101 research outputs found

    Doctor of Philosophy

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    dissertationWe are seeing an extensive proliferation of wireless devices including various types and forms of sensor nodes that are increasingly becoming ingrained in our daily lives. There has been a significant growth in wireless devices capabilities as well. This proliferation and rapid growth of wireless devices and their capabilities has led to the development of many distributed sensing and computing applications. In this dissertation, we propose and evaluate novel, efficient approaches for localization and computation offloading that harness distributed sensing and computing in wireless networks. In a significant part of this dissertation, we exploit distributed sensing to create efficient localization applications. First, using the sensing power of a set of Radio frequency (RF) sensors, we propose energy efficient approaches for target tracking application. Second, leveraging the sensing power of a distributed set of existing wireless devices, e.g., smartphones, internet-of-things devices, laptops, and modems, etc., we propose a novel approach to locate spectrum offenders. Third, we build efficient sampling approaches to select mobile sensing devices required for spectrum offenders localization. We also enhance our sampling approaches to take into account selfish behaviors of mobile devices. Finally, we investigate an attack on location privacy where the location of people moving inside a private area can be inferred using the radio characteristics of wireless links that are leaked by legitimate transmitters deployed inside the private area, and develop the first solution to mitigate this attack. While we focus on harnessing distributed sensing for localization in a big part of this dissertation, in the remaining part of this dissertation, we harness the computing power of nearby wireless devices for a computation offloading application. Specially, we propose a multidimensional auction for allocating the tasks of a job among nearby mobile devices based on their computational capabilities and also the cost of computation at these devices with the goal of reducing the overall job completion time and being beneficial to all the parties involved

    The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events

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    Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes

    Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

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    Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

