Indoor positioning systems based on Bluetooth 5.1 Direction Finding technology have recently attracted significant attention due to their capability to estimate the Angle of Arrival (AoA) of radio signals using commercial off-the-shelf devices. Despite this progress, the availability of large-scale, well-annotated experimental datasets collected under realistic conditions remains limited. This paper presents a comprehensive experimental dataset for indoor localization based on BLE 5.1 devices, providing synchronized azimuth and elevation AoA measurements together with Received Signal Strength (RSS) values and precise ground-truth annotations. Data were acquired in a 110 m 2 indoor environment with an adjacent corridor, deploying nine anchors in two configurations (wall-mounted and ceiling-mounted) and two wearable BLE tags. The campaign covers three scenarios: calibration (187 static reference points), static measurements with body orientation variations, and multiple mobility use-cases reproducing realistic walking patterns and posture changes. Overall, more than 4.5 million samples were collected. A preliminary analysis highlights the impact of anchor geometry, field of view, and body shadowing on angular accuracy. The dataset is publicly available and provides a comprehensive benchmark for evaluating angle-based localization, fingerprinting, filtering, and machine-learning approaches in realistic indoor environments.

Indoor navigation presents unique challenges due to complex layouts and the unavailability of GNSS signals. Existing solutions often struggle with contextual adaptation, and typically require dedicated hardware. In this work, we explore the potential of a Large Language Model (LLM), i.e., ChatGPT, to generate natural, context-aware navigation instructions from indoor map images. We design and evaluate test cases across different real-world environments, analyzing the effectiveness of LLMs in interpreting spatial layouts, handling user constraints, and planning efficient routes. Our findings demonstrate the potential of LLMs for supporting personalized indoor navigation, with an average of 86.59% correct indications and a maximum of 97.14%. The proposed system achieves high accuracy and reasoning performance. These results have key implications for AI-driven navigation and assistive technologies.

In this work, we propose a novel framework based on Physics-Informed Neural Networks (PINNs) for directly estimating indoor positions, a method that, to the best of our knowledge, has not been previously explored. Training is performed on a public BLE dataset that includes a variety of indoor scenarios, including Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions caused by human body signal attenuation. The integration of physics-compliant synthetic data during the training phase significantly reduces dependence on large-scale real-world datasets, enabling the use of a simple Multilayer Perceptron (MLP) architecture. Our results demonstrate that combining PINNs with real-world measurements enhances model generalization without compromising accuracy.

Several radio-frequency technologies have been investigated to develop accurate indoor localization systems, each offering distinct techniques for estimating a target’s position in indoor environments. Among them, the Ultra-WideBand (UWB) technology is a promising approach because it can estimate the distance and angle between a tag and an anchor. In this work, we evaluate the performance of a commercial UWB kit with a systematic data collection campaign. We gather data in a realistic setting, comparing estimated and actual Angles of Arrival (AoA) and distances. Results highlight that, while the system performs reliably in most scenarios, a few instances reveal noticeable deviations from the Ground Truth (GT) data.

Indoor localization is crucial for developing intelligent environments capable of understanding user contexts and adapting to environmental changes. Bluetooth 5.1 Direction Finding is a recent specification that leverages the angle of departure (AoD) and angle of arrival (AoA) of radio signals to locate objects or people indoors. This paper presents a set of algorithms that estimate user positions using AoA values and the concept of the Confidence Region (CR), which defines the expected position uncertainty and helps to remove outlier measurements, thereby improving performance compared to traditional triangulation algorithms. We validate the algorithms with a publicly available dataset, and analyze the impact of body orientation relative to receiving units. The experimental results highlight the limitations and potential of the proposed solutions. From our experiments, we observe that the Conditional All-in algorithm presented in this work, achieves the best performance across all configuration settings in both line-of-sight and non-line-of-sight conditions.

This work describes the ongoing design and development of the METRIQA platform, hosting the Italian agrifood data space.Both are key components that the Italian National Research Centre for Agricultural Technologies is putting forward in its activities.We present a high-level description of the platform, which is designed to provide web-like access to digital resources and services following an approach called Web of Agri-Food, to support the digital transformation of the sector in Italy.To show its potential, we also present a real case study demonstrating both the benefits and impacts of the proposed architecture, connecting stakeholders and authorities at different levels.

