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Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.

Abstract-This work proposes the deployment of Frequency Selective Surfaces (FSS) in indoor wireless environments and investigates their effect on radio wave propagation. FSS can be deployed to selectively confine radio propagation in indoor areas, by artificially increasing the radio transmission loss naturally caused by building walls. FSS can also be used to channel radio signals into other areas of interest. Simulations and measurements have been carried out in order to verify the frequency selectivity of the FSS. Practical considerations regarding the deployment of FSS on building walls and the separation distance between the FSS and the supporting wall have been also investigated. Finally, a controlled, small-scale indoor environment has been constructed and measured in an anechoic chamber in order to practically verify this approach through the usage of ray tracing techniques.