44 research outputs found

    Efficient double auction mechanisms in the energy grid with connected and islanded microgrids

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    Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSanjoy DasThe future energy grid is expected to operate in a decentralized fashion as a network of autonomous microgrids that are coordinated by a Distribution System Operator (DSO), which should allocate energy to them in an efficient manner. Each microgrid operating in either islanded or grid-connected mode may be considered to manage its own resources. This can take place through auctions with individual units of the microgrid as the agents. This research proposes efficient auction mechanisms for the energy grid, with is-landed and connected microgrids. The microgrid level auction is carried out by means of an intermediate agent called an aggregator. The individual consumer and producer units are modeled as selfish agents. With the microgrid in islanded mode, two aggregator-level auction classes are analyzed: (i) price-heterogeneous, and (ii) price homogeneous. Under the price heterogeneity paradigm, this research extends earlier work on the well-known, single-sided Kelly mechanism to double auctions. As in Kelly auctions, the proposed algorithm implements the bidding without using any agent level private infor-mation (i.e. generation capacity and utility functions). The proposed auction is shown to be an efficient mechanism that maximizes the social welfare, i.e. the sum of the utilities of all the agents. Furthermore, the research considers the situation where a subset of agents act as a coalition to redistribute the allocated energy and price using any other specific fairness criterion. The price homogeneous double auction algorithm proposed in this research ad-dresses the problem of price-anticipation, where each agent tries to influence the equilibri-um price of energy by placing strategic bids. As a result of this behavior, the auction鈥檚 efficiency is lowered. This research proposes a novel approach that is implemented by the aggregator, called virtual bidding, where the efficiency can be asymptotically maximized, even in the presence of price anticipatory bidders. Next, an auction mechanism for the energy grid, with multiple connected mi-crogrids is considered. A globally efficient bi-level auction algorithm is proposed. At the upper-level, the algorithm takes into account physical grid constraints in allocating energy to the microgrids. It is implemented by the DSO as a linear objective quadratic constraint problem that allows price heterogeneity across the aggregators. In parallel, each aggrega-tor implements its own lower-level price homogeneous auction with virtual bidding. The research concludes with a preliminary study on extending the DSO level auc-tion to multi-period day-ahead scheduling. It takes into account storage units and conven-tional generators that are present in the grid by formulating the auction as a mixed inte-ger linear programming problem

    Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?

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    We consider the problem of task allocation in a network of cyber-physical systems (CPSs). The network can have different states, and the tasks are of different types. The task arrival is stochastic and state-dependent. Every CPS is capable of performing each type of task with some specific state-dependent efficiency. The CPSs have to agree on task allocation prior to knowing about the realized network's state and/or the arrived tasks. We model the problem as a multi-state stochastic cooperative game with state uncertainty. We then use the concept of deterministic equivalence and sequential core to solve the problem. We establish the non-emptiness of the strong sequential core in our designed task allocation game and investigate its characteristics including uniqueness and optimality. Moreover, we prove that in the task allocation game, the strong sequential core is equivalent to Walrasian equilibrium under state uncertainty; consequently, it can be implemented by using the Walras' tatonnement process

    Multi-attribute demand characterization and layered service pricing

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    As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare

    Traffic offloading in future, heterogeneous mobile networks

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    The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. In order to tackle this challenge, Mobile Network Operators (MNOs) aim to increase their networks' capacity by expanding their infrastructure, deploying more Base Stations (BSs). Particularly, the creation of Heterogeneous Networks (HetNets) and the application of traffic offloading through the dense deployment of low-power BSs, the small cells (SCs), is one promising solution to address the aforementioned explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the MNOs, the emergence of third parties that deploy small cell networks creates new business opportunities. Thus, the investigation of frameworks that facilitate the implementation of outsourced traffic offloading, the collaboration and the transactions among MNOs and third-party small cell owners, as well as the provision of participation incentives for all stakeholders is essential for the deployment of the necessary new infrastructure and capacity expansion. The aforementioned emergence of third-party content providers and their applications not only drives the increase in mobile data traffic, but also create new Quality of Service (QoS) as well as Quality of Experience (QoE) requirements that the MNOs need to guarantee for the satisfaction of their subscribers. Moreover, even though the MNOs accommodate this traffic, they do not get any monetary compensation or subsidization for the required capacity expansion. On the contrary, their revenues reduce continuously. To that end, it is necessary to research and design network and economic functionalities adapted to the new requirements, such as QoE-aware Radio Resource Management and Dynamic Pricing (DP) strategies, which both guarantee the subscriber satisfaction and maximization the MNO profit (to compensate the diminished MNOs' revenues and the increasing deployment investment). Following a thorough investigation of the state-of-the-art, a set of research directions were identified. This dissertation consists of contributions on network sharing and outsourced traffic offloading for the capacity enhancement of MNO networks, and the design of network and economic functions for the sustainable deployment and use of the densely constructed HetNets. The contributions of this thesis are divided into two main parts, as described in the following. The first part of the thesis introduces an innovative approach on outsourced traffic offloading, where we present a framework for the Multi-Operator Radio Access Network (MORAN) sharing. The proposed framework is based on an auction scheme used by a monopolistic Small Cell Operator (SCO), through which he leases his SC infrastructure to MNOs. As the lack of information on the future offered load and the auction strategies creates uncertainty for the MNOs, we designed a learning mechanism that assists the MNOs in their bid-placing decisions. Our simulations show that our proposal almost maximizes the social welfare, satisfying the involved stakeholders and providing them with participation incentives. The second part of the thesis researches the use of network and economic functions for MNO profit maximization, while guaranteeing the users' satisfaction. Particularly, we designed a model that accommodates a plethora of services with various QoS and QoE requirements, as well as diverse pricing, that is, various service prices and different charging schemes. In this model, we proposed QoE-aware user association, resource allocation and joint resource allocation and dynamic pricing algorithms, which exploit the QoE-awareness and the network's economic aspects, such as the profit. Our simulations have shown that our proposals gain substantial more profit compared to traditional and state-of-the-art solutions, while providing a similar or even better network performance.El aumento de los proveedores de contenido de terceros y la introducci贸n de numerosas aplicaciones ha impulsado el crecimiento del tr谩fico de datos en redes m贸viles en los 煤ltimos a帽os. Para hacer frente a este desaf铆o, los operadores de redes m贸viles (Mobile Network Operators, MNOs) apuntan a aumentar la capacidad de sus redes mediante la expansi贸n de su infraestructura y el despliegue de m谩s estaciones base (BS). Particularmente, la creaci贸n de Redes Heterog茅neas (Heterogenous Networks, HetNets) y la aplicaci贸n de descarga de tr谩fico a trav茅s del despliegue denso de BSs de baja potencia, las c茅lulas peque帽as (small cells, SCs), es una soluci贸n prometedora para abordar el aumento del tr谩fico de datos explosivos antes mencionado. Debido a sus requisitos de implementaci贸n financiera, que los MNO no pudieron cumplir, la aparici贸n de terceros que implementan redes de c茅lulas peque帽as crea nuevas oportunidades comerciales. Por lo tanto, la investigaci贸n de marcos que faciliten la implementaci贸n de la descarga tercerizada de tr谩fico, la colaboraci贸n y las transacciones entre MNOs y terceros propietarios de c茅lulas peque帽as, as铆 como la provisi贸n de incentivos de participaci贸n para todas las partes interesadas esencial para el despliegue de la nueva infraestructura necesaria y la expansi贸n de la capacidad. La aparici贸n antes mencionada de proveedores de contenido de terceros y sus aplicaciones no solo impulsa el aumento del tr谩fico de datos m贸viles, sino tambi茅n crea nuevos requisitos de calidad de servicio (Quality of Service, QoS) y calidad de la experiencia (Quality of Experience, QoE) que los operadores de redes m贸viles deben garantizar para la satisfacci贸n de sus suscriptores. Adem谩s, a pesar de que los operadores de redes m贸viles adaptan este tr谩fico, no obtienen ninguna compensaci贸n monetaria o subsidio por la expansi贸n de capacidad requerida. Por el contrario, sus ingresos se reducen continuamente. Para ello, es necesario investigar y dise帽ar funcionalidades econ贸micas y de red adaptadas a los nuevos requisitos, tales como las estrategias QoE-conscientes de gesti贸n de recursos de radio y de precios din谩micos (Dynamic Pricing, DP), que garantizan la satisfacci贸n del abonado y la maximizaci贸n de la ganancia de operador m贸vil (para compensar los ingresos de los MNOs disminuidos y la creciente inversi贸n de implementaci贸n). Despu茅s de una investigaci贸n exhaustiva del estado del arte, se identificaron un conjunto de direcciones de investigaci贸n. Esta disertaci贸n consiste en contribuciones sobre el uso compartido de redes y la descarga tercerizada de tr谩fico para la mejora de la capacidad de redes MNO, y el dise帽o de funciones econ贸micas y de red para el despliegue y uso sostenible de las HetNets densamente construidas. Las contribuciones de esta tesis se dividen en dos partes principales, como se describe a continuaci贸n. La primera parte de la tesis presenta un enfoque innovador sobre la descarga subcontratada de tr谩fico, en el que presentamos un marco para el uso compartido de la red de acceso de radio de m煤ltiples operadores (Multi-Operator RAN, MORAN). El marco propuesto se basa en un esquema de subasta utilizado por un operador monop贸lico de celda peque帽a (Small Cell Operator, SCO), a trav茅s del cual arrienda su infraestructura SC a MNOs. Como la falta de informaci贸n sobre la futura carga de red y las estrategias de subasta creaban incertidumbre para los MNO, dise帽amos un mecanismo de aprendizaje que asiste a los MNO en sus decisiones de colocaci贸n de pujas. Nuestras simulaciones muestran que nuestra propuesta casi maximiza el bienestar social, satisfaciendo a las partes interesadas involucradas y proporcion谩ndoles incentivos de participaci贸n. La segunda parte de la tesis investiga el uso de las funciones econ贸micas y de red para la maximizaci贸n de los beneficios de los MNOs, al tiempo que garantiza la satisfacci贸n de los usuarios. Particularmente, dise帽amos un modelo que acomoda una gran cantidad de servicios con diversos requisitos de QoS y QoE, tanto como diversos precios, es decir, varios precios de servicio y diferentes esquemas de cobro. En este modelo, propusimos algoritmos QoE-conscientes para asociaci贸n de usuarios, asignaci贸n de recursos y conjunta asignaci贸n de recursos y de fijaci贸n din谩mica de precios, que explotan la conciencia de QoE y los aspectos econ贸micos de la red, como la ganancia. Nuestras simulaciones han demostrado que nuestras propuestas obtienen un beneficio sustancial en comparaci贸n con las soluciones tradicionales y del estado del arte, a la vez que proporcionan un rendimiento de red similar o incluso mejor.Postprint (published version

