ALBERT

Description: ERA-PLANET is a wide European network comprised of 118 researchers from 35 partner institutions located in 18 countries, aiming to strengthen the European Research Area in the domain of Earth Observation (EO) in coherence with the European participation to the Group on Earth Observation (GEO) and the program for the establishment of a European capacity for Earth Observation, COPERNICUS. It will provide more accurate, comprehensive, and authoritative information to policy and decision-makers in key societal benefit areas (SBAs), under the umbrellas of dedicated projects in the topics of: smart cities and resilient societies; resource efficiency and environmental management; global changes and environmental treaties; polar areas and natural resources. ERA-PLANET will provide advanced decision support tools and technologies aimed to better monitor our global environment and share the information and knowledge in different domains of EO by launching joint transnational calls along the above four strands. The concept of the project that tackles with strand 1, as well as an example of a specific application fitting in, are described, aspiring to promote and coordinate the “smart-city” approach into a European network of cities and non-European follower cities, serving the need for a common approach to enhance environmental and societal resilience to air pollution, urban growth, and urban heat islands, as well as other natural/manmade stresses and relevant impacts. This is achieved through the synergy among technology, government, and society, while at the same time creating bridges between local/national initiatives with GEO/GEOSS, COPERNICUS, and other smart cities and GEO relevant projects. The project addresses initiatives in European cities but also specific issues dealing with air quality management in other parts of the world. Finally, it places major emphasis on fully exploiting key-enabling technologies and firmly addressing interoperability issues, in the context of big “smart city” data, and open science.

Description: 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.