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Measurement setup IV. EVALUATION OF IWSN PRESENCE DETECTION To validate the proposed approach, presence detection is evaluated in an environment comparable to Fig. 1. Note that presence detection requires path nodes that are installed along the area of interest. Thus, in this chapter, the installment of path nodes, the performance of the algorithms and their robustness towards attacks is investigated.
Source publication
We propose to add a monitoring system consisting of so-called path- and guard nodes to industrial wireless sensor network (IWSN), to increase the security level by using RSSI measurements. Via these measurements, the monitoring system determines the presence of a mobile sensor node in a predefined area, which can be used to handle access rights and...
Citations
... For many use cases, it is necessary to record the spatial position of the wireless sensor in addition to its measured value. As an example, we present an extension to an IWSNbased measurement system [4] for the emission certification of cars according to the Euro 6 standard which traces the required measurements in time and position [5]. During these tests, cars are moved between differently conditioned areas and for the position tracking a non-interfering add-on-localization extends This work is funded by the InSecTT project (https://www.insectt.eu/). ...
Industrial wireless sensor networks are becoming crucial for modern manufacturing. If the sensors in those networks are mobile, the position information, besides the sensor data itself, can be of high relevance. E.g. this position information can increase the trustability of a wireless sensor measurement by assuring that the sensor is not physically removed, off track, or otherwise compromised. In certain applications, localization information at cell-level, whether the sensor is inside or outside a room or cell, is sufficient. For this, localization using Received Signal Strength Indicator (RSSI) measurements is very popular since RSSI values are available in almost all existing technologies and no direct interaction with the mobile sensor node and its communication in the network is needed. For this scenario, we propose methods to improve the robustness and accuracy of common machine learning classifiers, by using features based on short-term moments and a second classification stage using Hidden Markov Models. With the data from an extensive measurement campaign, we show the applicability of our method and achieve a cell-level localization accuracy of 93.5\%.
... For many use cases, it is necessary to record the spatial position of the wireless sensor in addition to its measured value. As an example, we present an extension to an IWSNbased measurement system [4] for the emission certification of cars according to the Euro 6 standard which traces the required measurements in time and position [5]. During these tests, cars are moved between differently conditioned areas and for the position tracking a non-interfering add-on-localization extends This work is funded by the InSecTT project (https://www.insectt.eu/). ...
For Cyper-Physical Production Systems (CPPS), localization is becoming increasingly important as wireless and mobile devices are considered an integral part. While localizing targets in a wireless communication system based on the Received Signal Strength Indicators (RSSIs) is a usual solution, it is limited by sensor quality. We consider the scenario of a car moving in and out of a chamber and propose to use a particle filter for sensor fusion, allowing us to incorporate non-idealities in our model and achieve a high-quality position estimate. Then, we use Machine Learning (ML) to classify the vehicle position. Our results show that the location output of the particle filter is a better input to the classifiers than the raw RSSI data, and we achieve improved accuracy while simultaneously reducing the number of features that the ML has to consider. We also compare the performance of multiple ML algorithms and show that SVMs provide the overall best performance for the given task.