The importance of reproducibility and transparency in scientific research has always been a cornerstone of the scientific Ethos. Recently, after identifying the challenges in terms of reproducibility in various research fields, the necessity of wide adoption of Open Science practices has become prominent. The field of Indoor Positioning and Indoor Navigation is no exception to these realizations. The current work provides a comprehensive review of Open Science practices followed in recent publications in the field, analyzing all reference papers from the 2022 and 2023 editions of the International Conference on Indoor Positioning and Indoor Navigation (IPIN). Particularly, the level of use of open data, open code, and open materials, is studied. Moreover, for all works relying on Open Research Data (ORD), our analysis went a step deeper characterizing multiple relevant features describing the data used, such as the technologies, the measurement types, and the environments associated with the open data. Our findings reveal that 22.4% of papers use open research data, 10.5% utilize open code, and 21.1% incorporate other open materials. However, only 7.9% of papers provide both open data and code. This study underscores the need for wider adoption of those practices, to enhance the transparency, reproducibility, replicability, and reliability of research outcomes of the field of indoor positioning. The files containing the complete characterization of the reviewed publications and of the Open Science practices followed are publicly available in [1].

In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users’ smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery’s museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.

The last decade showed a clear technological trend toward the adoption of heterogeneous source of information, combined with data-fusion strategies to increase the performance of indoor localization systems. In this respect, the adoption of short-range network protocols such as WiFi and Bluetooth represent a common approach. We investigate, in this work, the use of Bluetooth 5.1 Direction Finding specification to test an indoor localization system solely based on the estimated Angle of Arrival (AoA) between an anchor and a receiver. We first detail our experimental data collection campaign and the adopted hardware. Then, we study not only the accuracy of the estimated angles on two reference planes but also the localization error introduced with the proposed algorithm by varying the body orientation of the target user, namely North, South, West, Est. Experimental results in a real-world indoor environment show an average localization error of 2.08m with only 1 anchor node and 5° of AoA' error for all 28 monitored locations. We also identify regions in which the AoA estimation rapidly decreases, giving rise to the possibility of identifying the boundaries of the adopted technology.

Indoor localization provides important context information to develop Intelligent Environments able to understand user situations, to react and adapt to changes in the surrounding environment. Bluetooth 5.1 Direction Finding (DF) is a recent specification based on angle of departure (AoD) and arrival (AoA) of radio signals and it is addressed to localize objects or people in indoor scenarios. In this work, we study the error propagation of an indoor localization system based on AoA technique and on multiple anchor receivers.

The widespread use of Location Based Services (LBS) drives the pervasive adoption of localization systems available anywhere. Environments equipped with multiple indoor localization systems (ILSs), require managing the transition from one ILS to another in order to continue localizing the user’s device even when moving indoors or outdoor-to-indoor environments. In this paper, we focus on the handoff procedure, whose goal is enabling a device to trigger the transition between ILSs when specific conditions are verified. We describe the activation of handoff procedures by considering three types of ILS design and deployment, each with increasing complexity. Moreover, this work defines three handoff algorithms based on the proximity detection, and we test them in a realistic environment characterized by two contiguous ILSs.

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.

User location and tracking information are increasingly used for contact tracing and social community detection. In-door positioning and indoor navigation systems are reaching good performances in several realistic scenarios. After an evaluation exclusively done through simulations, nowadays, these systems are trying to reach robust performances and good accuracy in heterogeneous environments. Problems are manifold as each environment presents a structure that strongly affects inertial sensors and radio signal propagation. Generally, systems showing the best performances rely on an extended knowledge of the indoor map. Moreover, they implement a model for pedestrian dynamics in terms of e.g step length, stride and the behaviour of the target users. Experimental results obtained during realistic indoor competitions, clearly show that performances drop when such systems are used in unseen scenarios in which an external user test the proposed solution. In fact, many parameters that are generally calibrated and set to maximize the performances might not work as expected. In this paper, we highlight which best practices should be applied for model calibration in smartphone-based indoor positioning systems. We describe a reference system based on a particle filter, and we show the most relevant parameters and the main factors that are generally in common with all similar systems in the literature. We also present the Run-Once tool for reaching optimal parameters, highlighting those best practices that should be applied to indoor positioning systems to maximize their performances and improve their robustness.

