Probabilistic Location Estimate of Passive Mobile Positioning Events

Uurijad, kes on püüdnud mõista inimeste liikumise mustreid, korjavad andmeid mobiilivõrkudelt. Mobiilid teevad sündmuse kirjeid iga kord, kui nendega helistatakse, saadetakse SMSi või kasutatakse Interneti. Sündmuste kirjed sisaldavad informatsiooni sellest, millisesse võrgu transiiversisse mobiiltelefon oli sel hetkel ühendatud. Võrgu ühe transiiveri leviala saab kasutada, et püüda positsioneerida telefoni geograafilist asukohta. Kasutades positsioneerimiseks transiiveri leviala, siis need hinnatavad asukohad pole punktid kaardil, vaid geograafilised alad, kus telefon võib olla kui ta on transiiveriga ühendatud.\n\r Mobiilide ühendamine transiiveritega sõltub mitmest muutujast, mis tähendab, et mobiil ei ole alati ühendatud kõige tugevama signaaliga transiiveriga. See teeb mobiili asukoha hindamise keerulisemaks, sest transiiverite levialad võivad üksteisest üleulatuda.\n\rVõrguplaan kirjeldab võrgus olevate transiiverite levialasid ning seda kasutatakse, et defineerida transiiverite levialasid.\n\rSelles lõputöös hinnatakse mobiilisündmuste positsioneerimise kvaliteeti ruumilise jaotuse tihedusfunktsioonidega. Luuakse erinevad võrguplaani variandid ja erinevate võrguplaanide kvaliteeti hinnatakse Bayesi statistikaga ja kasutatakse reaalsed asukoha andmed. Erinevate võrguplaanide kvaliteeti hinnatakse suurima tõepära meetodiga.\n\rVõrreldi RSSI ja Voronoi põhjal tehtud võrguplaane ja nende modificatsioone ja leiti, et Voronoi võrguplaanide puhul paistis asukoha positsioneerimine paremini kui RSSI võrguplaanide puhul.\n\rLisaks uuriti, kuidas transiiverite levialade üleulatamisel arvestamine Bayesi metoodiga mõjutab asukoha positsioneerimise täpsust. Leiti, et Bayesi levialade üleulatamise metood tegi halvemate võrguplaanide täpsust paremaks, aga paremate võrguplaanide täpsust halvemaks.Researchers, who are trying to understand human mobility patterns, collect data from cellular telephone networks. Mobiles are creating events every time they are used for calling, SMS, or the Internet. The events contain the information, in which network cell that mobile was at the moment of the event. Cell's coverage can be used for estimating the geographical location of the mobile. The estimated locations are not a point on the map, but the possible area, where the mobile may be when they are connected to that specific cell. \n\rMobiles connecting to cells are depending on multiple variables, meaning, that a mobile may not always connect to the cell with the strongest signal. That makes estimation of the mobile location more difficult, as the coverage areas may overlap with each other. \n\rCell plan is a description of cell coverage areas and there are multiple ways for defining cell coverage areas.\n\rThis thesis is about estimating mobile events positioning quality with spatial probability density functions. Different cell plan variants will be implemented and real ground truth location data is used to find the modification that maximizes the likelihood estimation. \n\rCompared RSSI-based and Voronoi-based cell plans and their modifications and was found that Voronoi-based cell plans are better for location positioning than the RSSI-based cell plans.\n\rFurthermore, Bayesian overlapping method was examined to see does applying it would improve location positioning accuracy. It was found that applying Bayesian overlapping methods improved the accuracy of the worse cell plans, but made accuracy worse for the better cell plans

D2D-based Cooperative Positioning Paradigm for Future Wireless Systems: A Survey

Emerging communication network applications require a location accuracy of less than 1m in more than 95% of the service area. For this purpose, 5G New Radio (NR) technology is designed to facilitate high-accuracy continuous localization. In 5G systems, the existence of high-density small cells and the possibility of the device-to-device (D2D) communication between mobile terminals paves the way for cooperative positioning applications. From the standardization perspective, D2D technology is already under consideration (5G NR Release 16) for ultra-dense networks enabling cooperative positioning and is expected to achieve the ubiquitous positioning of below one-meter accuracy, thereby fulfilling the 5G requirements. In this survey, the strengths and weaknesses of D2D as an enabling technology for cooperative cellular positioning are analyzed (including two D2D approaches to perform cooperative positioning); lessons learned and open issues are highlighted to serve as guidelines for future research

