The growing need for accurate localization in increasingly interconnected industrial environments has driven research towards developing new indoor localization systems. This work analyzes and compares three asynchronous localization methods based on the Two-Way-Ranging (TWR) protocol: Single-Sided TWR (SS-TWR), Symmetric Double-Sided TWR (SDS-TWR), and Alternative Double-Sided TWR (AltDS-TWR) in addition to the Asynchronous Time Difference of Arrival (A-TDOA) system. Similar comparisons have been previously reported, however, these only take into consideration clock-associated errors and unrealistic test conditions, thus overlooking the impact of signal paths required by each method, therefore reaching conclusions far from those expected in real applications. In this paper, we propose a more complete and fair comparison among these four asynchronous systems. For this purpose, we propose a clock, noise, and multipath error characterization for each localization system to perform a realistic comparison over multiple industrial scenarios where Autonomous Mobile Robots freely navigate. In order to ensure a fair comparison, a sensor distribution optimization has been carried out for attaining the best achievable performance of each analyzed system. Results show that the selection of the best localization system may depend on the scenario and application conditions as well as the deployment budget. Nevertheless, results from the AltDS-TWR method highlight the potential of this system, yet further research should be conducted to verify the influence of moving targets for this TWR method. • The error-bounds characterization of the SS-TWR, SDS-TWR, AltDS-TWR methods and the A-TDOA localization systems under noise, multipath and clock-related errors in indoor environments. • The proposal of a realistic comparison of the attainable performance among the 4 characterized localization systems over an industrial scenario of deployment under different conditions. • The optimization of the compared sensor distributions in accordance with the respective path and clock related error-bounds for guaranteeing a fair comparison among the 4 localization systems.

Autonomous Mobile Robots (AMRs) are essential for automating internal transportation in Industry 4.0, relying on visual-based positioning systems to generate maps and compare their location with mapped reference obstacles. However, enhancing positioning accuracy is crucial for demanding industrial tasks. Combining vision-based systems with Ultra-Wideband (UWB) time-based positioning systems has gained research interest. In this sense, UWB based localization systems are commonly categorized into synchronous and asynchronous configurations. Synchronous systems, such as the Time of Arrival (TOA) and the Time Difference of Arrival (TDOA) systems, require clock synchronization but reduce signal path lengths. In contrast, asynchronous systems, such as Asynchronous Time Difference of Arrival (A-TDOA) and Single-Sided Two-Way Ranging (SS-TWR), eliminate synchronization needs but may involve longer signal paths. To address these differences, in this paper, we propose a methodology to fairly compare these configurations. The approach includes characterizing the error sources in each system and analyzing the optimal spatial arrangement of sensors under incremental time reply conditions. Results reveal the superiority of asynchronous configurations in low sensor density deployments, achieving up to a 54.8% reduction in localization error compared to synchronous systems. In addition, as the number of deployed nodes decreases, synchronous systems show up to a 28.9% increase in localization error to the analysis performed, whereas the asynchronous systems achieved a 24.8% increase for the A-TDOA, and only a 9.2% increase in the case of the SS-TWR method. These findings prompt further investigation into the suitability of synchronous positioning for minimizing errors in large-scale industrial applications. • Error bounds of TOA, TDOA, A-TDOA, and SS-TWR with noise, multipath, and clock errors • Comparison of synchronous and asynchronous methods in a sensor deployment via BWO • Consideration of discontinuous node regions into BWO to improve representability

Indoor tracking of products in a warehouse is a challenge that requires a scalable and reliable solution. Among those challenges are energy efficiency, positioning accuracy, scalability and coverage. However, when using radio-based indoor localization and tracking systems, the major challenge is dealing with the characteristics of the operating area and of the target objects, where the presence of metal objects deeply affects the propagation of radio signals. Fingerprinting based on Bluetooth Low Energy (BLE) Received Signal Strength Indicator (RSSI) is a cheap and straightforward technique. However, it suffers in terms of accuracy compared to other solutions using different technologies and/or measurements to implement an Indoor Positioning System (IPS), offering a trade-off between complexity, cost, and accuracy. Product tracking inside a warehouse may not require sub-meter accuracy, but it requires a scalable and reliable solution. To this end, BLE RSSI Fingerprinting was assessed to assert its viability in these scenarios. This paper describes the implementation of an IPS using BLE RSSI Fingerprinting to track pallets inside a warehouse, which was tested in a real-world setting, achieving an average error of 2.24 meters.

