Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

Participatory location fingerprinting through stationary crowd in a public or commercial indoor environment

The training phase of indoor location fingerprinting has been traditionally performed by dedicated surveyors in a manner that is time and labour intensive. Crowdsourcing process is more efficient, but is impractical in public or commercial buildings because it requires occasional location fix provided explicitly by the participant, the availability of an indoor map for correlating the traces, and the existence of landmarks throughout the area. Here, we address these issues for the first time in this context by leveraging the existence of stationary crowd that have timetabled roles, such as desk-bound employees, lecturers and students. We propose a scalable and effortless positioning system in the context of a public/commercial building by using Wi-Fi sensor readings from its stationary occupants' smartphones combined with their timetabling information. Most significantly, the entropy concept of information theory is utilised to differentiate between good and spurious measurements in a manner that does not rely on the existence of known trusted users. Our analysis and experimental results show that, regardless of such participants' unpredictable behaviour, including not following their timetabling information, hiding their location or purposefully generating wrong data, our entropy-based filtering approach ensures the creation of a radio-map incrementally from their measurements. Its effectiveness is validated experimentally with two well-known machine learning algorithms

Enabling environmental fingerprinting with an NFC-powered sensor board

Abstract. In recent times, people have become concerned about their environmental conditions, amid deteriorating global statistics on bad air quality, global warming and UV light exposure. Conventional technologies for reading environmental conditions are expensive, bulky and situated, yet, people are mobile and need portable tools to be aware of their immediate environmental conditions on demand. Smartphones are now widely used, endowed with sensors and wireless communication technologies such as Bluetooth, and Near Field Communication (NFC) for external sensor connectivity, making smartphones a viable tool for fingerprinting the environment. This thesis outlines the design, evaluation and implementation of a mobile-enabled system for environmental data collection using a portable NFC powered sensor board. The name of the system developed in this thesis is the S3 system. The S3 system is a two-tier system which consists of S3 Android application and an online dashboard with a data repository. The S3 Android application is used for collecting and visualising environmental data; temperature, humidity, UV, ambient light, with a smartphone and a credit card-size NFC powered sensor board. The sensor data is then periodically synced to the online data repository. Additional features of the S3 application include automated feedback sampling, introductory tutorial, and user preference settings. The thesis further details the design and implementation process with scenarios, use cases, paper sketches, expert review of sketches, interface mockups, evaluation of prototype with a user study, quantitative and qualitative analysis of user study data, and finally the implementation of the S3 application. The thesis also presents a test run to demonstrate the capabilities of the S3 system as a mobile-enabled solution for crowdsourced environmental fingerprint datasets. To the end user, the work in this thesis provides the S3 application and the NFC powered sensor card as a portable tool for personalised environmental fingerprinting. On the other hand, the intervention in this thesis will have an impact on research since the crowdsourced environmental fingerprint datasets can be valuable datasets for research. As a TEKES project, the solution also provides a proof of concept for further improvement and deployment into the commercial software market

ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed

IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT

With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead

Crowdsourced Reconstruction of Cellular Networks to Serve Outdoor Positioning: Modeling, Validation and Analysis

Positioning via outdoor fingerprinting, which exploits the radio signals emitted by cellular towers, is fundamental in many applications. In most cases, the localization performance is affected by the availability of information about the emitters, such as their coverage. While several projects aim at collecting cellular network data via crowdsourcing observations, none focuses on information about the structure of the networks, which is paramount to correctly model their topology. The difficulty of such a modeling is exacerbated by the inherent differences among cellular technologies, the strong spatio-temporal nature of positioning, and the continuously evolving configuration of the networks. In this paper, we first show how to synthesize a detailed conceptual schema of cellular networks on the basis of the signal fingerprints collected by devices. We turned it into a logical one, and we exploited that to build a relational spatio-temporal database capable of supporting a crowdsourced collection of data. Next, we populated the database with heterogeneous cellular observations originating from multiple sources. In addition, we illustrate how the developed system allows us to properly deal with the evolution of the network configuration, e.g., by detecting cell renaming phenomena and by making it possible to correct inconsistent measurements coming from mobile devices, fostering positioning tasks. Finally, we provide a wide range of basic, spatial, and temporal analyses about the arrangement of the cellular network and its evolution over time, demonstrating how the developed system can be used to reconstruct and maintain a deep knowledge of the cellular network, possibly starting from crowdsourced information only