Reproducibility is a core component of any scientific discovery. A step towards reproducibility within the IPIN community is the contribution of this paper, our software-based testbed, called RETSINA (Reproducibility and Experimentation Testbed for Signal-strength Indoor Near Analysis). RETSINA enables the repeatability, reproducibility and comparison of approaches that use machine learning to detect proximity. We demonstrate RETSINA’s functionality by repeating and extending the findings of a recent case study on Wi-Fi signal strength based contact tracing accuracy. Furthermore, we leverage RETSINA to experimentally compare the results for detecting close encounters produced by the original Wi-Fi signal strength readings study and our study using Bluetooth signal strength readings.

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.

Connected and Autonomous Vehicles (CAV) depend on satellite systems, such as the Global Positioning System (GPS), for location awareness. Location data are streamed in real-time to the CAV’s perception engine from its onboard GPS receiver for autonomous driving and navigation. However, these receivers are vulnerable to location spoofing attacks that can be easily launched using Commercial-Off-The-Self (COTS) equipment and open-source software. Existing data-driven attack detection solutions typically require data associated with ‘normal’ and ‘attack’ labels. The latter are hard to collect in operational conditions or even in controlled experiments. To this end, we formulate the GPS location spoofing attack detection as an outlier detection problem. The proposed solution based on Machine Learning (ML) relies solely on normal location data for training during attack-free operation. Our solution demonstrates more than 98% detection accuracy according to standard metrics on realistic data produced with the CARLA driving simulator and outperforms by 15% another (non ML-based) state-of-the-art solution.

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced indoor positioning system (IPS) has to handle diverse devices and the inherent heterogeneity in received signal strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this article, we focus on mean differential fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all access points from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér–Rao lower bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS data sets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings, including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

With the advent of Connected and Autonomous Vehicles (CAVs) comes the very real risk that these vehicles will be exposed to cyber-attacks by exploiting various vulnerabilities. This paper gives a technical overview of the H2020 CARAMEL project (currently in the intermediate stage) in which Artificial Intelligent (AI)-based cybersecurity for CAVs is the main goal. Most of the possible scenarios are considered, by which an adversary can generate attacks on CAVs, such as attacks on camera sensors, GPS location, Vehicle to Everything (V2X) message transmission, the vehicle's On-Board Unit (OBU), etc. The counter-measures to these attacks and vulnerabilities are presented via the current results in the CARAMEL project achieved by implementing the designed security algorithms.

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.

Contact Tracing (CT) is an effective mitigation measure against the Covid-19 pandemic that is typically performed manually. Its technological counterpart, i.e., Digital Contact Tracing (DCT), has emerged as potential, yet controversial due to privacy concerns, complementary tool to identify exposed individuals automatically. Among DCT solutions, privacy-preserving DCT mobile apps that rely on a Bluetooth-based protocol recently developed jointly by Google and Apple are becoming increasingly popular and have already been deployed in 67 territories around the world. Recent studies indicate that such apps are promising in controlling the spread of infections, yet their adoption is still low. This work presents CovTracer-EN, the national privacy-preserving DCT app in Cyprus that triggers Exposure Notifications (EN) to users that had contact with another infected user who shared this information anonymously with his/her consent. Performance evaluation results and lessons learned aim to offer useful insights and practical guidelines to public health authorities that consider deploying DCT apps for reducing the impact of Covid-19 and future pandemics.

Attacks on the GPS receiver of Connected and Autonomous Vehicles (CAV) and specifically GPS location spoofing is of great concern for the automotive industry as the attacker can compromise the security of CAVs leading to serious repercussions for the drivers and pedestrians. Attack detection solutions based on specialized hardware (e.g., antenna arrays) and satellite signal processing techniques are accurate, yet bulky and expensive to mount on CAVs. Thus, lightweight and cost-effective solutions for detecting location spoofing attacks are highly desirable. This work presents an in-vehicle attack detection solution that fuses multi-source data readily available from the CAV's onboard sensors. It can be implemented in software running on cheap embedded computing platforms integrated into the CAV. The proposed solution is validated using the real-time CARLA simulator, while extensive experimental results demonstrate its effectiveness under different attack scenarios.