Over the last years, numerous studies have emerged using Wi-Fi channel state information, enabling device-free (passive) sensing for applications such as motion detection, indoor positioning or human activity recognition. More recently, the development framework for the low-cost ESP32 microcontrollers has added support for obtaining channel state information data. In this work, we collected channel state information data for human activity recognition, where activities are relatively localized with respect to the Wi-Fi infrastructure. The data was collected in several runs, duly spaced in time, and a convolutional neural network model was used for the classification of activities. Classification performance evaluation showed a clear degradation when a model evaluated with data collected 10 minutes after the data used for training showed a 52% relative loss in the accuracy of the classification.

Synthetic data of high quality can provide research teams with an effective means of conducting large-scale evaluations of their indoor positioning systems under controlled conditions, while avoiding the significant effort and costs associated with real-world experiments and data collection/labelling. Moreover, it facilitates the fair comparison with other solutions, since data can be generated for more diverse conditions and can be shared without concerns. The work described in this paper aims to improve the quality of WiFi synthetic data by integrating new models for channel noise and beacon receive probability into the Dioptra tool. We compare the results of 13 fingerprinting methods used on 15 synthetic databases and 14 real-world databases. The results indicate that synthetic data can be an effective alternative/complement for the evaluation and comparison of WiFi-based positioning methods.

Wi-Fi-based positioning systems, particularly the ones based on Wi-Fi fingerprinting, rely on a Radio Map (RM) which represents the radio environment at the time when it was collected. Over time, phenomena such as the propagation effects or adding/removing Access Points (APs) from an indoor environment may lead to significant variations in the radio environment, thus leading to errors in estimated positions. Although it is common knowledge that RMs degrade over time, it is difficult to predict and detect when degradation causes large errors. In this paper, we propose a method that continuously monitors the radio environment and uses Radial Basis Functions (RBF) interpolation to automatically enrich an old RM with new information. Before enriching the RM, AP selection is performed to remove APs that disappeared and mobile APs from the old radio map. Then, the analysis of the radio environment is performed to select newly detected APs to enrich the radio map, based on predefined criteria. Our experiments with real-world data show a significant improvement over 100% in mean error when using the enriched RM. This approach presents a promising solution to overcome the RM degradation in Wi-Fi fingerprinting, with potential applications in indoor positioning and location-based services.

Microsoft proposed RADAR in 2000, the first indoor positioning system based on Wi-Fi fingerprinting. Since then, the indoor research community has worked not only to improve the base estimator but also on finding an optimal RSS data representation. The long-term objective is to find a positioning system that minimises the mean positioning error. Despite the relevant advances in the last 23 years, a disruptive solution has not been reached yet. The evaluation with non-open datasets and comparisons with non-optimized baselines make the analysis of the current status of fingerprinting for indoor positioning difficult. In addition, the lack of implementation details or data used for evaluation in several works make results reproducibility impossible. This paper focuses on providing a comprehensive analysis of fingerprinting with k-NN and settling the basement for replicability and reproducibility in further works, targeting to bring relevant information about k-NN when it is used as a baseline comparison of advanced fingerprint-based methods.

Autonomous vehicles require sophisticated sensors to safely and timely detect the conditions of the world around them and act accordingly. However, just like human senses, sensors have limitations and blind spots. Vulnerable Road Users, such as pedestrians and cyclists, are at particular risk since they can quickly come to the road from a blind spot too late for the vehicle to react. This article evaluates the use of a commercial 5G-enabled smartphone and a purpose-designed app to advertise a Vulnerable Road User position to a connected autonomous vehicle using European Telecommunications Standards Institute standard messages. The results obtained in a real testbed proved that a 5G network is capable of supporting the low latency required for this use case, even though the usefulness of the positioning data transmitted was limited by the accuracy of the GPS embedded in commercial smartphones.

