Security by Spatial Reference:Using Relative Positioning to Authenticate Devices for Spontaneous Interaction

Spontaneous interaction is a desirable characteristic associated with mobile and ubiquitous computing. The aim is to enable users to connect their personal devices with devices encountered in their environment in order to take advantage of interaction opportunities in accordance with their situation. However, it is difficult to secure spontaneous interaction as this requires authentication of the encountered device, in the absence of any prior knowledge of the device. In this paper we present a method for establishing and securing spontaneous interactions on the basis of emphspatial references that capture the spatial relationship of the involved devices. Spatial references are obtained by accurate sensing of relative device positions, presented to the user for initiation of interactions, and used in a peer authentication protocol that exploits a novel mechanism for message transfer over ultrasound to ensures spatial authenticity of the sender

High precision hybrid RF and ultrasonic chirp-based ranging for low-power IoT nodes

Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning

Hardware prototyping and validation of a W-ΔDOR digital signal processor

Microwave tracking, usually performed by on ground processing of the signals coming from a spacecraft, represents a crucial aspect in every deep-space mission. Various noise sources, including receiver noise, affect these signals, limiting the accuracy of the radiometric measurements obtained from the radio link. There are several methods used for spacecraft tracking, including the Delta-Differential One-Way Ranging (ΔDOR) technique. In the past years, European Space Agency (ESA) missions relied on a narrowband ΔDOR system for navigation in the cruise phase. To limit the adverse effect of nonlinearities in the receiving chain, an innovative wideband approach to ΔDOR measurements has recently been proposed. This work presents the hardware implementation of a new version of the ESA X/Ka Deep Space Transponder based on the new tracking technique named Wideband ΔDOR (W-ΔDOR). The architecture of the new transponder guarantees backward compatibility with narrowband ΔDOR

BCB Based Packaging for Low Actuation Voltage RF MEMS Devices

This paper outlines the issues related to RF MEMS packaging and low actuation voltage. An original approach is presented concerning the modeling of capacitive contacts using multiphysics simulation and advanced characterization. A similar approach is used concerning packaging development where multi-physics simulations are used to optimize the process. A devoted package architecture is proposed featuring very low loss at microwave range

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models