In this demonstration paper, we present FixCyprus, which is a cost-effective crowdsourcing service for road transportation authorities in Cyprus to gather information and manage defects and incidents on the road network and surrounding infrastructure (e.g., pavements, lighting, drinking water pipes, etc.). The production service, which includes a lightweight and user-friendly mobile application for sharing image-annotated incident reports, is already operating nationwide for six months providing significant budget savings by reducing field inspections and the use of expensive equipment. We will demonstrate FixCyprus using two modes: i) Interactive mode, where attendees will be able to create and submit their own dummy incident reports and see the end-to-end processing flow in a test environment and ii) Trace-driven mode, where attendees will be able to visualize a large number of synthetic reports, see the workload for manually managing them, and explore enhancements that are underway for automating some of the underlying tasks using machine learning.

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform’s user interface the location traces and associated images collected during a real field exercise.

In this work, we present a deep neural network (DNN)-based indoor fingerprinting localization method with WiFi fine time measurements (FTM). The proposed method leverages the WiFi FTM and its variance as environment features to provide accurate location estimation. An $i$ -th layer DNN structure used in this paper is implemented by back propagation using an Adam optimizer. The weights and the bias of the $l-\text{th}$ layer that minimize the loss function is computed in order to minimize the positioning mean squared error (MSE). Experimental results using real-world data obtained in a typical office setting proves the efficiency of the proposed solution. The performance of the system is remarkably improved, using the $600\times 600$ hidden layer size of the DNN, we achieved an average positioning accuracy of 0.7 m and 0.9 m for the 68-th percentiles $(1-\sigma)$ and 95-th percentiles $(2-\sigma)$ respectively.

Mobile applications for triggering Covid-19 exposure notifications without sacrificing the users' privacy are a promising tool for complementing manual contact tracing that is a resource-demanding and labor-intensive task when the infections grow rapidly. This advanced seminar presents the fundamental concepts behind the realization of large-scale Mobile Contact Tracing Apps (MCTA). We provide an overview of this emerging field, while focusing on Bluetooth-based privacy-preserving solutions. We tackle the topic from multiple perspectives: background, state-of-the-art technologies, protocols, real-life implementations, performance indicators, security and privacy aspects, as well as future directions. The seminar presents the big picture, so that the target audience can further expand their knowledge by studying the material and following the references. Our presentation will be delivered through the lens of 2 country-wide MCTA, namely the Corona-Warn-App (CWA) and the CovTracer-Exposure Notification (CovTracer-EN) app deployed in Germany and Cyprus, respectively.

At the onset of Covid-19 several Mobile Contact Tracing Applications (MCTA) were deployed and in many cases contributed to curbing the pandemic by triggering Exposure Notifications (EN) to users who were in proximity to infected users. Recently, a number of MCTA were enhanced with Digital Presence Tracing (DPT) functionality in an effort of the public health authorities to break infection chains mostly in indoor crowded spaces and manage super-spreading events (e.g., concerts, parties). That is, alerting individuals who visited the same place or attended the same event with infected users. This is typically implemented by scanning a QR code at the venue entrance. In this work, we present a DPT solution that relies on EN-Hubs, i.e., Bluetooth-enabled IoT devices, that propagate EN in a machine-to-machine fashion reaching all visitors/attendants seamlessly through their MCTA. The proposed solution removes the overhead of issuing, managing, and scanning QR codes every time people visit a place. In addition, it can be conveniently retrofitted to existing nation-wide MCTA offering DPT capabilities with limited implementation cost.

The Internet of Things (IoT) revolution has massively introduced sensor-rich tracking devices to an ever growing landscape of smart spaces (e.g., factories, hospitals, and ships). One problem that remains unsolved over the years is the localization problem for IoT, given that Satellite-based solutions are inaccurate in indoor spaces where human activity takes place 80-90% of the time. In this paper, we introduce a novel opensource architecture for IoT localization, coined Anyplace 4.0 IoT (A4IoT), which exploits signal fingerprinting to organize under the same roof a wide range of different localization technologies (e.g., Wi-Fi, BLE, Cellular, UWB, Computer Vision). We present the technical requirements of A4IoT inspired by the Alstom SA smart factory, operating worldwide in rail transport markets. A4IoT comprises a crowdsourcing architecture where deployers can collect and organize fingerprint signals inside smart spaces in a designated localization service running on the Edge (from Raspberry to Datacenter). The service incorporates timeseries databases for tracking targets and deployers can provide accurate room-level localization accuracy (≈ 2 m) on a variety of platforms (e.g., Android, Linux, Mac, Windows, Robot OS) but also integrate A4IoT through Web 2.0 endpoints to their software ecosystems.