The core of fingerprinting is based on the uniqueness of the RF signature in a given location over time. In the offline phase, the fingerprints -the set of RSSI values from different anchors-are collected at given locations generating a radio map. In the online phase, a matching algorithm retrieves the most similar fingerprints from the radio map and computes the position estimate for every operational fingerprint. However, computing the similarities to all the samples in the radio map may be inefficient and not scale in those cases where the radio map is large. Previous attempts to alleviate the computational load rely on the segmentation of the radio map through smart clustering in the offline stage, and a two-step estimation process in the online stage. However, most of the clustering models applied are generic without any consideration about signal propagation and relevant fingerprints are often filtered, resulting in a higher positioning error. This paper introduces Strongest AP Set (SAS), a clustering model conceived for RSSI-based fingerprinting. The results show that SAS is not only able to reduce the computational cost, but also to provide better accuracy than the full model without clustering.

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only$k$-means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle’s initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles’ weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.

Indoor Positioning Systems (IPSs) based on different approaches and technologies have been proposed to support localization and navigation applications in indoor environments. The fair benchmarking and comparison of these IPSs is a difficult task since each IPS is usually evaluated in very specific and controlled conditions and using private data sets, not allowing reproducibility and direct comparison between the reported results and other competing solutions. In addition, testing and evaluating an IPS in the real world is difficult and time-consuming, especially when considering evaluation in multiple environments and conditions. To enhance IPS assessment, we propose Dioptra, an open access and user-friendly application to support research, development and evaluation of IPSs through simulation. To the best of our knowledge, Dioptra is the first application specially developed to generate synthetic datasets to promote reproducibility and fair benchmarking between IPSs.

One of the most common assumptions regarding indoor positioning systems based on Wi-Fi fingerprinting is that the Radio Map (RM) becomes outdated and has to be updated to maintain the positioning performance. It is known that propagation effects, the addition/removal of Access Points (APs), changes in the indoor layout, among others, cause RMs to become outdated. However, there is a lack of studies that show how the RM degrades over time. In this paper, we describe an empirical study, based on real-world experiments, to evaluate how and why RMs degrade over time. We conducted site surveys and deployed monitoring devices to analyse the radio environment of one building over 2+ years, which allowed us to identify significant changes/events that caused the degradation of RMs. To quantify the RM degradation, we use the positioning error and propose the RM degradation ratio, a metric to directly compare two RMs and measure how different they are. Obtained results show that the positioning performance is much better when RMs are collected on the same day as the test data, and although RM degradation tends to increase over time, it only leads to large positioning errors when significant changes occur in the Wi-Fi infrastructure, making previous RMs outdated.

The evaluation of Indoor Positioning Systems (IPSs) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experiments, collecting data, and considering multiple scenarios usually limits the evaluation area and, therefore, the assessment of the proposed systems. The requirements and features of controlled experiments cannot be generalized since the use of the same sensors or anchors density cannot be guaranteed. The dawn of datasets is pushing IPS evaluation to a similar level as machine-learning models, where new proposals are evaluated over many heterogeneous datasets. This paper proposes a way to evaluate IPSs in multiple scenarios, that is validated with three use cases. The results prove that the proposed aggregation of the evaluation metric values is a useful tool for high-level comparison of IPSs.

Fingerprint-based indoor positioning is widely used in many contexts, including pedestrian and autonomous vehicles navigation. Many approaches have used traditional Machine Learning models to deal with fingerprinting, being k-NN the most common used one. However, the reference data (or radio map) is generally limited, as data collection is a very demanding task, which degrades overall accuracy. In this work, we propose a novel approach to add random noise to the radio map which will be used in combination with an ensemble model. Instead of augmenting the radio map, we create n noisy versions of the same size, i.e. our proposed Indoor Positioning model will combine n estimations obtained by independent estimators built with the n noisy radio maps. The empirical results have shown that our proposed approach improves the baseline method results in around 10% on average.