In this article, we propose a novel deep reinforcement learning (DRL) approach for controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking multiple first responders (FRs) in challenging 3-D environments in the presence of obstacles and occlusions. We assume that the UAVs receive noisy distance measurements from the FRs which are of two types, i.e., Line of Sight (LoS) and non-LoS (NLoS) measurements and which are used by the UAV agents in order to estimate the state (i.e., position) of the FRs. Subsequently, the proposed DRL-based controller selects the optimal joint control actions according to the Cramér–Rao lower bound (CRLB) of the joint measurement likelihood function to achieve high tracking performance. Specifically, the optimal UAV control actions are quantified by the proposed reward function, which considers both the CRLB of the entire system and each UAV's individual contribution to the system, called global reward and difference reward, respectively. Since the UAVs take actions that reduce the CRLB of the entire system, tracking accuracy is improved by ensuring the reception of high quality LoS measurements with high probability. Our simulation results show that the proposed DRL-based UAV controller provides a highly accurate target tracking solution with a very low runtime 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.

Wi-Fi technology has been thoroughly studied for indoor localization. This is mainly due to the existing infrastructure inside buildings for wireless connectivity and the uptake of mobile devices where Wi-Fi location-dependent measurements, e.g., timing and signal strength readings, are readily available to determine the user location. To enhance the accuracy of Wi-Fi solutions, a two-way ranging approach was recently introduced into the IEEE 802.11 standard for the provision of Fine Timing Measurements (FTM). Such measurements enable a more reliable estimation of the distance between FTM-capable Wi-Fi access points and user-carried devices; thus, promising to deliver meter-level location accuracy. In this work, we propose two novel solutions that leverage FTM and follow different approaches, which have not been investigated in the literature. The first solution is based on an Unscented Kalman Filter (UKF) algorithm to process FTM ranging measurements, while the second solution relies on an FTM fingerprinting method. Experimental results using real-life data collected in a typical office environment demonstrate the effectiveness of both solutions, while the FTM fingerprinting approach demonstrated 1.12m and 2.13m localization errors for the 67-th and 95-th percentiles, respectively. This is a two to three times improvement over the traditional Wi-Fi signal strength fingerprinting approach and the UKF ranging algorithm.

Recently, we experience the increasing prevalence of wearable cameras, some of which feature Wireless Local Area Network (WLAN) connectivity, and the abundance of mobile devices equipped with on-board camera and WLAN modules. Motivated by this fact, this work presents an indoor localization system that leverages both imagery and WLAN data for enabling and supporting a wide variety of envisaged location-aware applications ranging from ambient and assisted living to indoor mobile gaming and retail analytics. The proposed solution integrates two complementary localization approaches, i.e., one based on WLAN and another one based on image location-dependent data, using a fusion engine. Two fusion strategies are developed and investigated to meet different requirements in terms of accuracy, run time, and power consumption. The one is a light-weight threshold-based approach that combines the location outputs of two localization algorithms, namely a WLAN-based algorithm that processes signal strength readings from the surrounding wireless infrastructure using an extended Naive Bayes approach and an image-based algorithm that follows a novel approach based on hierarchical vocabulary tree of SURF (Speeded Up Robust Features) descriptors. The second fusion strategy employs a particle filter algorithm that operates directly on the WLAN and image readings and also includes prior position estimation information in the localization process. Extensive experimental results using real-life data from an indoor office environment indicate that the proposed fusion strategies perform well and are competitive against standalone WLAN and imaged-based algorithms, as well as alternative fusion localization solutions.

Indoor location systems that rely on WiFi signal strength values from the existing building infrastructure deliver adequate accuracy with no installation cost at the expense of time and effort to populate the signal database. To this end, crowdsourcing has been widely explored to leverage on large volumes of user-collected data; however, mobile devices that report signal strength differently, known as device diversity, limit its applicability in practice. We consider crowdsourced location systems and present a qualitative (in terms of analytical results) and a quantitative (with respect to real-life experimental data) evaluation of several approaches that are robust to diverse devices while focusing on differential signal strength methods.