The demand for indoor localization services has led to the development of techniques that create a Fingerprint Map (FM) of sensor signals (e.g., magnetic, Wi-Fi, bluetooth) at designated positions in an indoor space and then use FM as a reference for subsequent localization tasks. With such an approach, it is crucial to assess the quality of the FM before deployment, in a manner disregarding data origin and at any location of interest, so as to provide deployment staff with the information on the quality of localization. Even though FM-based localization algorithms usually provide accuracy estimates during system operation (e.g., visualized as uncertainty circle or ellipse around the user location), they do not provide any information about the expected accuracy before the actual deployment of the localization service. In this paper, we develop a novel frame-work for quality assessment on arbitrary FMs coined ACCES. Our framework comprises a generic interpolation method using Gaussian Processes (GP), upon which a navigability score at any location is derived using the Cramer-Rao Lower Bound (CRLB). Our approach does not rely on the underlying physical model of the fingerprint data. Our extensive experimental study with magnetic FMs, comparing empirical localization accuracy against derived bounds, demonstrates that the navigability score closely matches the accuracy variations users experience.

In this demonstration we present ACCES, a novel framework that enables quality assessment of arbitrary fingerprint maps and offline accuracy estimation for the task of fingerprint-based indoor localization. Our framework considers collected fingerprints disregarding the physical origin of the data. First, it applies a widely used statistical instrument, namely Gaussian Process Regression (GPR), for interpolation of the fingerprints. Then, to estimate the best possibly achievable localization accuracy at any location, it utilizes the Cramer-Rao Lower Bound (CRLB) with interpolated data as an input. Our demonstration entails a standalone version of the popular and open-source Anyplace Internet-based indoor navigation service in which the software modules of ACCES are integrated. At the conference, we will present the utility of our method in two modes: (i) Collection Mode, where attendees will be able to use our service directly to collect signal measurements over the venue using an Android smartphone, and (ii) Reflection Mode, where attendees will be able to observe the collected measurements and the respective ACCES accuracy estimations in the form of an overlay heatmap.

People do most of their activities, business, commerce, entertainment and socializing indoors. As all of these are increasingly aided by online services and indoor spaces are becoming bigger and more complex, there is a growing need for cost-effective indoor localization, mapping, navigation and information services. In this paper, we present a complete Indoor Information Service, coined Anyplace, which has an open, modular, extensible and scalable architecture, making it ideal for a wide range of applications. Our service features three highly desirable properties, namely crowd sourcing, scalability and accuracy. Anyplace implements a set of crowd sourcing-supportive mechanisms to handle the enormous amount of crowd-sensed data, filter incorrect user contributions and exploit Wi-Fi data from heterogeneous mobile devices. Moreover, it uses a big-data architecture for efficient storage and retrieval of localization and mapping data. Finally, our service relies on the abundance of sensory data on smartphones (e.g., Wi-Fi signal strength and inertial measurements) to deliver reliable indoor geolocation information that received several international awards.

This advanced seminar presents the fundamental mobile data management concepts behind the realization of innovative indoor information services that deal with all aspects of handling indoor data as a valuable resource, including data modeling, data acquisition, query processing, privacy and energy consumption. The goal is to provide an overview of the emerging field of indoor data management with a particular emphasis on mobile systems. We tackle the topic from a wide range of perspectives: fundamentals, definitions, current state, academic & industrial perspective, reality & visionary scenarios as well as future challenges. The seminar captures the big picture, such that interested researchers and practitioners can expand their study by following the references. Our presentation will be carried out through the lens of an experimental Indoor Information System we developed at the University of Cyprus, coined Anyplace, which has obtained three international awards and was ranked the second most accurate indoor localization technology by Microsoft Research at IEEE/ACM IPSN'14.