FastGraph is a novel positioning approach recently proposed to address the challenges of positioning in large spaces. Wi-Fi-based indoor positioning solutions often require complex and time-consuming deployments. Fingerprinting, as one of most used approaches, relies on a radio map, usually created by manual site survey, a process unpractical even for small spaces. Moreover, the site survey has to be repeated frequently due to the changes in the radio environment. Wi-Fi-based solutions are also frequently discarded for applications such as indoor vehicle navigation due to limited accuracy. This article introduces FastGraph Enhanced, a new version of FastGraph, able to provide high accuracy positioning, opening new fields of application, such as navigation for autonomous vehicles. In FastGraph Enhanced, the 3D Force-Directed Graph-Based method, used to model the radio environment, is extended with new algorithms, allowing to improve, among other aspects, the positioning performance. The core advantages of FastGraph are maintained, not requiring previous calibration or knowledge about the space. The proposed solution was evaluated in real world, with very significative improvements in positioning accuracy when compared with the basic version of FastGraph (from around 5m to 0.5m), and with state-of-the-art solutions.

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 interference in Wi-Fi networks severely degrades the quality of service. In contrast to cellular networks that operate in regulated radio frequencies, and where the use of the radio spectrum is controlled and planned, the deployment of Wireless LAN networks infrastructures is unregulated and often uncontrolled, leading to high levels of interference. The interference analysis process normally requires a manual site survey, similar to the site survey required by indoor positioning Fingerprinting, which in large buildings is unpractical.In this paper we argue that the recently introduced FastGraph solution [1], [2], in addition to provide unsupervised positioning, can also be used to automatically map the level of interference in Wi-Fi environments. The proposed solution can automatically provide interference maps based on two distinct strategies: AP-Based Interference Mapping or Sample-Based Interference Mapping, each one with its own characteristics and advantages.The experimental results presented in this paper suggest that FastGraph can be used to easily create interference maps. In addition, a good correlation between the level of interference and the positioning error was observed - to the best of our knowledge, a relationship never reported before. This finding is very interesting, and can be explored for example to improve the positioning performance, as well as to provide a confidence indicator for the estimated positions, which it is currently not possible to obtain in Wi-Fi Fingerprinting-based systems.

Wi-Fi fingerprinting is a popular technique for smartphone-based indoor positioning. However, well-known RF propagation issues create signal fluctuations that translate into large positioning errors. Large errors limit the usage of Wi-Fi fingerprinting in industrial environments, where the reliability of position estimates is a key requirement. One successful approach to deal with signal fluctuations is to average the signals collected simultaneously through independent Wi-Fi interfaces. Another successful approach is to average the estimates provided by models built on independent radio maps. This paper explores multiple combinations of both approaches and determines the procedure to select the best model based on them through a simulated environment. The evaluation of the proposed model in a real-world industrial scenario shows that the positioning error (according to different metrics including the 95thand 99thpercentiles) is highly improved with respect to the traditional fingerprint.

It is well-known that fingerprinting-based positioning requires an exhaustive calibration phase to create a radio map, which often requires recalibration. Model-based and geometric approaches try to mitigate this effort at the expense of a lower accuracy or high computational cost. This paper introduces FastGraph, where a 3D graph is used to rapidly model the radio propagation environment. By means of unsupervised techniques, FastGraph is able to operate shortly after its deployment without previous knowledge about the environment. The proposed solution uses a novel algorithm to automatically provide location while simultaneously updating the radio map; and learn the position of the Access Points (APs) and location-specific radio propagation parameters. FastGraph has been evaluated in two real-world environments, a factory-plant and a regular university building, with results comparable to those obtained by conventional radio map-based solutions.

