Energy and cost management in shared heterogeneous network deployments

During the recent years, a huge augmentation of the data traffic volume has been noticed, while a further steep increase is expected in the following years. As a result, questions have been raised over the years about the energy consumption needs of the wireless telecommunication networks, their carbon dioxide emissions and their operational expenses. Aiming at meeting the high traffic demands with flat energy consumption and flat incurred expenses, mobile network operators (MNOs) have opted to improve their position (i) by deploying heterogeneous networks (HetNets), which are consisted of macrocell base stations (MBSs) and small cell base stations (SBSs) and (ii) by sharing their infrastructure. However, questions could be raised about the extend to which HetNet densification is of aid. Given that network planning is executed according to high traffic load volumes, BS underutilisation during low-traffic hours cannot be neglected. Similarly, the aggregated energy needs of multiple SBSs equals the ones of an energy hungry MBS, having thus a respectable share of the net energy consumption. In this context, a set of research opportunities have been identified. This thesis provides contribution toward the achievement of a greener and more cost efficient operation of HetNet deployments, where multiple stakeholders develop their activity and where energy support can have the form of various alternate schemes, including renewable energy (RE) sources. Depending on the network energy support, i.e., whether RE sources are used in the network or not, the main body of this thesis is divided in two research directions. The first part of the thesis uses the technology of switching off strategies in order to explore their efficiency in terms of both energy and costs in a HetNet. The HetNet is assumed to be a roaming-based cooperative activity of multiple MNOs that is powered exclusively by grid energy. A switching off and a cost allocation scheme are proposed, using as criteria the BS type, the BS load and the roaming cost for traffic offloading. The performance of the proposed schemes is evaluated with respect to energy efficiency, cost savings and fairness, using computer-based simulations. The second part of the thesis explores energy and cost management issues in energy harvesting (EH) HetNet deployments where EH-BSs use an EH system (EHS), an energy storage system (ESS) and the smart grid (SG) as energy procurement sources. The EH-HetNet is assumed a two-tier network deployment of EH-MBSs that are passively shared among an MNO set and EH-SBSs that are provided to MNOs by an infrastructure provider. Taking into consideration the infrastructure location and the variety of stakeholders involved in the network deployment, approaches of RE exchange (REE) are proposed as a cooperative RE sharing for the shared EH-MBSs, based on bankruptcy theory, and a non-cooperative, aggregator-assisted RE trading, based on double auctions, for the EH-SBSs. The performance of the proposed schemes is evaluated in terms of the hours of independence of the studied system from the SG, the fairness regulated by the provided solution and the economical payoffs extracted for the stakeholdersDurante los últimos años, se ha notado un aumento enorme del volumen de tráfico de datos, mientras que se espera un nuevo aumento en los próximos años. Como resultado, se han planteado preguntas sobre las necesidades de consumo de energía de las redes inalámbricas de telecomunicaciones, sus emisiones de dióxido de carbono y sus gastos operativos. Con el objetivo de satisfacer las altas demandas de tráfico con consumo de energía constante y con gastos incurridos constantes, además de utilizar soluciones basadas en la nube, los operadores de redes móviles (MNOs) han optado por mejorar su posición (i) desplegando redes heterogéneas (HetNets), que consisten en estaciones base de macro-células (MBSs) y estaciones base de células pequeñas (SBSs), y (ii) compartiendo su infraestructura. Sin embargo, podrían plantearse preguntas sobre hasta qué punto la densificación de una HetNet es de ayuda. Dado que la planificación de la red se ejecuta de acuerdo con los volúmenes de carga de tráfico más elevados, no se puede descuidar la subutilización de las estaciones base (BS) durante las horas de poco tráfico. De manera similar, las necesidades de energía agregadas de múltiples SBSs son iguales a las de una MBS que consume mucha energía, teniendo así una parte respetable del consumo neto de energía. En este contexto, se ha identificado un conjunto de oportunidades de investigación. Esta tesis contribuye al logro de una operación más ecológica y rentable de las implementaciones de HetNet, donde múltiples partes interesadas desarrollan su actividad y donde el apoyo energético puede tener la forma de varios esquemas alternativos, incluidas las fuentes de energía renovables (RE). Dependiendo del soporte de energía de red, es decir, si las fuentes de RE se usan en la red o no, el cuerpo principal de esta tesis se divide en dos direcciones de investigación. La primera parte de la tesis utiliza la tecnología de las estrategias de apagado con el objetivo de explorar su eficiencia en términos de energía y gastos en una HetNet. Se asume que la HetNet es una actividad cooperativa basada en la itinerancia de múltiples MNO que se alimenta exclusivamente de energía de la red. Se propone un esquema de desconexión y de asignación de costes, que utiliza como criterios el tipo de BS, la carga de BS y el coste de la itinerancia para la descarga de tráfico. El rendimiento de los esquemas propuestos se evalúa con respecto a la eficiencia energética, el ahorro de costes y la equidad, usando simulaciones en computadora. La segunda parte de la tesis explora los problemas de gestión de energía y de costes en las implementaciones de HetNet donde las estaciones base recolectan energía usando un sistema EH (EHS), un sistema de almacenamiento de energía (ESS) y la red eléctrica inteligente (SG) como sistemas de adquisición de energía. Se asume que el EH-HetNet es una implementación de redes de dos niveles donde los EH-MBSs se comparten pasivamente entre un conjunto de MNOs y EH-SBSs se proporcionan a los MNOs de un proveedor de infraestructura. Teniendo en cuenta la ubicación de la infraestructura y la variedad de partes interesadas e involucradas en el despliegue de la red, se proponen enfoques de intercambio de RE (REE) como un intercambio cooperativo de RE para los EH-MBS compartidos, basado en la teoría de bancarrota, y un no cooperativo comercio de RE para los EH-SBSs, que es asistido por un agregador y basado en las subastas dobles. El rendimiento de los esquemas propuestos se evalúa en términos de las horas de independencia del sistema estudiado con respecto al SG, la imparcialidad regulada por la solución proporcionada y los beneficios económicos extraídos para las interesadas

