A promising approach for improving the capacity of Wireless Mesh Networks is by making use of multiple non-overlapping RF channels. Multi-channel protocols have the advantage that several devices can transmit in parallel within a collision domain on distinct channels. When using IEEE 802.11b/g/a most protocol designers assume 3 and 12 non-overlapping channels, respectively. However, this simplified assumption does not hold. We present results from measurements that show that the number of available non-interfering channels depends on the antenna separation, PHY modulation, RF band, traffic pattern and whether single-or multi-radio systems are used. The problem is caused by Adjacent Channel Interference (ACI) where nearby transmitters "bleed over" to other frequencies and either cause spurious carrier sensing or frame corruption. For nearby transceivers, as in the factory defaults of multi-radio devices, this results in at most two noninterfering channels, one within 2.4 GHz and the other within the 5 GHz band. Only if the distance between the antennas is increased, non-interfering channels within the bands themselves become available. Moreover, our comparison of single-and multiradio systems allows us to isolate ACI from board crosstalk and radiation leakage of which only the multi-radio systems seem to suffer. Finally, we show how a packet-level simulator can be improved to realistically incorporate ACI. With the help of this simulator more confident statements about the performance of various multi-channel protocols can be made.

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The total cost of an Earthquake Early Warning System (EEWS) can be substantially decreased by using Wireless Mesh Networks (WMNs), which are inexpensive computer networks whose nodes communicate wirelessly using a license-free spectrum in a self-organized manner. The Early Warning System triggers on the small-amplitude, but fast P-wave in order to shutdown critical infrastructures before the destructive, but slow S-waves arrive only a few seconds later. It demands low-latency communications of high robustness. We conducted shakeboardbased measurements using IEEE 802.11a/b. Innovatively, our tests show that already for the slight shaking related to Pwaves representative for strong (Mw > 6) and nearby (epicentral distance < 40 km) earthquakes, the performance of the wireless communications can be considerably affected at the very moment when the Early Warning system is supposed to be used. We observed swift link quality oscillations of up to 10 dB within only half a second. The more an environment is vulnerable to multi-path interference and shadow fading, e.g. no line of sight (NLOS), the more erratic are the wireless links between nodes. However, for clear line of sight (LOS) the influence of the vibrations is negligible. We recommend several measures that should be applied in order to make the unique use case of Earthquake Early Warning, nonetheless, well-functioning on top of a Wireless Mesh Network. A higher fade margin, in our setup at least an additional 5 dB, should be included to cope with sudden link fading. Moreover, antenna diversity should be enabled as it strongly mitigates the adverse effects of shaking.

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&lt;p&gt;The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany&amp;#8217;s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD&amp;#8217;s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the &amp;#8216;ground truth&amp;#8217; and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.&lt;/p&gt;