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Internet of Things (CPS/Industry 4.0)

The Internet of Things (IoT) envisions a world where virtually everything is connected including billions of mobile devices and also everyday devices like cars, smart homes, wearable devices like smart glasses and watches, or industrial automation systems following the trend of Industry 4.0. Sensors embedded into these devices sense the state of users, objects, and ultimately the world to enable context-aware applications that react automatically to the current situation without manual intervention by the user.

In our research, we investigate concepts to enable the IoT as well as concepts utilizing the huge number of IoT devices to implement novel services and applications.

 

Sensing and Control

Networked IoT devices featuring embedded sensors can deliver a huge amount sensor data to ultimately capture the state of the world. 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. However, installing a dedicated, fixed sensor network spanning a large area is costly. Therefore, we investigate public sensing concepts utilizing the crowd of already existing mobile devices like smartphones or novel wearable devices like smart glasses to implement a huge mobile sensor network with great coverage in urban areas. This setup is augmented by cheap wireless sensor devices based, for instance, on the BLE standard, utilizing mobile devices as gateways to the Internet. In particular, we develop concepts to face four essential challenges of public sensing:

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

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

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

  • protecting the privacy of users in the IoT, which might collect privacy-sensitive user data

With respect to applications based on public sensing, we investigate how to automatically build detailed 2D and 3D indoor models from crowd-sensed data collected by mobile users.

Moreover, we investigate concepts to implement so-called cyber-physical systems (CPS), which monitor the state of physical objects through sensors and control their physical state through a set of actuators. Physical objects to be controlled include, for instance, machines in an “Industry 4.0” factory, a fleet of cars, smart home devices, or energy consumers and producers in the smart grid. In particular, we are interested in CPS consisting of a set of geographically distributed networked sensors, actuators, and controllers implementing a networked control system, which is typically highly sensitive to the delay and loss of communicated sensor data. Therefore, operating a networked control systems over the Internet, which was not designed to support such time-sensitive applications, is a great challenge. To this end, we investigate networking concepts optimizing the quality of control of networked control systems operating on top of an unreliable IP-based networking infrastructure like the Internet.

Cloud Computing for the IoT

Cloud computing offers scalable infrastructures that are well-suited to handle the huge amount of data stemming from the large number of IoT devices. In our research, we tackle essential challenges for utilizing cloud computing for the IoT:

  • Quality of service: Designing a cloud computing infrastructure offering predictable quality of service with respect to availability, latency. etc. at minimum cost, i.e., without heavy over-provisioning.

  • Fog / Edge Cloud Computing: reducing the latency of geo-distributed IoT services by distributing functions and data between remote cloud data centers and edge cloud (“fog”) resources close to the devices.

  • Mobile cloud computing: increasing the energy-efficiency of battery-operated, mobile IoT devices by dynamically offloading computations from the resource-constrained device to the cloud or fog.

Simulation

Most IoT devices embed sensors that can feed simulations enabling novel applications. In particular, we are interested in simulations utilizing mobile devices, allowing for instance to execute simulations “in the field”. A typical example could be structural engineer who wants to simulate the current state of a building, bridge, or similar based on collected sensor values. Using a mobile augmented-reality headsets like Microsoft’s HoloLens, the engineer can visualize simulation results in real-time, and evaluate planned changes to the architecture on-site.  

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.