    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

    Traffic offloading in future, heterogeneous mobile networks

    Get PDF
    The rise of third-party content providers and the introduction of numerous applications has been driving the growth of mobile data traffic in the past few years. In order to tackle this challenge, Mobile Network Operators (MNOs) aim to increase their networks' capacity by expanding their infrastructure, deploying more Base Stations (BSs). Particularly, the creation of Heterogeneous Networks (HetNets) and the application of traffic offloading through the dense deployment of low-power BSs, the small cells (SCs), is one promising solution to address the aforementioned explosive data traffic increase. Due to their financial implementation requirements, which could not be met by the MNOs, the emergence of third parties that deploy small cell networks creates new business opportunities. Thus, the investigation of frameworks that facilitate the implementation of outsourced traffic offloading, the collaboration and the transactions among MNOs and third-party small cell owners, as well as the provision of participation incentives for all stakeholders is essential for the deployment of the necessary new infrastructure and capacity expansion. The aforementioned emergence of third-party content providers and their applications not only drives the increase in mobile data traffic, but also create new Quality of Service (QoS) as well as Quality of Experience (QoE) requirements that the MNOs need to guarantee for the satisfaction of their subscribers. Moreover, even though the MNOs accommodate this traffic, they do not get any monetary compensation or subsidization for the required capacity expansion. On the contrary, their revenues reduce continuously. To that end, it is necessary to research and design network and economic functionalities adapted to the new requirements, such as QoE-aware Radio Resource Management and Dynamic Pricing (DP) strategies, which both guarantee the subscriber satisfaction and maximization the MNO profit (to compensate the diminished MNOs' revenues and the increasing deployment investment). Following a thorough investigation of the state-of-the-art, a set of research directions were identified. This dissertation consists of contributions on network sharing and outsourced traffic offloading for the capacity enhancement of MNO networks, and the design of network and economic functions for the sustainable deployment and use of the densely constructed HetNets. The contributions of this thesis are divided into two main parts, as described in the following. The first part of the thesis introduces an innovative approach on outsourced traffic offloading, where we present a framework for the Multi-Operator Radio Access Network (MORAN) sharing. The proposed framework is based on an auction scheme used by a monopolistic Small Cell Operator (SCO), through which he leases his SC infrastructure to MNOs. As the lack of information on the future offered load and the auction strategies creates uncertainty for the MNOs, we designed a learning mechanism that assists the MNOs in their bid-placing decisions. Our simulations show that our proposal almost maximizes the social welfare, satisfying the involved stakeholders and providing them with participation incentives. The second part of the thesis researches the use of network and economic functions for MNO profit maximization, while guaranteeing the users' satisfaction. Particularly, we designed a model that accommodates a plethora of services with various QoS and QoE requirements, as well as diverse pricing, that is, various service prices and different charging schemes. In this model, we proposed QoE-aware user association, resource allocation and joint resource allocation and dynamic pricing algorithms, which exploit the QoE-awareness and the network's economic aspects, such as the profit. Our simulations have shown that our proposals gain substantial more profit compared to traditional and state-of-the-art solutions, while providing a similar or even better network performance.El aumento de los proveedores de contenido de terceros y la introducci贸n de numerosas aplicaciones ha impulsado el crecimiento del tr谩fico de datos en redes m贸viles en los 煤ltimos a帽os. Para hacer frente a este desaf铆o, los operadores de redes m贸viles (Mobile Network