    Open Infrastructure for Edge Computing

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    Edge computing, bringing the computation closer to end-users and data producers, has now firmly gained the status of enabling technology for the new kinds of emerging applications, such as Virtual/Augmented Reality and IoT. The motivation backing this rapidly developing computing paradigm is mainly two-fold. On the one hand, the goal is to minimize the latency that end-users experience, not only improving the quality of service but empowering new kinds of applications, which would not even be possible given higher delays. On the other, edge computing aims to save core networking bandwidth from being overwhelmed by myriads of IoT devices, sending their data to the cloud. After analyzing and aggregating IoT streams at edge servers, much less networking capacity will be required to persist remaining information in distant cloud datacenters. Having a solid motivation and experiencing continuous interest from both academia and industry, edge computing is still in its nascency. To leave adolescence and take its place on a par with the cloud computing paradigm, finally forming a versatile edge-cloud environment, the newcomer needs to overcome a number of challenges. First of all, the computing infrastructure to deploy edge applications and services is very limited at the moment. Indeed, there are initiatives supported by the telecommunication industry, like Multi-access Edge Computing. Also, cloud providers plan to establish their facilities near the edge of the network. However, we believe that even more efforts will be required to make edge servers generally available. Second, to emerge and function efficiently, the ecosystem of edge computing needs practices, standards, and governance mechanisms of its own kind. The specificity originates from the highly dispersed nature of the edge, implying high heterogeneity of resources and diverse administrative control over the computing facilities. Finally, the third challenge is the dynamicity of the edge computing environment due to, e.g., varying demand, migrating clients, etc. In this thesis, we outline underlying principles of what we call Open Infrastructure for Edge (OpenIE), identify its key features, and provide solutions for them. Intended to tackle the challenges we mentioned above, OpenIE defines a set of common practices and loosely coupled technologies creating a unified environment out of highly heterogeneous and administratively partitioned edge computing resources. Particularly, we design a protocol capable of discovering edge providers on a global scale. Further, we propose a framework of Ingelligent Containers (ICONs), capable of autonomous decision making and forming a service overlay on a large-scale edge-cloud setting. As edge providers need to be economically incentivized, we devise a truthful double auction mechanism where edge providers can meet application owners or administrators in need of deploying an edge service. Due to truthfulness, in our auction, it is the best strategy for all participants to bid one's privately known valuation (or cost), thus making complex market behavior strategies obsolete. We analyze the potential of distributed ledgers to serve for OpenIE decentralized agreement and transaction handling and show how our auction can be implemented with the help of distributed ledgers. With the key building blocks of OpenIE, mentioned above, we hope to make an entrance for anyone interested in service provisioning at the edge as easy as possible. We hope that with the emergence of independent edge providers, edge computing will finally become pervasive.Reunalaskenta, joka tuo laskentakapasiteettia lähemmäksi loppukäyttäjiä ja datan tuottajia, on noussut uudentyyppisten sovelluksien, kuten virtuaalisen ja lisätyn todellisuuden (VR/AR) sekä esineiden internetin (IoT) keskeiseksi mahdollistajaksi. Reunalaskennan kehitystä tukevat pääosin kaksi sen tuomaa etua. Ensiksi, reunalaskenta minimoi loppukäyttäjien kokemaa latenssia mahdollistaen uudentyyppisiä sovelluksia. Toiseksi, reunalaskenta säästää ydinverkon tiedonsiirtokapasiteettia, esimerkiksi IoT-laitteiden pilveen lähettämien tietojen osalta. Kun reunapalvelimet analysoivat ja aggregoivat IoT-virrat, verkkokapasiteettia tarvitaan paljon vähemmän. Reunalaskentaan on panostettu paljon, sekä teollisuuden, että tutkimuksen osalta. Reunalaskennan kehittymispolulla monipuoliseksi reunapilviympäristöksi on edessä useita haasteita. Ensinnäkin laskentakapasiteetti tietoverkkojen reunalla on tällä hetkellä hyvin rajallinen. Vaikka teleoperaattorit ja pilvipalvelujen tarjoajat suunnittelevat lisäävänsä laskentakapasiteettia reunalaskennan tarpeisiin, uskomme kuitenkin, että enemmän ponnisteluja tarvitaan, jotta reunalaskennan edut olisivat yleisesti saatavilla. Toiseksi, toimiakseen tehokkaasti, reunalaskennan ekosysteemi tarvitsee omat käytäntönsä, standardinsa ja hallintamekanisminsa. Reunalaskenan erityistarpeet johtuvat resurssien heterogeenisyydestä, niiden suuresta maantieteellisesta hajautuksesta ja hallinnollisesta jaosta. Kolmas haaste on reunalaskentaympäristön dynaamisuus, joka johtuu esimerkiksi vaihtelevasta kysynnästä ja asiakkaiden liikkuvuudesta. Tässä väitöstutkimuksessa esittelemme Avoimen Infrastruktuurin Reunalaskennalle (OpenIE), joka vastaa edellä mainittuihin haasteisiin, ja tunnistamme ongelman pääominaisuudet ja tarjoamme niihin ratkaisuja. OpenIE määrittelee joukon yleisiä käytäntöjä ja löyhästi yhdistettyjä tekniikoita, jotka luovat yhtenäisen ympäristön erittäin heterogeenisistä ja hallinnollisesti jaetuista reunalaskentaresursseista. Suunnittelemme protokollan, joka kykenee etsimään reunaoperaattoreita maailmanlaajuisesti. Lisäksi ehdotamme Älykontti (ICON) -kehystä, joka kykenee itsenäiseen päätöksentekoon ja muodostaa palvelupäällysteen laajamittaisessa reunapilviympäristössä. Koska reunaoperaattoreita on kannustettava taloudellisesti, suunnittelemme totuudenmukaisen huutokauppamekanismin, jossa reunapalveluntarjoajat voivat kohdata sovellusten omistajia tai järjestelmien omistajia, jotka tarvitsevat reunalaskentakapasiteettia. Totuudenmukaisessa huutokaupassa paras strategia kaikille osallistujille on tehdä tarjous yksityisesti tunnetun arvostuksen perusteella, mikä tekee monimutkaisen markkinastrategian kehittämisen tarpeettomaksi. Analysoimme lohkoketjualustojen potentiaalia palvella OpenIE:n hajautetun sopimisen ja tapahtumien käsittelyä ja näytämme, miten huutokauppamme voidaan toteuttaa lohkoketjuteknologia hyödyntäen. Edellä mainittujen OpenIE:n keskeisten kompponenttien avulla pyrimme luomaan yleisiä puitteita joiden avulla jokainen reunalaskennan kapasiteetin tarjoamisesta kiinnostunut taho voisi ryhtyä palveluntarjojaksi helposti. Riippumattomien reunapalveluntarjoajien mukaantulo tekisi reunalaskennan lupaamat hyödyt yleisesti saataviksi

    Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses

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    In the vehicular mixed reality (MR) Metaverse, the distance between physical and virtual entities can be overcome by fusing the physical and virtual environments with multi-dimensional communications in autonomous driving systems. Assisted by digital twin (DT) technologies, connected autonomous vehicles (AVs), roadside units (RSU), and virtual simulators can maintain the vehicular MR Metaverse via digital simulations for sharing data and making driving decisions collaboratively. However, large-scale traffic and driving simulation via realistic data collection and fusion from the physical world for online prediction and offline training in autonomous driving systems are difficult and costly. In this paper, we propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations for improving driving safety and traffic efficiency. First, we propose a multi-task DT offloading model for the reliable execution of heterogeneous DT tasks with different requirements at RSUs. Then, based on the preferences of AV's DTs and collected realistic data, virtual simulators can synthesize unlimited conditioned driving and traffic datasets to further improve robustness. Finally, we propose a multi-task enhanced auction-based mechanism to provide fine-grained incentives for RSUs in providing resources for autonomous driving. The property analysis and experimental results demonstrate that the proposed mechanism and architecture are strategy-proof and effective, respectively
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