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.

Description: This research work investigates how RSS information fusion from a single, multi-antenna access point (AP) can be used to perform device localization in indoor RSS based localization systems. The proposed approach demonstrates that different RSS values can be obtained by carefully modifying each AP antenna orientation and polarization, allowing the generation of unique, low correlation fingerprints, for the area of interest. Each AP antenna can be used to generate a set of fingerprint radiomaps for different antenna orientations and/or polarization. The RSS fingerprints generated from all antennas of the single AP can be then combined to create a multi-layer fingerprint radiomap. In order to select the optimum fingerprint layers in the multilayer radiomap the proposed methodology evaluates the obtained localization accuracy, for each fingerprint radio map combination, for various well-known deterministic and probabilistic algorithms (Weighted k-Nearest-Neighbor—WKNN and Minimum Mean Square Error—MMSE). The optimum candidate multi-layer radiomap is then examined by calculating the correlation level of each fingerprint pair by using the “Tolerance Based—Normal Probability Distribution (TBNPD)” algorithm. Both steps take place during the offline phase, and it is demonstrated that this approach results in selecting the optimum multi-layer fingerprint radiomap combination. The proposed approach can be used to provide localisation services in areas served only by a single AP.

Description: Planning and deploying a functional large scale Wireless Sensor Network (WSN) or a Network of Internet of Things (IoTs) is a challenging task, especially in complex urban environments. A main network design bottleneck is the existence and/or correct usage of appropriate cross layer simulators that can generate realistic results for the scenario of interest. Existing network simulators tend to overlook the complexity of the physical radio propagation layer and consequently do not realistically simulate the main radio propagation conditions that take place in urban or suburban environments, thus passing inaccurate results between Open Systems Interconnection (OSI) layers. This work demonstrates through simulations and measurements that, by correctly passing physical information to higher layers, the overall simulation process produces more accurate results at the network layer. It is demonstrated that the resulting simulation methodology can be utilized to accomplish realistic wireless planning and performance analysis of the deployed nodes, with results that are very close to those of real test-beds, or actual WSN deployments.