Wireless connectivity is present in the everyday live. Wireless networks enable many emerging mobile applications, such as:

• Wireless Sensor Networks, e.g., for Mobile Healthcare
• Smart Homes
• Internet of Things
• Object Tracking

Reducing Energy Consumptions in Wireless Systems

To realize such applications, the RCS is committed to develop techniques for reducing the energy-consumption of wireless networks.  In particular, we are working on the following techniques:

• Energy-Efficient Protocol Design
  One of the largest energy consumers of wireless devices are their wireless communication interfaces. At the RCS, we analyze wireless protocols to identify potentials for saving energy, design efficient protocols and derive and implement appropriate power-management-techniques.
  
  Before two devices can exchange data, they have to discover each other for the first time and synchronize their clocks. This asynchronous communication procedure is called neighbor discovery. It is one of the most energy-expensive operations in wireless networks. Therefore, we are working on energy-efficient protocols for neighbor discovery. In this field, we have developed the first theory to analyze periodic interval-based protocols, such as Bluetooth Low Energy (BLE) and ANT/ANT+. In addition, our protocols PI-kM+-Opt and Griassdi can achieve one order of magnitude faster discoveries than the best known previously known solutions, while consuming the same amount of energy.
  
  After two devices have discovered each other, they follow a synchronous communication schedule in many protocols, e.g., BLE. By adjusting this schedule online based on the traffic requirements, we can reduced the energy-consumption by up to 40%. In addition, our technique to exploit the movements of a human body can be used to reduce the transmit power in body-worn networks, which saves additional energy.
  
  
• Energy-Efficient Wireless Sensor Nodes
  At the RCS, we develop custom sensors and analyze existing ones for potential techniques to reduce the energy consumption. Our sensor E1CH has the size of a coin and can sense acceleration or perform localization tasks for multiple weeks without changing the battery. To characterize its energy-consumption in mobile networks, we have designed EG0N, a mobile power monitoring device.
  
  
• Data Compression in Wireless Networks
  If signals are compressed before being sent wirelessly, the energy a mobile device spends on processing is increased, while the energy for transmission can be reduced. However, most existing compression schemes are optimized for high compression rates, while neglecting the required processing effort and memory footprint. For mobile networks, a trade-off between high compression efficiencies (and hence low transmission energies) and low processing effort (and hence low energies for processing) is required. Further, mobile devices mostly have limited memories. At the RCS, we develop compression schemes that minimize the joint energy-consumption for processing and wireless transmission, while realizing small memory footprints.
  
  
• Next Generation System Architectures
  Currently, the firmwares of mobile wireless devices are considered to be monolithic and fixed. This also implies that the functionalities of such devices are predefined. At the RCS, we work on flexible architectures that can replace or install parts of the firmware during runtime. Thus, the functionality can be defined by application concepts, similar to smartphone apps. Towards this, we are working on concepts for installing code wirelessly and executing code in an efficient manner. Further, such architectures open up flexible power-management schemes, in which the code for the power-management can be exchanged on the fly during runtime. By doing so, a distributed power-management systemin a wireless network can adapt to changing network topologies, incidents or states of operation.

Estimating the energy consumption of the wireless interface is a crucial design requirement for low power devices. The RCS offers tools for energy estimation.

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