Received Signal Strength Indicator (RSSI)-based fingerprinting is currently viewed as an important technique for the positioning capabilities in the Internet of Things (IoT). However, in the case of practical measurement, the localization methods based on RSSI are easily affected by the temporal and spatial variation, which contributes to most of the estimation errors in current systems. In this paper, the feasibility of utilizing the Channel State Information (CSI) for localization is studied, after knowing that the CSI contains information about the channel between the sender and receiver at the level of individual data subcarriers. Unlike most of the previous work, the intended approach is to use the entire subcarrier magnitudes without averaging or any reduction of the obtained narrowband CSI. Moreover, the frequency hopping in the LoRa systems should be a profit for localization by getting access to a wider band. In order to obtain a reliable basis for this approach, an outdoor measurement campaign is performed in the area of the Campus Beaulieu in Rennes to estimate the CSI of transmitted LoRa signals from different locations. For this, it is necessary for the individual channels from each different position to be appropriately different from one another to achieve significant localization gain. Hence, a comparison is done investigating the attainable evolution in the CSI at each location based on the CSI slope versus its average amplitude. In the given results, the feasibility of using the proposed technique is asserted by the drastic stability of the CSI slope over time and space, in contrary to the CSI average amplitude. This manifests the robustness of the CSI to the signal fluctuations and its more valuable rendering than the RSSI.


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    Title :

    A Robustness Comparison of Measured Narrowband CSI vs RSSI for IoT Localization


    Contributors:


    Publication date :

    2020-11-01


    Size :

    2867344 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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