The accuracy of fingerprinting-based positioning methods accuracy is limited by the fluctuations in the radio signal intensity mainly due to reflections, refractions, and multipath interference, among other factors. We consider that the fluctuations (often modelled as a Gaussian process for simplification purposes) can be minimized by exploiting the richness of multiple signals collected simultaneously through independent network interfaces. This paper introduces an analysis of Wi-Fi signals' statistics using simultaneous measurements which shows that RSSI values obtained from independent devices are not highly correlated. The low correlation between Wi-Fi interfaces might be exploited to improve the positioning accuracy. The validation of the proposed fingerprinting approach in a real scenario shows that the mean and maximum error in positioning can be reduced by more than 40% when five Wi-Fi interfaces are simultaneously used for fingerprinting.

Tracking the position of people or vehicles in large indoor settings with high accuracy is still a challenge despite the significant progress observed in indoor positioning technology in the last decade. To date, there is not a clearly dominant indoor positioning solution for general use, and challenges related to seamless indoor-outdoor positioning, reliable floor estimation and indoor maps are still needing more research. In this context, the IPIN 2016 conference is promoting a competition to evaluate a set of competing indoor positioning solutions in a realistic scenario. This paper describes the proposal of the UMINHO team and some of the obtained results.

In the past decade, the research community has been dedicating considerable effort into indoor positioning systems based on Wi-Fi fingerprinting techniques, mainly due to their capability to exploit existing infrastructures. Crowdsourcing approaches, also known as organic, have been proposed recently to address the problem of creating and maintaining the corresponding radio maps. In these organic systems, the users of the system build the radio map themselves while using it to estimate their own position/location. However, most of these collaborative methods, proposed by several authors, assume that all the users are honest and committed to contribute to a good quality radio map. In this paper we assess the quality of a radio map built collaboratively and propose a method to classify the credibility of individual contributions and the reputation of individual users. Experimental results are presented for an organic indoor location system that has been used by more than one hundred users over a period of around 12 months.

Research and development around indoor positioning and navigation is capturing the attention of an increasing number of research groups and labs around the world. Among the several techniques being proposed for indoor positioning, solutions based on Wi-Fi fingerprinting are the most popular since they exploit existing WLAN infrastructures to support software-only positioning, tracking and navigation applications. Despite the enormous research efforts in this domain, and despite the existence of some commercial products based on Wi-Fi fingerprinting, it is still difficult to compare the performance, in the real world, of the several existing solutions. The EvAAL competition, hosted by the IPIN 2015 conference, contributed to fill this gap. This paper describes the experience of the RTLS@UM team in participating in track 3 of that competition.

Maintaining consistent radio maps for WiFi fingerprinting-based indoor positioning systems is an essential step to improve the performance of the positioning engines. The radio maps consist of WiFi fingerprints collected at a predefined set of positions/places within a positioning area. Each fingerprint consists of the identification and radio signal level of the surrounding Access Points (APs). Due to the wide proliferation of WiFi networks, it is very common to observe 10 to 20 APs at a single position and more than 50 APs across a single building. However, in practical, not all of the detected APs are useful for the position estimation process. Some of them might have weak signals at certain positions or might have less significance for a position's fingerprint. Thus, those useless APs will add additional computational overheads during the position estimation, and consequently they will reduce the overall performance of the positioning engines. A similar phenomenon also occurs with some of the collected fingerprints. While it is widely accepted that the larger and more detailed the radio map is, the better is the accuracy of the positioning system, we found that some of the fingerprint samples on the radio maps do not contribute significantly to the estimation process. In this paper, we propose two methods for filtering the positioning radio maps: APs filtering and Fingerprints filtering. Then we report on the results of a set of experiments that have been done to evaluate the performance of a WiFipositioning radio map before and after applying the filtering approaches. The results show that there is possibility to simplify the radio maps of the positioning engines without significant degradation on the positioning precision and accuracy, and therefore to reduce the processing time for estimating the position of a tracked WiFi tag. This result has an important impact on increasing the number of tags a single instance of a WiFi positioning engine can handle at a time.

