We present a novel radio-frequency identification (RFID) system with capability of localization and tracking of passive or semi-passive tags. Localization and tracking features are enabled by backscatter modulation on ultra-wide bandwidth tag's antenna. A ultra-high frequency signal allows the wake-up of the tags enabling the reduction of energy consumption and ensuring compatibility with existing RFID systems. The overall system as well as the reader and tag architectures are introduced. The localization and tracking performance evaluation is presented in some reference scenarios

In the past couple of years, sensor networks have evolved to ap owerful infrastructure component for monitoring and tracking events and phenomena in many application domains. An important task in processing streams of sensor data is the detection of anomalies, e.g., outliers or bursts, and in particular the computation of the location and spatial extent of such anomalies in as ensor network. In this paper, we present an approach that facilitates the efficient computation of such anomaly regions from sensor readings. We propose an algorithm to derive spatial regions from individual anomalous sensor readings, with aparticular focus on obstacles present in the sensor network. We improve this approach by proposing adistributed in-network processing technique where the region detection is performed at the sensor nodes. We demonstrate the advantages of this strategy overac entralized processing strategy by utilizing acost model for real sensors and sensor networks. 1I ntroductionDrivenb ym ajor advancements in sensor technology,s everal sensor networks have been and are being deployed in various application domains such as the monitoring of traffic, buildings, rivers, and the environment in general. Typical examples for environmental monitoring include precision agriculture (e.g., observing the humidity of the soil) and monitoring particles in urban areas to react to changes in air quality measures. An important objective in processing sensor data is the detection of anomalies that occur,e.g., in the form of outliers or bursts. This kind of data processing and analysis not only reduces the volume of data reaching end user applications butitalso simplifies the further processing and interpretation of the sensor data.By analyzing individual and aggregated sensor measurements, one can obtain useful information about the locations where anomalous events and phenomena occur.Such location information then can be visualized on amap and interpreted for individual sensors. In particular it can be used to derive anomaly regions.Such regions are composed of neighboring *

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Detecting bursts in data streams is an important and challenging task. Due to the complexity of this task, usually burst detection cannot be formulated using standard query operators. Therefore, we show how to integrate burst detection for stationary as well as non-stationary data into query formulation and processing, from the language level to the operator level. Afterwards, we present fundamentals of threshold-based burst detection. We focus on the applicability of time series forecasting techniques in order to dynamically identify suitable thresholds for stream data containing arbitrary trends and periods. The proposed approach is evaluated with respect to quality and performance on synthetic and real-world sensor data using a full-fledged DSMS.