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Interconnecting Intelligent Electricity Meters using Wireless Self-Organizing Mesh Technology

Contact Prof. Dr. rer. nat. Volker Turau
Start 16. December 2010
Financing German Federal Ministry of Education and Research

Project Description

Goal of the project iEZMesh is the development and validation of the architecture, hardware, and protocols for a novel wireless sensor network enabling automatic and low-maintenance readout and configuration of electricity meters. This sensor network will be self-organizing and rely on electricity meters as routers. All radio modules must be integrated into and tighly coupled with the existing hardware in order to retain the existing infrastructure. Furthermore, the system is required to be highly scalable even in metropolitan areas with networks consisting of tenth of thousands of electricity meters. Reliability, robustness and efficiency of the network have to meet the required standards even in shielding and interference-prone environments, in which electricity meters are installed prevalently. The protection of data privacy and enforcement of data integrity are additional requirements of the system design; collecting and propagating fine-grained consumption data provide an insight into the lifes of humans.

Project partner is the German company EMH metering. The results of project will be integrated into the business line of EMH metering. iEZMEsh is funded by the German Federal Ministry of Education and Research.


Martin Ringwelski, Christian Renner, Andreas Reinhardt, Andreas Weigel and Volker Turau. The Hitchhiker's Guide to Choosing the Compression Algorithm for Your Smart Meter Data. In Proceedings of the IEEE International Energy Conference and Exhibition (EnergyCon'12), September 2012. Florence, Italy.
@InProceedings{Telematik_RRWRT_2012_CompressionGuide, author = {Martin Ringwelski and Christian Renner and Andreas Reinhardt and Andreas Weigel and Volker Turau}, title = {The Hitchhiker's Guide to Choosing the Compression Algorithm for Your Smart Meter Data}, booktitle = {Proceedings of the IEEE International Energy Conference and Exhibition (EnergyCon'12)}, day = {9-12}, month = sep, year = 2012, location = {Florence, Italy}, }
Abstract: Smart meters are increasingly penetrating the market, resulting in enormous data volumes to be communicated. In many cases, embedded devices collect the metering data and transmit them wirelessly to achieve cheap and facile deployment. Bandwidth is yet scarce and transmission occupies the spectrum. Smart meter data should hence be compressed prior to transmission. Here, solutions for personal computers are not applicable, as they are too resource-demanding. In this paper, we propose four lossless compression algorithms for smart meters. We analyze processing time and compression gains and compare the results with five off-the-shelf compression algorithms. We show that excellent compression gains can be achieved when investing a moderate amount of memory. A discussion of the suitability of the algorithms for different kinds of metering data is presented.
Christian Renner, Sebastian Ernst, Christoph Weyer and Volker Turau. Prediction Accuracy of Link-Quality Estimators. In Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN'11), February 2011. Bonn, Germany. Acceptance rate 20%.
@InProceedings{Telematik_REWT_HoPS, author = {Christian Renner and Sebastian Ernst and Christoph Weyer and Volker Turau}, title = {Prediction Accuracy of Link-Quality Estimators}, booktitle = {Proceedings of the 8th European Conference on Wireless Sensor Networks (EWSN'11)}, day = {23-25}, month = feb, year = 2011, location = {Bonn, Germany}, note = {Acceptance rate 20%}, }
Abstract: The accuracy of link-quality estimators (LQE) is mission-critical in many application scenarios in wireless sensor networks (WSN), since the link-quality metric is used for routing decisions or neighborhood formation. Link-quality estimation must offer validity for different timescales. Existing LQEs describe and approximate the current quality in a single value only. This method leads to a limited accuracy and expressiveness about the presumed future behavior of a link. The LQE developed in this paper incorporates four quality metrics that give a holistic assessment of the link and its dynamic behavior; therefore, this research is an important step to achieving a higher prediction accuracy including knowledge about the short- and long-term behavior.