Energy and cost management in shared heterogeneous network deployments

Pla de Doctorat industrial de la Generalitat de CatalunyaDuring the recent years, a huge augmentation of the data traffic volume has been noticed, while a further steep increase is expected in the following years. As a result, questions have been raised over the years about the energy consumption needs of the wireless telecommunication networks, their carbon dioxide emissions and their operational expenses. Aiming at meeting the high traffic demands with flat energy consumption and flat incurred expenses, mobile network operators (MNOs) have opted to improve their position (i) by deploying heterogeneous networks (HetNets), which are consisted of macrocell base stations (MBSs) and small cell base stations (SBSs) and (ii) by sharing their infrastructure. However, questions could be raised about the extend to which HetNet densification is of aid. Given that network planning is executed according to high traffic load volumes, BS underutilisation during low-traffic hours cannot be neglected. Similarly, the aggregated energy needs of multiple SBSs equals the ones of an energy hungry MBS, having thus a respectable share of the net energy consumption. In this context, a set of research opportunities have been identified. This thesis provides contribution toward the achievement of a greener and more cost efficient operation of HetNet deployments, where multiple stakeholders develop their activity and where energy support can have the form of various alternate schemes, including renewable energy (RE) sources. Depending on the network energy support, i.e., whether RE sources are used in the network or not, the main body of this thesis is divided in two research directions. The first part of the thesis uses the technology of switching off strategies in order to explore their efficiency in terms of both energy and costs in a HetNet. The HetNet is assumed to be a roaming-based cooperative activity of multiple MNOs that is powered exclusively by grid energy. A switching off and a cost allocation scheme are proposed, using as criteria the BS type, the BS load and the roaming cost for traffic offloading. The performance of the proposed schemes is evaluated with respect to energy efficiency, cost savings and fairness, using computer-based simulations. The second part of the thesis explores energy and cost management issues in energy harvesting (EH) HetNet deployments where EH-BSs use an EH system (EHS), an energy storage system (ESS) and the smart grid (SG) as energy procurement sources. The EH-HetNet is assumed a two-tier network deployment of EH-MBSs that are passively shared among an MNO set and EH-SBSs that are provided to MNOs by an infrastructure provider. Taking into consideration the infrastructure location and the variety of stakeholders involved in the network deployment, approaches of RE exchange (REE) are proposed as a cooperative RE sharing for the shared EH-MBSs, based on bankruptcy theory, and a non-cooperative, aggregator-assisted RE trading, based on double auctions, for the EH-SBSs. The performance of the proposed schemes is evaluated in terms of the hours of independence of the studied system from the SG, the fairness regulated by the provided solution and the economical payoffs extracted for the stakeholdersDurante los últimos años, se ha notado un aumento enorme del volumen de tráfico de datos, mientras que se espera un nuevo aumento en los próximos años. Como resultado, se han planteado preguntas sobre las necesidades de consumo de energía de las redes inalámbricas de telecomunicaciones, sus emisiones de dióxido de carbono y sus gastos operativos. Con el objetivo de satisfacer las altas demandas de tráfico con consumo de energía constante y con gastos incurridos constantes, además de utilizar soluciones basadas en la nube, los operadores de redes móviles (MNOs) han optado por mejorar su posición (i) desplegando redes heterogéneas (HetNets), que consisten en estaciones base de macro-células (MBSs) y estaciones base de células pequeñas (SBSs), y (ii) compartiendo su infraestructura. Sin embargo, podrían plantearse preguntas sobre hasta qué punto la densificación de una HetNet es de ayuda. Dado que la planificación de la red se ejecuta de acuerdo con los volúmenes de carga de tráfico más elevados, no se puede descuidar la subutilización de las estaciones base (BS) durante las horas de poco tráfico. De manera similar, las necesidades de energía agregadas de múltiples SBSs son iguales a las de una MBS que consume mucha energía, teniendo así una parte respetable del consumo neto de energía. En este contexto, se ha identificado un conjunto de oportunidades de investigación. Esta tesis contribuye al logro de una operación más ecológica y rentable de las implementaciones de HetNet, donde múltiples partes interesadas desarrollan su actividad y donde el apoyo energético puede tener la forma de varios esquemas alternativos, incluidas las fuentes de energía renovables (RE). Dependiendo del soporte de energía de red, es decir, si las fuentes de RE se usan en la red o no, el cuerpo principal de esta tesis se divide en dos direcciones de investigación. La primera parte de la tesis utiliza la tecnología de las estrategias de apagado con el objetivo de explorar su eficiencia en términos de energía y gastos en una HetNet. Se asume que la HetNet es una actividad cooperativa basada en la itinerancia de múltiples MNO que se alimenta exclusivamente de energía de la red. Se propone un esquema de desconexión y de asignación de costes, que utiliza como criterios el tipo de BS, la carga de BS y el coste de la itinerancia para la descarga de tráfico. El rendimiento de los esquemas propuestos se evalúa con respecto a la eficiencia energética, el ahorro de costes y la equidad, usando simulaciones en computadora. La segunda parte de la tesis explora los problemas de gestión de energía y de costes en las implementaciones de HetNet donde las estaciones base recolectan energía usando un sistema EH (EHS), un sistema de almacenamiento de energía (ESS) y la red eléctrica inteligente (SG) como sistemas de adquisición de energía. Se asume que el EH-HetNet es una implementación de redes de dos niveles donde los EH-MBSs se comparten pasivamente entre un conjunto de MNOs y EH-SBSs se proporcionan a los MNOs de un proveedor de infraestructura. Teniendo en cuenta la ubicación de la infraestructura y la variedad de partes interesadas e involucradas en el despliegue de la red, se proponen enfoques de intercambio de RE (REE) como un intercambio cooperativo de RE para los EH-MBS compartidos, basado en la teoría de bancarrota, y un no cooperativo comercio de RE para los EH-SBSs, que es asistido por un agregador y basado en las subastas dobles. El rendimiento de los esquemas propuestos se evalúa en términos de las horas de independencia del sistema estudiado con respecto al SG, la imparcialidad regulada por la solución proporcionada y los beneficios económicos extraídos para las interesadas.Postprint (published version

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

NASA Tech Briefs, September 1990

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Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Marshall Space Flight Center Research and Technology Report 2018

Many of NASAs missions would not be possible if it were not for the investments made in research advancements and technology development efforts. The technologies developed at Marshall Space Flight Center contribute to NASAs strategic array of missions through technology development and accomplishments. The scientists, researchers, and technologists of Marshall Space Flight Center who are working these enabling technology efforts are facilitating NASAs ability to fulfill the ambitious goals of innovation, exploration, and discovery