The Bluetooth 5.1. Direction Finding (DF) specification opens to the possibility of estimating the angle between an emitting and a receiving device. Such angle is generally measured estimating the Angle of Arrival (AoA) or the Angle of Departure (AoD). In particular, knowledge about AoA between a set of anchor nodes and a moving target could be used to localize the target, with greater accuracy with respect to traditional approaches based on the Received Signal Strength of the received messages. In this work, we rigorously evaluate the performance of a commercial kit implementing the DF specification, with the purpose of understanding how the AoA measure varies with respect to the angles' ground truth. We describe two real-world experimental scenarios and we compute the errors between the estimated and actual angles. We also discuss three key aspects for the purpose of adopting BT 5.1 in indoor localization applications.

Indoor localisation is a thriving field, whose progresses are mainly led by innovations in sensor technology, both hardware and software. With a focus on smartphone-based personal navigation, we examine the evolution of sensing technologies in eleven leading applications. In order to select applications we choose among independently-tested prototypes, as opposed to simulation or laboratory-only experiments. To this end, we look at the best performers in the smartphone-based Tracks of IPIN competitions. This selection is particularly severe and significant, as this competition Track is performed live, without an opportunity for competitors to instrument or prepare the site or to know the path in advance and with only two attempts allowed, of which the best result is taken. An independent actor holds in hand the smartphone running the competing system, and results are downloaded from the phone immediately after the competition path is completed, without any post-processing. We show how sensing technologies have evolved from 2014 to 2019 and show a trend towards improving accuracy performance. Last, we provide insight in the role that sensors and algorithms play in the evolution of smartphone-based indoor localisation solutions.

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.

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

The way people interact in daily life is a challenging phenomenon to capture and to study without altering the natural rhythm of interactions. Our work investigates the possibility of automatically detecting proximity among people, the first mandatory condition before a dyad starts interacting. We present Remote Detection of Human Proximity (ReD-HuP), an algorithm based on the analysis of Bluetooth Low Energy beacons emitted by commercial wearable tags. We validate ReD-HuP with real-world indoor settings and we compare its performance with respect to detailed ground truth data collected from a number of volunteers. Experimental results show an accuracy and F-Score metric up to 95%.

Indoor localisation systems have been studied in the literature for more than ten years and nowadays are starting to approach the market. While technology is not mature yet, we can argue that the single biggest obstacle to wide adoption is the lack of standard ways to integrate different systems together. The missing pieces are a common taxonomy, definition of services, protocols. This work is an attempt to define what is next for indoor localisation systems in order to promote market adoption. It is a first high-level attempt at defining a taxonomy of indoor positioning systems, at outlining the main phases of a protocol for the utilisation of different cooperating indoor localisation systems, and at drawing a vision of services and applications in the close future.

Indoor localisation systems have been studied in the literature for more than ten years and are starting to approach the market. The absence of standard evaluation methods is one of the obstacles to their adoption outside of customised environments. Specifically, the definition of benchmarking methodologies, common evaluation criteria, standardised methodologies useful to developers, testers, and end users is an open challenge. The need for common benchmarks has been tackled by some initiatives in recent years: EvAAL, EVARILOS, the Microsoft competition and the IPIN competition. The first formal attempt at defining a standard methodology to evaluate indoor localisation systems is the ISO/IEC 18305:2016 International Standard, which defines a complete framework for performing Test&Evaluation of localisation and tracking systems. This work is a first critical reading of the standard, intended to be a key contribution to the activities of the International Standards Committee of IPIN.

Indoor environments are becoming more and more sensorized. Technologies such as Bluetooth, Wi-Fi and RFID are commonly used to provide connectivity to people living in such spaces. However, these technologies can also be exploited to automatically detect empty/occupied indoor areas and who is occupying that area. Our work goes toward such direction proposing an occupancy detection strategy based on the Bluetooth Low Energy (BLE) stack. We designed our solution by considering two fundamental challenges: costs and power-efficiency. We tested our system in several offices of our research institute by deploying few Bluetooth receivers and assigning to people a Bluetooth tag integrated with the institute's badge. We analyzed the performance of our solution with Bluetooth tags emitting at one single power first, and then we further refined our solution by considering the possibility of using beacons emitting simultaneously at different powers. The obtained results show a high accuracy without significantly affecting the energy consumption of the tags.

