay of the physical world. This not only enables novel gaming and entertainment applications, but also serious engineering applications. For instance, a structural engineer could simulate the current state of a building, bridge, etc. and evaluate planned changes to the architecture on-site using his mobile headset.  

Typically, simulations are compute-intensive requiring both, long time to calculate on a mobile device and lots of limited battery resources. To solve these problems, we investigate how to enable distributed mobile simulations. The basic idea is to offload parts of the simulation to a powerful remote cloud computing infrastructure or a nearby edge cloud infrastructure (“fog”) to increase efficiency. Using optimized simulation methods and a careful consideration of the trade-off between communication overhead, computation overhead, and required quality of simulation results, we can achieve a significant reduction of energy consumption and execution time to bring even complex simulations to mobile devices.