With the adoption of indoor positioning solutions, which enable for a variety of location-based spatial services, a number of indoor map standards and formats have been proposed in the last decade. As each of these indoor map standard has its own purpose, the strengths and weaknesses are necessary to be understood and analyzed before selecting one of them for a given application. The Indoor Map Subcommittee has been established under IPIN/ISC in 2017. Among others, the goal of this working group is to compare available indoor map standards, provide a guideline for their application and advise on changes to their standardization development organizations if necessary. In this paper we present a survey of indoor map standards as an achievement of the subcommittee. The scope of the survey covers official standards such as IFC of BuildingSmart, IndoorGML and CityGML of OGC, and Indoor OpenStreetMap. We present several use-cases to show and discuss how to build indoor maps.

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.

Camera Sensor Networks (CSN) are becoming increasingly popular in a variety of security and safety-critical applications including public space surveillance, monitoring of attack-sensitive facilities, and critical infrastructure protection. Cameras in such networks are equipped with high-resolution visual sensors and on-board processors, while featuring wireless communication capabilities. These features enable the execution of various tasks, such as area coverage, activity recognition and target tracking, in a cooperative fashion. However, the performance of CSN may be compromised when faults occur, either due to unintentional software and hardware faults or as the result of a malicious attack. Paving the way for fault tolerance in CSN-based target tracking, we introduce a flexible fault model that can be used to generate different types of erroneous behaviour, thus simulating realistic faults in CSN. We also propose a fault-tolerant decentralized solution for tracking a target that passes through the area monitored by the CSN. Our simulation results indicate that the proposed solution is able to track the target reliably despite the presence of faults.

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.

The provision of reliable location estimates in WiFi fingerprinting localization is challenging, mainly because users typically carry heterogeneous devices that report Received Signal Strength (RSS) measurements from surrounding Access Points (AP) very differently. This may render the user-carried device incompatible with the fingerprinting system, in case the RSS radiomap was collected with a different device, thus incurring high localization errors. To this end, we introduce a novel differential fingerprinting method that computes the difference between the RSS value of each AP and the mean RSS value across all APs in the original fingerprint. We show that the new fingerprints are robust to device heterogeneity, as opposed to traditional RSS fingerprints. In addition, we derive analytical results and demonstrate with simulations and experimental data that the proposed approach performs considerably better than existing differential fingerprinting solutions, in terms of localization accuracy and computational overhead.

Distance-based network localization is a very effective way to obtain the position of nodes in a network via range measuring; this, however, requires nodes with nontrivial sensing capabilities. Range-free techniques, conversely, use much simpler nodes but often provide just a rough estimate on the node's position. In this paper we develop a hybrid localization algorithm that combines range-free and distance-based sensors. A detailed simulation campaign aimed at corroborating the findings and at showing the potentialities of the approach concludes the paper.

This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.

The provision of accurate and reliable localization and tracking information for a target moving inside a binary Wireless Sensor Network (WSN) is quite challenging, especially when sensor failures due to hardware and/or software malfunctions or adversary attacks are considered. Most tracking algorithms assume fault-free scenarios and exploit all binary sensor observations, thus their accuracy may degrade when faults are present in the field. Spatiotemporal information available while the target is traversing the sensor field can be used not only for tracking the target, but also for detecting certain types of faults that appear highly correlated both in time and space. Our main contribution is ftTRACK, a target tracking architecture that is resilient to sensor faults and consists of three main components, namely the sensor health-state estimator, a fault-tolerant localization algorithm, and a location smoothing component. The key idea in the ftTRACK architecture lies in the sensor health-state estimator that leverages spatiotemporal information from previous estimation steps to intelligently choose which sensors to employ in the localization and tracking tasks. Simulation results indicate that ftTRACK maintains a high level of tracking accuracy, even when a large number of sensors fail.

