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In this paper, we investigate the impacts of location on the spectral distribution of received light, i.e., the intensity of light for different wavelengths, in indoor environments. Our findings show that, even when using the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. Based on this observation, we present Spectral-Loc, a novel indoor localization method that employs light spectrum information to detect the device's position. Because spectrum sensors are increasingly being used in new products and applications, such as white balance in smartphone photography, Spectral-Loc can be quickly implemented without the need for extra hardware or infrastructure. We used a commercially available light spectrum sensor, the AS7265x, to prototype Spectral-Loc, which can measure light intensity over 18 different wavelength sub-bands.We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations in two indoor areas, a meeting room and a large office, show that using light spectral information considerably decreases the localization error for different percentiles. [ABSTRACT FROM AUTHOR]

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate OmniCells: a cellular-based indoor localization system designed to combat the device heterogeneity problem. OmniCells is a deep learning-based system that leverages cellular measurements from one or more training devices to provide consistent performance across unseen tracking phones. In this demo, we show the effect of device heterogeneity on the received cellular signals and how this leads to performance deterioration of traditional localization systems. In particular, we show how OmniCells and its novel feature extraction methods enable learning a rich and device-invariant representation without making any assumptions about the source or target devices. The system also includes other modules to increase the deep model's generalization and resilience to unseen scenarios. [ABSTRACT FROM AUTHOR]

Spectrum regulators consider geo-location databases as the most reliable source of spectrum information for White Space Devices (WSDs). Geo-location databases protect TV band incumbents by keeping track of TV transmitters, and their protected service areas based on their location, transmission parameters, and sophisticated propagation models. However, propagation models inaccuracies can cause an overestimation of the protected area of TV transmitters leading to the inefficient usage of white spaces. In this paper, we present a large scale study, spanning an area of around 3000 km2 over a driving path of around 190 km, showing that one of the most accurate propagation models, the Irregular Terrain Model (IMT), overestimates the signal power by up to 97% of the time. Based on this study, we provide a characterization of spectrum sensory readings that can be used to amend the prediction of propagation models. This characterization allows spectrum sensors to detect the absence of white spaces with a fairly high threshold of -84 dbm, which enables low cost and accurate spectrum sensing. Furthermore, we present the initial design of SPOC, a system that combines spectrum sensing and propagation modeling in order to better detect white spaces. [ABSTRACT FROM AUTHOR]

Device-free passive (DfP) localization has been recently proposed to allow localizing a stationary entity that neither carries a device nor participates actively in the localization process. In this paper, we present a Kalman filter-based system that enables tracking a continuously moving entity in a typical wireless environment rich in multipath. The concept behind DfP tracking is that the received signal strength at monitoring points in a wireless environment is influenced by any changes in the environment. These changes include the movement of an entity, such as a human being, within the environment. This can be utilized to track an entity in many military and civil applications. We evaluate the performance of our system by conducting experiments in a typical office environment rich in multipath. Our results show that we were able to track a passive entity in a long corridor with a 1.2m median distance error and a less than 4.6m distance error with probability one. [ABSTRACT FROM AUTHOR]

Building large-scale testbeds for cognitive radio networks has been identified as one of the main challenging problems and basic needs for advancing cognitive radio research. The cost of buying and maintaining a large-scale cognitive radio networks using special RF interfaces can not be ignored. In this paper we introduce CogFrame, a framework that facilitates large-scale deployment of cognitive routing protocols by enabling the deployment of these protocols on off-the-shelf laptops nodes using their built-in Wi-Fi interfaces for RF communication. The framework is built on top on the Click modular router to ensure the easy of development of new protocols while providing new modules to handle cognitive radio functionalities. [ABSTRACT FROM AUTHOR]

Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. We present a demonstration of Spot as an accurate and efficient system for multi-entity DF detection and tracking. Spot is based on a probabilistic energy minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities' poses. A novel cross-calibration technique is used to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. The demo presents the different components of the system in action. [ABSTRACT FROM AUTHOR]

We demonstrate a practical and provably-secure multi-node communication scheme in the presence of a passive eaves- dropper. The scheme is based on a random scheduling approach that hides the identity of the transmitter from the eavesdropper. This random scheduling leads to ambiguity at the eaves- dropper with regard to the origin of the transmitted frame. We demonstrate the internal details of the technique and quantify the secrecy-overhead tradeoff. Our implementation is based on Crossbow Telosb motes, equipped with CC2420 radio chips. The demo will allow the attendees to see the scheme in action and how the scheme can achieve significant secrecy gains with vanishing outage probability. The demo also compared the proposed scheme to other direct extensions the two node case. [ABSTRACT FROM AUTHOR]

Current methods for building radio maps for wireless localization systems require a tedious, manual and error-prone calibration of the area of interest. Each time the layout of the environment is changed or different hardware is used, the whole process of location fingerprinting and constructing the radio map has to be repeated. The process gets more complicated in the case of localizing multiple entities in a device-free scenario, since the radio map needs to takeall possible combinations of the location of the entities into account. In this demo, we present a novel system (AROMA) that is capable of generating accurate radio maps for a given site of interest. AROMA constructs radio maps for both deterministic and probabilistic wireless localization systems. According to our knowledge, AROMA is the first system to generate radio maps for device-free localization. It uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. AROMA also automates a number of routine tasks, such as importing building models and automatic sampling of the area of interest. [ABSTRACT FROM AUTHOR]