Indoor localization is a key topic for the Ambient Intelligence (AmI) research community. In this scenarios, recent advancements in wearable technologies, particularly smartwatches with built-in sensors, and personal devices, such as smartphones, are being seen as the breakthrough for making concrete the envisioned Smart Environment (SE) paradigm. In particular, scenarios devoted to indoor localization represent a key challenge to be addressed. Many works try to solve the indoor localization issue, but the lack of a common dataset or frameworks to compare and evaluate solutions represent a big barrier to be overcome in the field. The unavailability and uncertainty of public datasets hinders the possibility to compare different indoor localization algorithms. This constitutes the main motivation of the proposed dataset described herein. We collected Wi-Fi and geo-magnetic field fingerprints, together with inertial sensor data during two campaigns performed in the same environment. Retrieving sincronized data from a smartwatch and a smartphone worn by users at the purpose of create and present a public available dataset is the goal of this work.

While indoor localization techniques based on Wi-Fi RSS measurements have been extensively studied, their application to eavesdropping Wi-Fi probe requests sent from mobile devices in large indoor environments, such as shopping malls, is scarce or absent in the literature. The idea behind this work is to observe Wi-Fi enabled smartphones, especially when they are not associated to a network. They periodically perform active network scanning by issuing probe requests, which are detected by networked sniffing devices produced by Cloud4Wi®. We experimentally investigate the opportunities offered by passive gathering of Wi-Fi probes for purposes of crowd positioning in areas of interest. Our preliminary experimental setting convincingly shows that a small number of sniffing devices may be enough for analysing crowd movements in indoor areas.

IoT and cloud represent the breakthrough for making concrete the envisioned scenarios for Smart Environments and specifically, those scenarios devoted to the human well-being. Under this respect, we propose a system focused on the quality of the environments where employees work with particular attention to the energy consumption. We describe a long-term monitoring system together with the Stigma algorithm designed to detect the presence or the absence of a worker by exploiting sensing information.

Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.

Device-free localization algorithms attract, among others, the attention of researchers working in the Ambient Assisted Living (AAL) scenarios, where the target user might not be able or willing to wear any devices. We concentrate on systems that exploit the Received Signal Strength indicator coming from wireless devices whose position is known, called anchors. In this paper we select and test the main device-free localization solutions and experimentally compare their performance using a smaller number of anchors than commonly found in the literature. We illustrate the procedure used to validate our comparing procedure and we give suggestions on usability in the application scenarios typical of AAL. To the best of our knowledge, this is the first direct comparison between different device-free algorithms using the same input data for all of them, and the first one that compares their performance with a varying number of anchors. Thanks to the characteristics of our comparison procedure, we can make suggestions about the more appropriate algorithms to use for different kinds of applications.

Indoor localization applications that involve Wireless Sensor Networks (WSNs) identify the target position by measuring the Received Signal Strength (RSS), the Time of Arrival (ToA), the Time Difference of Arrival (TDoA) or the Angle of Arrival (AoA). Of these, the most promising for low-cost applications are those based on measures of the RSS, which exploit the relationship between RSS and the distance, or more reliably the relation between the multi-path interference (shadowing) and the position of the target. These methods work with WSNs based on Wi-Fi, Bluetooth and ZigBee sensor technologies. In this paper we concentrate on device-free RSS-based indoor localization methods. These methods, which have generated much research interest in the last few years, are now starting to hit the market. Specifically, the purpose of this paper is to assess the performance improvements of a Variance-based Radio Tomographic Imaging technique, when scanning various radio channels with respect to using only one, the latter being the “minimum introduced interference” option. Moreover, in this paper we will discuss in which application scenario the multi-channel scanning technique is usable and appropriate. The experimental data used for target localization are captured by wireless sensors deployed in the localization area and the localization error metrics include the mean square error and percentiles of the error distribution. Specifically, we aim to study the localization error reduction obtained by using multiple ZigBee channels, with respect to using a single channel.