Human mobility has been widely studied for a variety of purposes, from urban planning to the study of spread of diseases. These studies depend heavily on large datasets, and recent advances in collaborative sensing and WiFi infrastructures have created new opportunities for generating that data. However, these methods and procedures require the participation of a significant community of users through extended periods of time. In this paper, we address the problem of how to engage people to participate in the data collection process. We have conducted a user study on the utilisation of a mobile collaborative sensing application. We have found that users react positively to campaigns, but it is difficult to keep them participating for long periods of time. We also hypothesise that one must close the loop, rewarding the participants with services based on the collected data, eventually showing that there is added value obtainable from crowd sourcing.

Location and tracking services and technologies are becoming fundamental components for supporting healthcare solutions. They facilitate patients' tracking and monitoring processes and also allow for better and long-term daily activity recognition. Various location and tracking services have been developed, over the last years, to provide real time localization for different applications. However, most of these services are not designed particularly to comply with all the requirements of Ambient Assisted Living (AAL) and, as a result, they reduce the viability of adopting AAL services as an alternative for continuous healthcare services. In this paper we set out the general requirements for location and tracking services for AAL. The requirements are extracted from a typical scenario of AAL. From the scenario, we define the requirements and also we identify a set of metrics to be used as evaluation criteria. If the identified requirements and metrics are adopted widely, potential location and tracking services will fit the real needs of AAL, and thus will increase the accessibility to AAL services by a larger sector of people. Moreover, in the paper, we evaluate two of the existing location techniques through the use of the proposed metrics. The aim is to asses to which level these solutions fulfill the identified requirements.

Fingerprint is one of the most widely used methods for locating devices in indoor wireless environments and we have witnessed the emergence of several positioning systems aimed for indoor environments based on this approach. However, additional efforts are required in order to improve the performance of these systems so that applications that are highly dependent on user location can provide better services to its users. In this work we discuss some improvements to the positioning accuracy of the fingerprint-based systems. Our algorithm ranks the information about the location in a hierarchical way by identifying the building, the floor, the room and the geometric position. The proposed fingerprint method uses a previously stored map of the signal strength at several positions and determines the position using similarity functions and majority rules. In particular, we compare different similarity functions to understand their impact on the accuracy of the positioning system. The experimental results confirm the possibility of correctly determining the building, the floor and the room where the persons or the objects are at with high rates, and with an average error around 3 meters. Moreover, detailed statistics about the errors are provided, showing that the average error metric, often used by many authors, hides many aspects on the system performance.

Understanding and modeling the way humans move in urban contexts is beneficial for many applications. The recent advances on positioning technologies, namely those based on the ubiquity of wireless networks, is facilitating the observation of people for human motion analysis. In this paper we present the result of a large scale work conducted to study the human mobility in a University's campuses. The study was conducted along several months, using data collected from thousands of users that freely moved inside the numerous buildings existent in two University campuses and a few other buildings in the city center. A Wi-Fi infrastructure of more than 550 access points provides Internet access to the academic community. We tracked the user movements by logging the devices connected to each access point. Based on that data, an analysis process that highlights the relationships between space features and human motion has been developed. In this paper we introduce the concepts of “place connectivity” and “flow across a boundary” to model these relationships. Results show the mobility patterns detected, which are the attraction places along the day, and what places are more strongly connected. This paper also includes an analysis of the short and long term movements between places. With this study we extended our understanding of the life in the campus, enabling us to feel the campus “pulse”.

Techniques using RSS fingerprinting for localization have been studied over a number of different technologies in many different scenarios. In the case of underground tunnels localization can be quite challenging, yet it is extremely important for safety reasons. In the specific case of the CERN tunnels, accurate and automatized localization methods would additionally allow the workflow of some activities to become substantially faster. In a radiation area this would also have the added benefit of reducing the exposure time of personnel conducting so called radiation surveys which have to be carried out before access can be granted. In this paper Fingerprinting techniques for GSM and Wireless LAN are studied and enhanced to take advantage of both network technologies simultaneously as well as the channels RSS differential and an observed effect in the radiated power in the leaky-feeder cables. Besides the higher accuracy achieved for a single technology, this methodology looks promising for scenarios where several types of wireless networks are available or expected to be installed at a later stage.