Tracking of a source (target) which is moving inside a binary Wireless Sensor Network (WSN) is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors to locate and track a target may lead to high estimation errors, therefore, there is a need to develop fault tolerant localization algorithms which perform well even when a percentage of the sensors report erroneous observations. Alternatively, one may fuse information from neighboring sensors in order to determine the health state of each sensor, and subsequently use only healthy sensors in the localization and tracking process. Our contribution is the development of an architecture which combines the sensor health state estimation together with fault tolerant localization algorithms that leads to more robust target tracking in binary WSNs. Simulation results indicate that the proposed approach is resilient to various types of faults.

We consider the problem of target (event source) tracking using a binary Wireless Sensor Network (WSN). For this problem, a WSN consisting of sensors that can detect the presence of a target in an area around them, should fuse the information received by the individual sensors in order to localize and track the target. This is a challenging problem particularly when sensors may fail either due to hardware and/or software malfunctions, energy depletion or adversary attacks. Using information from failed sensors during target tracking may lead to high estimation errors. Since failure of individual sensors is unavoidable, there is a need to estimate the health state of each sensor in order to ignore those sensors that are considered as faulty. The contribution of this work is the investigation of three different algorithms for estimating the sensors' health state simultaneously with target tracking.

Crowdsourcing is an emerging field that allows to tackle difficult problems by soliciting contributions from common people, rather than trained professionals. In the post-pc era, where smartphones dominate the personal computing market offering both constant mobility and large amounts of spatiotemporal sensory data, crowdsourcing is becoming increasingly popular. In this context, crowdsourcing stands as the only viable solution for collecting the large amount of location-related network data required to support location-based services, e.g., the signal strength radiomap of a fingerprinting localization system inside a multi-floor building. However, this benefit does not come for free, because crowdsourcing also poses new challenges in radiomap creation. We focus on the problem of device diversity that occurs frequently as the contributors usually carry heterogeneous mobile devices that report network measurements very differently. We demonstrate with simulations and experimental results that the traditional signal strength values from the surrounding network infrastructure are not suitable for crowdsourcing the radiomap. Moreover, we present an alternative approach, based on signal strength differences, that is far more robust to device variations and maintains the localization accuracy regardless of the number of contributing devices.

In this demo, we present an efficient hybrid indoor positioning solution that uses multi-sensory location-oriented observations, including WiFi, accelerometer, gyroscope and digital compass data, that are widely available on Android smartphones.

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.

Traditionally, positioning with WLAN Received Signal Strength (RSS) fingerprints involves the laborious task of collecting a radiomap with a reference mobile device. Best accuracy can be guaranteed only in case the user carries the same device, while positioning with different devices requires a calibration step to make the new devices' RSS values compatible with the existing radiomap. We propose a novel device self-calibration method that uses histograms of RSS values. First, we use the existing radiomap to create the RSS histogram of the reference device. Subsequently, when the user enters a building and starts positioning, the observed RSS values are recorded simultaneously in the background to create and update the histogram of the user device. We use these RSS histograms to fit a linear mapping between the user and reference device. Our calibration method is running concurrently with positioning and does not require any user intervention. Experimental results with five smartphones in a real indoor environment indicate that soon after positioning is initiated the device is self-calibrated, thus improving the accuracy to be comparable with the case of using a radiomap created with the same device.

This work proposes the use of 3D Ray Tracing (RT) to construct radiomaps for WLAN Received Signal Strength (RSS) fingerprint-based positioning, in conjunction with calibration techniques to tackle with the problem using different devices. In addition to the fact that RSS data collection might be a tedious and time-consuming process, the measured radiomap accuracy and applicability is subject to potential changes in the wireless environment. Therefore, RT becomes a very suitable and efficient solution to tackle this problem. Moreover, in traditional fingerprint-based methods, the underlying radiomap is restricted to the mobile device for which the radiomap has been created. To overcome this limitation, we propose the use of linear data transformation to match the characteristics of various devices. We address both challenges by using 3D RT-generated radiomaps and highlight the efficiency of this approach in terms of the time spent to create the radiomap, the amount of data required to calibrate the radiomap for different devices and the positioning error which is compared against the case of using dedicated radiomaps collected with each device.