The increasing demand for services and higher comfort levels inside buildings, together with the rise in time spent indoor, ensure an upward trend in indoor localization demand for the future. Evaluation of indoor localization systems is particularly challenging due to the complexity of such systems and to the variety of solutions adopted and services offered. EvAAL is an international competition aimed at evaluating and assessing indoor localization systems. The fifth edition of EvAAL promotes competitions on indoor localization in large environments. This paper describes its technical aspects, the competing systems and the results.

In this paper, a feasibility study where small wireless devices are used to classify some typical users positions in the bed is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz are supposed to be widely deployed in indoor settings and on peoples bodies in tomorrows pervasive computing environments. The key idea of this work is to leverage their presence by collecting the received signal strength (RSS) measured among fixed devices, deployed in the environment, and the wearable one. The RSS measurements are used to classify a set of users positions in the bed, monitoring the activities of patients unable to make the desirable bodily movements. The collected data are classified using both support vector machine and K-nearest neighbour methods, in order to recognize the different users position, and thus supporting the bedsores issue.

ABSTRACT The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI‐based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd.

RSS-based indoor localization systems are widely accepted in the literature as one of the less invasive localization technique. In fact, this range-based approach does not require any special hardware and is available in most standard wireless devices. Furthermore, judicious use of RSS has not a significant impact on local power consumption, sensor size, and cost. In front of these interesting characteristics, the performance of the RSS approach is worst with respect to some more invasive ad hoc hardware range-based solutions (such as Angle of Arrival, Time of Arrival etc…). In this paper we propose a localization method that leveraging the context information, such as the knowledge of being in a given room, increases the localization accuracy of RSS-based methods. Performance evaluation is done via real measurements in an office environment composed of three adjacent rooms.

A feasibility study, where small wireless transceivers are used to classify some typical limb movements used in physical therapy processes is presented. Wearable wireless low-cost commercial transceivers operating at 2.4 GHz are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The key idea of this work is to exploit their presence by collecting the received signal strength measured between those worn by a person. The measurements are used to classify a set of kinesiotherapy activities. The collected data are classified by using both support vector machine and K-nearest neighbor methods, in order to recognise the different activities.

We examine the indoor single-room localisation problem while using multiple fixed transmitters (anchors) and multiple mobile receivers placed on the user (mobiles). Anchors transmit a periodic beacon that the mobiles receive and of which they measure the received power level value (RSSI). Using this information only, which requires no specialised hardware, the mobiles estimate the position and orientation of the user. Many methods have been proposed to tackle this problem. In this paper we describe a purely theoretical procedure that aims to evaluate the maximum attainable performance of any real methods using RSSI for localisation purpose. Our analysis we present is based on a fine grid of RSSI values in a room, which are computed via ray-tracing, and a maximum-likelihood approach to localisation. Here we illustrate the performance gains of using multiple mobiles versus using a single one and and the attainable performance of user orientation estimation.

Quality of experience is becoming an important parameter for estimating the end user perceived video quality. Video coding algorithms are currently increasing the compression performances by exploiting the temporal and spatial correlations of multimedia information. Such a trend is self-defeating in hybrid networks, due to the frequent channel impairments experienced. Here, the authors present a double-stage forward error correction (FEC) scheme to reduce the channel impairments that a multimedia communication undergoes when broadcasting a video stream through an hybrid infrastructure constituted by satellite and terrestrial wireless links. The authors present a detailed statistical description of the terrestrial wireless channel and exploit it to design the parameters for tuning the algorithm. Simulations results show that this approach not only performs better than the error recovery techniques currently used in the literature, but it also experiences a significant reduction in the bandwidth overhead.