Accurate localization techniques have long been of major importance for safety systems and a lot of research has been conducted in the distributed computing field regarding its functionality and reliability. In the specific scenario of long yet narrow tunnels existing at CERN, localization methods will enable a number of applications and processes to substantially reduce human intervention. In this paper we evaluate the use of Fingerprinting techniques with GSM signal available throughout the LHC tunnel via a radiating cable and compare some methods to estimate the location. The existing GSM infrastructure and tunnel conditions seem to be favorable to the adoption of these Fingerprinting methods. Nevertheless significant variations in the signal have been observed which might be traced back to different operational states of accelerator equipment. These effects and their sources will be analyzed in more detail in order to improve the applied techniques and their accuracy under such challenging conditions.

Existing navigation systems are very appropriate for car navigation, but lack support for convenient pedestrian navigation and cannot be used indoors due to GPS limitations. In addition, the creation and the maintenance of the required models are costly and time consuming, and are usually based on proprietary data structures. In this paper we describe a navigation system based on a human inspired symbolic space model. We argue that symbolic space models are much easier to create and to maintain, and that they can support routing applications based on self-locating through the recognition of nearby features. Our symbolic space model is supported by a federation of servers where the spatial descriptions are stored, and which provide interfaces for feeding and querying the model. Local models residing in different servers may be connected between them, thus contributing to the system scalability.

Abstract – Ubiquitous computing and the development of context-aware applications have been limited by the lack of open and generic solutions. In this paper we propose a flexible location-context representation which supports data acquired through multiple sensors represented in different space models.

The mobile Internet is enabling a broad range of new applications that dynamically obtain information that is relevant to their current location. This type of application would greatly benefit from generic mechanisms for supporting the association between network resources and physical space, but existing systems are typically based on vertical approaches valid only for narrow application scenarios. This paper argues that a comprehensive solution to this issue should address the important challenges of heterogeneity and openness, and proposes an approach based on the concept of location-based service, i.e. a service whose usage is associated with physical space, as a generic abstraction to support the development of location-dependent systems. The paper describes a model for associating location scopes with services, an architecture to support the discovery of location-based services on the Internet, and a prototype infrastructure in which several services and applications have been developed for validating the architecture.

Infrared technology is a suitable alternative for the support of indoor wireless local area networks (WLANs). When compared to radio technologies, infrared offers the potential for lower cost, higher security and better resilience to interference provoked by users from adjacent cells. This paper presents experimental results of an infrared transceiver for diffuse systems based on pulse position modulation. The implementation followed the upcoming IEEE 802.11 specification. This transceiver was developed within the ESPRIT.6892 POWER (Portable Workstation for Education in Europe) project.

Wireless indoor infrared transmission systems are affected by noise and interference induced by natural and artificial ambient light. While the shot noise induced on the receiver photodiode by steady ambient light has been extensively described and included in system models, the interference produced by artificial light has only been mentioned as a source of degradation and quite simple descriptions have been presented. This paper presents a characterisation (through extensive measurements) of the interference produced by artificial light and proposes a simple model to describe it. These measurements show that artificial light can introduce significant in-band components for systems operating at bit rates up to several Mbit/s. Therefore it is essential to include it as part of the optical wireless indoor channel. The measurements show that fluorescent lamps driven by solid state ballasts produce the wider band interfering signals, and are then expected to be the more important source of degradation in optical wireless systems.

The design and implementation of a wireless infrared Ethernet link is considered. It is shown that, using commercially available devices, it is technical and economically feasible to implement wireless infrared extensions of Ethernet networks, maintaining the degree of complexity in transceiver electronics at very low level. In particular, it is shown that Manchester coding can still be used in the infrared path without impairing system range and performance. A laboratory prototype was developed and implemented using Manchester coding, achieving a bit error rate of 10/sup -10/ over a range typical of normal office environments. The utilization of low cost lenses, both for the LED's radiation pattern correction and as optical power collecting elements, was investigated and shown to provide significant performance improvements.>