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 demo paper, we present the Airplace indoor positioning platform developed for Android smartphones [1]. Airplace relies on existing WLAN infrastructure and exploits Received Signal Strength (RSS) values from neighboring Access Points (AP) to infer the unknown user location. Our system utilizes a number of RSS fingerprints collected a priori to build the so-called radiomap. Location is then estimated by finding the best match between the currently measured fingerprint and fingerprints in the radiomap [2].

This paper investigates the use of a sensor network for localizing and tracking a moving target using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate a number of misbehaving nodes. SNAP (Subtract on Negative Add on Positive), is a simple event localization algorithm designed for WSNs applications that exhibits this fault tolerant behavior. The main contribution of this paper is to combine the decentralized implementation of SNAP with Kalman Filter techniques for tracking the movement of a target. This efficient tracking procedure provides fairly accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations.

The increasing demand for indoor location-based services has motivated the development of positioning methods that exploit the existing wireless network infrastructure. Accuracy is an important requirement, however fault tolerance is also highly desirable in case of failures or malicious attacks. We investigate the fault tolerance of fingerprint-based methods under a variety of fault or attack scenarios. We study the Subtract on Negative Add on Positive (SNAP) algorithm and modify it appropriately for the WLAN setup. Our results indicate that SNAP achieves adequate accuracy with very low computational complexity and exhibits smoother performance degradation in the presence of faults compared to other methods.

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.

The provision of reliable location estimates in case of unpredicted failures or malicious attacks, which inject faults and compromise the performance of the positioning system, is very important. Thus, our main interest is on the fault tolerance of WLAN fingerprint-based methods, rather than the absolute positioning error in the fault-free case. We study the Nearest Neighbor method and as a first step we develop a robust detection scheme to accurately detect faults. We incorporate this into a hybrid positioning method that switches to a modified distance metric, instead of the Euclidean, if faults are present. Experimental results indicate that the proposed approach exhibits higher resilience to faults compared to other positioning methods.

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.

Accurate and reliable location estimates using wireless networks are important for enabling indoor location oriented services and applications, such as in-building guidance and asset tracking. Providing adequate level of accuracy in case of faults or attacks to the positioning system is equally significant, thus our main interest is on the fault tolerance of positioning methods, rather than the absolute accuracy in the fault-free case. We introduce several fault models to capture the effect of failures in the wireless infrastructure or malicious attacks and discuss how these models can simulate the corruption of signal strength values during positioning. The models are used to investigate the fault tolerance of positioning methods and evaluate them in terms of their performance degradation as the percentage of corrupted signal strength measurements increases. Experimental results using our fault models are also presented.

Fingerprinting localization techniques provide reliable location estimates and enable the development of location aware applications especially for indoor environments, where satellite based positioning is infeasible. In our approach we utilize received signal strength (RSS) fingerprints collected in known locations and employ a radial basis function (RBF) neural network to approximate the function that maps fingerprints to location coordinates. We present a clustering scheme to reduce the size and computational complexity of the RBF architecture and demonstrate the applicability of this approach in a real-world WLAN setup. Experimental results indicate that the RBF based method is an efficient approach to the location determination problem that outperforms existing techniques in terms of the positioning error.

Statistical terminal assisted mobile positioning is a methodology that enhances the performance of existing localization techniques, by exploiting historical measurements collected at the terminal side. We propose a unified positioning framework in which different types of network related measurements may be employed by multiple techniques to derive coarse position estimates, while filtering is applied as a post processing step to further increase accuracy. The proposed architecture is applicable to user plane location architectures, while its open and modular design allows easy integration of new positioning algorithms and post processing techniques. The implementation of this concept in ubiquitous terminal assisted positioning prototype concentrates particularly on quality of position issues and compatibility with currently available standards and communication protocols.