The real-time control of multiple queues handling traffic of different nature is obtaining increasing relevance in both the uplink and downlink of wireless telecommunication networks, characterised by the presence of a central access point. Such is the case of satellite networks, with either on-board processing or double-hop configuration, besides a number of terrestrial local and metropolitan wireless networks. Given a certain amount of available bandwidth, the problem is that of deciding, within a certain time frame, the allocation of bandwidth partitions for each traffic queue, whose packets are awaiting transmission; eventually, this determines the transmission rates to be passed to the scheduler and to the physical layer adaptive coding and modulation devices. In a satellite network, where this task is accomplished by a master station, residing at the access point, it is possible to take such decisions by means of a centralised controller, based on real-time instantaneous (in the downstream direction) or delayed (in the upstream) information on the queues' state. The study derives a control law to be used in this task, by adopting an approach based on optimal linear quadratic regulation. Both cases of un-delayed and delayed information are considered. The control laws are tested in a geo-stationary satellite scenario of digital video broadcasting – return channel via satellite (DVB-RCS), and the queues are considered at the medium access control level. Simulation results under real-traffic traces are also presented to highlight the effectiveness of the control and to compare alternative solutions.

Localization systems based on Received Signal Strength Indicator (RSSI) exploit fingerprinting (based on extensive signal strength measurements) to calibrate the system parameters. This procedure is very expensive in terms of time as it relies on human operators. In this paper we propose a virtual calibration procedure which only exploits the measurements of the RSSI between pairs of anchors. In particular, we propose two procedures for virtual calibration and we evaluate their performance with respect to an ad-hoc calibration campaign by performing measures in an indoor environment with an IEEE 802.15.4 sensor network.

We propose a novel localization algorithm of mobile sensors based on wireless sensor networks providing RSSI measurements between the mobile and the fixed sensors (anchors) in the network. The algorithm selects and weights the RSSI measurements according to their strength, and it uses a propagation model to transform RSSI measurements into distances, in order to estimate the position of the mobile. The algorithm also uses a virtual calibration method of the propagation model that does not require human intervention. By an experimental setup we show that the localization algorithm increases the performance with respect to the commonly used least mean square algorithm showing also how to achieve a wished accuracy increasing the anchor density.

This paper aims at providing different cost-efficient solutions for the channel impairments in tropical areas. In order to extend service to isolated areas, we propose hybrid architecture based on DVB-S2/RCS+Wi-Fi networks. In this scenario the satellite channel is affected by deep rain events that do not allow ACM modes to protect data. Moreover, the delay in the ACM reaction to fade changes can affect the quality of the video transmission. In order to avoid the QoS reduction, we focus on different fading mitigation techniques (FMT), designing a multi-layer scheme protection, using LL-FEC and AL-FEC in higher layers, and optimizing the intervention threshold and super-frame length in the physical layer.

We present a measurement-based model of the frame error process on a Wi-Fi channel in rural environments. Measures are obtained in controlled conditions, and careful statistical analysis is performed on the data, providing information which the network simulation literature is lacking. Results indicate that most network simulators use a frame loss model that can miss important transmission impairments even at a short distance, particularly when considering antenna radiation pattern anisotropy and multi-rate switching.

With the advent of ubiquitous and pervasive computing environments, one of promising applications is a guidance system. In this paper, we propose a mobile mixed reality guide system for indoor environments, [email protected] A mobile computing device (Sony's Ultra Mobile PC) is hidden inside a jacket and a user selects a destination inside a building through voice commands. A 3D virtual assistant then appears in the see-through HMD and guides him/her to destination. Thus, the user simply follows the virtual guide. [email protected] also suggests the most suitable virtual character (e.g. human guide, dog, cat, etc.) based on user preferences and profiles. Depending on user profiles, different security levels and authorizations for content are previewed. Concerning indoor location tracking, WiFi, RFID, and sensor-based methods are integrated in this system to have maximum flexibility. Moreover smart and transparent wireless connectivity provides the user terminal with fast and seamless transition among Access Points (APs). Different AR navigation approaches have been studied: [Olwal 2006], [Elmqvist et al.] and [Newman et al.] work indoors while [Bell et al. 2002] and [Reitmayr and Drummond 2006] are employed outdoors. Accurate tracking and registration is still an open issue and recently it has mostly been tackled by no single method, but mostly through aggregation of tracking and localization methods, mostly based on handheld AR. A truly wearable, HMD based mobile AR navigation aid for both indoors and outdoors with rich 3D content remains an open issue and a very active field of multi-discipline research.