Operators, MNOs) apuntan a aumentar la capacidad de sus redes mediante la expansi贸n de su infraestructura y el despliegue de m谩s estaciones base (BS). Particularmente, la creaci贸n de Redes Heterog茅neas (Heterogenous Networks, HetNets) y la aplicaci贸n de descarga de tr谩fico a trav茅s del despliegue denso de BSs de baja potencia, las c茅lulas peque帽as (small cells, SCs), es una soluci贸n prometedora para abordar el aumento del tr谩fico de datos explosivos antes mencionado. Debido a sus requisitos de implementaci贸n financiera, que los MNO no pudieron cumplir, la aparici贸n de terceros que implementan redes de c茅lulas peque帽as crea nuevas oportunidades comerciales. Por lo tanto, la investigaci贸n de marcos que faciliten la implementaci贸n de la descarga tercerizada de tr谩fico, la colaboraci贸n y las transacciones entre MNOs y terceros propietarios de c茅lulas peque帽as, as铆 como la provisi贸n de incentivos de participaci贸n para todas las partes interesadas esencial para el despliegue de la nueva infraestructura necesaria y la expansi贸n de la capacidad. La aparici贸n antes mencionada de proveedores de contenido de terceros y sus aplicaciones no solo impulsa el aumento del tr谩fico de datos m贸viles, sino tambi茅n crea nuevos requisitos de calidad de servicio (Quality of Service, QoS) y calidad de la experiencia (Quality of Experience, QoE) que los operadores de redes m贸viles deben garantizar para la satisfacci贸n de sus suscriptores. Adem谩s, a pesar de que los operadores de redes m贸viles adaptan este tr谩fico, no obtienen ninguna compensaci贸n monetaria o subsidio por la expansi贸n de capacidad requerida. Por el contrario, sus ingresos se reducen continuamente. Para ello, es necesario investigar y dise帽ar funcionalidades econ贸micas y de red adaptadas a los nuevos requisitos, tales como las estrategias QoE-conscientes de gesti贸n de recursos de radio y de precios din谩micos (Dynamic Pricing, DP), que garantizan la satisfacci贸n del abonado y la maximizaci贸n de la ganancia de operador m贸vil (para compensar los ingresos de los MNOs disminuidos y la creciente inversi贸n de implementaci贸n). Despu茅s de una investigaci贸n exhaustiva del estado del arte, se identificaron un conjunto de direcciones de investigaci贸n. Esta disertaci贸n consiste en contribuciones sobre el uso compartido de redes y la descarga tercerizada de tr谩fico para la mejora de la capacidad de redes MNO, y el dise帽o de funciones econ贸micas y de red para el despliegue y uso sostenible de las HetNets densamente construidas. Las contribuciones de esta tesis se dividen en dos partes principales, como se describe a continuaci贸n. La primera parte de la tesis presenta un enfoque innovador sobre la descarga subcontratada de tr谩fico, en el que presentamos un marco para el uso compartido de la red de acceso de radio de m煤ltiples operadores (Multi-Operator RAN, MORAN). El marco propuesto se basa en un esquema de subasta utilizado por un operador monop贸lico de celda peque帽a (Small Cell Operator, SCO), a trav茅s del cual arrienda su infraestructura SC a MNOs. Como la falta de informaci贸n sobre la futura carga de red y las estrategias de subasta creaban incertidumbre para los MNO, dise帽amos un mecanismo de aprendizaje que asiste a los MNO en sus decisiones de colocaci贸n de pujas. Nuestras simulaciones muestran que nuestra propuesta casi maximiza el bienestar social, satisfaciendo a las partes interesadas involucradas y proporcion谩ndoles incentivos de participaci贸n. La segunda parte de la tesis investiga el uso de las funciones econ贸micas y de red para la maximizaci贸n de los beneficios de los MNOs, al tiempo que garantiza la satisfacci贸n de los usuarios. Particularmente, dise帽amos un modelo que acomoda una gran cantidad de servicios con diversos requisitos de QoS y QoE, tanto como diversos precios, es decir, varios precios de servicio y diferentes esquemas de cobro. En este modelo, propusimos algoritmos QoE-conscientes para asociaci贸n de usuarios, asignaci贸n de recursos y conjunta asignaci贸n de recursos y de fijaci贸n din谩mica de precios, que explotan la conciencia de QoE y los aspectos econ贸micos de la red, como la ganancia. Nuestras simulaciones han demostrado que nuestras propuestas obtienen un beneficio sustancial en comparaci贸n con las soluciones tradicionales y del estado del arte, a la vez que proporcionan un rendimiento de red similar o incluso mejor
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