Smartphones are nowadays equipped with a number of sensors, such as WiFi, GPS, accelerometers, etc. This capability allows smartphone users to easily engage in crowdsourced computing services, which contribute to the solution of complex problems in a distributed manner. In this work, we leverage such a computing paradigm to solve efficiently the following problem: comparing a query trace Q against a crowd of traces generated and stored on distributed smartphones. Our proposed framework, coined SmartTrace+, provides an effective solution without disclosing any part of the crowd traces to the query processor. SmartTrace+, relies on an in-situ data storage model and intelligent top-K query processing algorithms that exploit distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q. We evaluate our algorithms on both synthetic and real workloads. We describe our prototype system developed on the Android OS. The solution is deployed over our own SmartLab testbed of 25 smartphones. Our study reveals that computations over SmartTrace+result in substantial energy conservation; in addition, results can be computed faster than competitive approaches.

In this demonstration paper, we present an indoor positioning system developed for Android smartphones, coined Airplace. To infer the unknown user location we rely on ubiquitous WLANs and exploit Received Signal Strength (RSS) values from neighboring Access Points (AP) that are constantly monitored by the mobile devices under normal operation. Our system follows a mobile-based network-assisted architecture to eliminate the communication overhead and respect user privacy. In a typical scenario, when a user walks inside a building a smartphone client conducts a single communication with our Distribution Server to receive the RSS radiomap and is then able to position itself independently using the observed RSS values. Moreover, we have implemented an Android application to facilitate the collection of RSS values by users that may contribute their data to our system for constructing and updating the radiomap through crowdsourcing1. We will demonstrate the real-time positioning capabilities of the system during the conference by allowing attendees to carry an Android tablet in order to view their position on a floorplan map, while walking around inside the demo area (interactive scenario). Moreover, we will illustrate how to evaluate the performance of different positioning algorithms using profiled data in a trace-driven scenario. Our objective is to highlight the effectiveness and applicability of our system and at the same time the participants will be able to appreciate the potential of indoor location-oriented services and applications.

In this demonstration paper, we present a powerful distributed framework for finding similar trajectories in a smartphone network, without disclosing the traces of participating users. Our framework, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: “Report objects (i.e., trajectories) that follow a similar spatio-temporal motion to Q, where Q is some query trajectory.” SmartTrace, relies on an in-situ data storage model, where geo-location data is recorded locally on smartphones for both performance and privacy reasons. SmartTrace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. Our demonstration shows how the SmartTrace algorithmics are ported on a network of Android-based smartphone devices with impressive query response times. To demonstrate the capabilities of SmartTrace during the conference, we will allow the attendees to query local smartphone networks in the following two modes: (i) Interactive Mode, where devices will be handed out to participants aiming to identify who is moving similar to the querying node; and (ii) Trace-driven Mode, where a large-scale deployment can be launched in order to show how the K most similar trajectories can be identified quickly and efficiently. The conference attendees will be able to appreciate how interesting spatio-temporal search applications can be implemented efficiently (for performance reasons) and without disclosing the complete user traces to the query processor (for privacy reasons)1. For instance, an attendee might be able to determine other attendees that have participated in common sessions, in order to initiate new discussions and collaborations, without knowing their trajectory or revealing his/her own trajectory either.

In this paper we present a powerful distributed framework for finding similar trajectories in a smart phone network, without disclosing the traces of participating users. Our framework, coined Smart Trace, exploits opportunistic and participatory sensing in order to quickly answer queries of the form: "Report the users that move more similar to Q, where Q is some query trace". Smart Trace, relies on an in-situ data storage model, where geo-location data is recorded locally on smart phones for both performance and data-disclosure reasons. Smart Trace then deploys an efficient top-K query processing algorithm that exploits distributed trajectory similarity measures, resilient to spatial and temporal noise, in order to derive the most relevant answers to Q quickly and efficiently. We assess our ideas with realistic and real workloads from Microsoft Research Asia and other sources. Our study reveals that Smart Trace computes the desired results with 74% less energy consumption and 13% faster than its centralized and decentralized counterparts. Our experimental results also confirm our analytical study.