Automatic Passenger Counting on the Edge via Unsupervised Clustering

We present a device- and network-based solution for automatic passnger counting that operates on the edge in real time. The proposed solution consists of a low-cost WiFi scanner device equipped with custom algorithms for dealing with MAC address randomization. Our low-cost scanner is able to capture and analyze 802.11 probe requests emitted by passengers' devices such as laptops, smartphones, and tablets. The device is configured with a Python data-processing pipeline that combines data coming from different types of sensors and processes them on the fly. For the analysis task, we have devised a lightweight version of the DBSCAN algorithm. Our software artifact is designed in a modular way in order to accommodate possible extensions of the pipeline, e.g., either additional filters or data sources. Furthermore, we exploit multi-threading and multi-processing for speeding up the entire computation. The proposed solution has been tested with different types of mobile devices, obtaining promising experimental results. In this paper, we present the key ingredients of our edge computing solution

Occupancy Detection and People Counting Using WiFi Passive Radar

Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements

Optimizing the delivery of multimedia over mobile networks

Mención Internacional en el título de doctorThe consumption of multimedia content is moving from a residential environment to mobile phones. Mobile data traffic, driven mostly by video demand, is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. To tackle this, recent developments in anticipatory networking schemes make it possible to to predict the future capacity of mobile devices and optimize the allocation of the limited wireless resources. Further, optimizing Quality of Experience—smooth, quick, and high quality playback—is more difficult in the mobile setting, due to the highly dynamic nature of wireless links. A key requirement for achieving, both anticipatory networking schemes and QoE optimization, is estimating the available bandwidth of mobile devices. Ideally, this should be done quickly and with low overhead. In summary, we propose a series of improvements to the delivery of multimedia over mobile networks. We do so, be identifying inefficiencies in the interconnection of mobile operators with the servers hosting content, propose an algorithm to opportunistically create frequent capacity estimations suitable for use in resource optimization solutions and finally propose another algorithm able to estimate the bandwidth class of a device based on minimal traffic in order to identify the ideal streaming quality its connection may support before commencing playback. The main body of this thesis proposes two lightweight algorithms designed to provide bandwidth estimations under the high constraints of the mobile environment, such as and most notably the usually very limited traffic quota. To do so, we begin with providing a thorough overview of the communication path between a content server and a mobile device. We continue with analysing how accurate smartphone measurements can be and also go in depth identifying the various artifacts adding noise to the fidelity of on device measurements. Then, we first propose a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity. We proceed with another algorithm that takes advantage of the traffic generated by short-lived TCP connections, which form the majority of the mobile connections, to passively estimate the currently available bandwidth class. Our algorithm is able to extract useful information even if the TCP connection never exits the slow start phase. To the best of our knowledge, no other solution can operate with such constrained input. Our estimation method is able to achieve good precision despite artifacts introduced by the slow start behavior of TCP, mobile scheduler and phone hardware. We evaluate our solution against traces collected in 4 European countries. Furthermore, the small footprint of our algorithm allows its deployment on resource limited devices. Finally, in an attempt to face the rapid traffic increase, mobile application developers outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today’s mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today’s mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85% of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver. We also observe that the choice of operator’s Point of Presence (PoP) may inflate by at least 20% the delay towards popular websites.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Ahmed Elmokashfi.- Secretario: Rubén Cuevas Rumín.- Vocal: Paolo Din

Real-Time Crowd Counting based on wearable Ephemeral IDs

Crowd Counting is a very interesting problem aiming at counting people typically based on density averages and/or aerial images. This is very useful to prevent crowd crushes, especially on urban environments with high crowd density, or to count people in public demonstrations. In addition, in the last years, it has become of paramount importance for pandemic management. For those reasons, giving users automatic mechanisms to anticipate high risk situations is essential. In this work, we analyze ID-based Crowd Counting, and propose a real-time Crowd Counting system based on the Ephemeral ID broadcast by contact tracing applications on wearable devices. We also performed some simulations that show the accuracy of our system in different situations

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system