Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Cost-based linear holding practice and collaborative air traffic flow management under trajectory based operations

The current air transportation system is reaching the capacity limit in many countries/regions across the world. It tends to be less efficient or even incapable sometimes to deal with the enormous air traffic demand that continues growing year by year. This has been evidenced by the record-breaking flight delays reported in various places in recent years, which, have resulted in notable economical loses. To mitigate this imbalance between demand and capacity, air traffic flow management (ATFM) is usually one of the most useful options. It regulates traffic flows according to air traffic control capacity while preserving safety and efficiency of flights. ATFM initiatives can be considered well in advance of the flight execution - more than one year earlier - based on air traffic forecasts and capacity plans, and continue in effect, with information updated, to eventually the day of operation. This long effective period will inevitably allow substantial collaboration among different stakeholders, including the ATFM authority, airspace users (AUs), air navigation service providers (ANSPs), airports, etc. Under the forthcoming paradigm of trajectory based operations (TBO), the flight 4-Dimensional trajectory has been anticipated to further enhance the connection between flight planning and execution phases, thus fostering such collaboration in ATFM. Moreover, under nowadays operations, ground holding is a typical measure undertaken in many widely-used ATFM programs. Even though holding on the ground, at the origin airport, has the advantage of fuel efficiency over the air holding, it turns out that its feature of low flexibility would, in some circumstances, affect the ATFM performance. Yet, with proper flight trajectory management, it is also possible to have delay airborne at no extra fuel cost than performing ground holding. This PhD thesis firstly focuses on this trajectory management, specifically on a cost-based linear holding practice. The linear holding is realized progressively along the planned trajectory through precise speed control which can be enabled by aircraft trajectory optimization techniques. Some typical short/mid haul flights are simulated for achieving the maximum airborne delay that can be yielded using same fuel consumption as initially scheduled. Based on this, its potential applicability is demonstrated. A network ATFM model is adapted from the well-studied Bertsimas Stock-Patterson (BSP) model, incorporating different types of delay (including the linear holding) to flexibly handle the traffic flow with a set of given (yet changeable) capacities. In order that the benefits of the model can be fully realized, AUs are required to participate in the decision-making process, submitting for instance the maximum linear holding bound per flight along the planned trajectory. Next, increased AUs' participation is expected for a proposed Collaborative ATFM framework, in which not only various delay initiatives are considered, but also alternative trajectories which allow flights to route out of the identified hotspot areas. A centralized linear programming optimization model then computes for the best trajectory selections and the optimal delay distributions across all concerned flights. Finally, ANSPs' involvement is additionally considered for the framework, through dynamic airspace reconfiguration, further enhancing the collaboration between ATFM stakeholders. As such, the traffic flow regulation and sector opening scheduling are bounded into an integrated optimization model, and thus are conducted in a synchronized way. Results indicate that the performance of demand and capacity balancing can be even improved if compared with the previous ATFM models presented in this PhD thesis.El sistema de transport aeri actual està arribant al seu límit de capacitat en molts països i regions del món. Una gestió del flux de trànsit aeri (ATFM) més adequada podria mitigar aquest desequilibri entre la demanda i la capacitat. La funció de l'ATFM és regular els fluxos de trànsit aeri segons la capacitat de control del trànsit aeri, i alhora assegurar que els vols siguin segurs i eficients. Les regulacions del sistema d'ATFM es poden aplicar molt abans de l'execució del vol més d'un any abans. Un cop aplicades, aquestes regulacions continuaran evolucionant, amb informació actualitzada, fins el dia de la seva execució. El llarg període entre la planificació del vol i la seva execució permetrà una important col·laboració entre els diferents membres implicats, inclosa l'autoritat de l'ATFM, els usuaris de l'espai aeri (AUs), els proveïdors de serveis de navegació aèria (ANSP), els aeroports, etc. En les operacions d'avui en dia l'espera a terra és una de les regulacions que més aplica el sistema d'ATFM per tal d'evitar congestions als aeroports o sectors de l'espai aeri. Tot i que esperar a terra, a l'aeroport d'origen, té l'avantatge de consumir menys combustible que esperar a l'aire a l'aeroport de destí, la seva poca flexibilitat podria afectar negativament al rendiment de l'ATFM en algunes circumstàncies. Tanmateix, amb una gestió adequada de la trajectòria de vol, també és possible efectuar cert retard a l'aire sense cap cost addicional de combustible respecte al que resultaria esperant a terra. Aquesta tesi doctoral s'enfoca en primer lloc en aquesta gestió de trajectòria de vol, específicament en una pràctica d'espera tenint en compte els costos per l'aerolínia. L'espera lineal s'efectua progressivament al llarg de la trajectòria planificada mitjançant un control precís de la velocitat. Les velocitats que generen l'espera desitjada durant el vol és calculen mitjançant tècniques d'optimització. Alguns vols típics de curt i mig abast es simulen per quantificar el màxim retard a l'aire que es podria generar utilitzant el mateix consum de combustible que el previst inicialment. Basant-se en els resultats obtinguts, s'explora la seva aplicabilitat potencial. Es desenvolupa un model de la xarxa d'ATFM basat en el model de Bertsima Stock-Patterson. Com a novetat, el model desenvolupat en aquesta tesi incorpora diferents tipus de retard (incloent-hi l'espera lineal) per gestionar de forma més flexible el flux de trànsit donat un conjunt de capacitats pre-definides. Per tal d'explotar al màxim els beneficis del model proposat en aquesta tesi, les autoritats regionals estan obligades a participar en el procés de presa de decisions, declarant, per exemple, la màxima espera lineal associada a cada vol al llarg de la trajectòria planejada. Tot seguit, s'inclou la participació dels AUs en un sistema d'ATFM col·laboratiu, en el qual no només es consideren diverses tipus de retard per balancejar la capacitat i la demanda, sinó també trajectòries alternatives que permeten que els vols evitin de forma òptima els sectors de l?espai aeri congestionats. Un model d'optimització centralitzat basat en programació lineal calcula les millors seleccions de trajectòria i les distribucions òptimes de retard en tots els vols afectats per la regulació. Es demostra que incloure trajectòries alternatives pot reduir notablement la quantitat de retards. Finalment, es considera també la participació de l'ANSP en el sistema d'ATFM, a través de la configuració dinàmica de l'espai aeri, millorant encara més la col·laboració entre els membres implicats en el sistema. Com a tal, la regulació del flux de trànsit i la programació d'obertura dels diferents sectors de l'espai aeri s'inclouen en un model integrat d'optimització i, per tant, es programen de forma sincronitzada. Els resultats suggereixen que el rendiment del balanc¸ de la demanda i la capacitat es pot millorar encara m´es amb aquest sistema ATFM col·laboratiu complert. El nou model de balanc¸ de demanda i capacitat millora encara ées els resultats, si es compara amb els altres models d’ATFM presentats també en aquesta tesi doctoral.El sistema de transporte aéreo actual está llegando a su límite de capacidad en muchos países y regiones del mundo. Como consecuencia, éste tiende a ser menos eficiente e incluso en ocasiones incapaz de afrontar la enorme demanda de tráfico aéreo que incluso hoy en día crece rápidamente. Este hecho se ha visto evidenciado por los enormes retrasos registrados en diferentes lugares los últimos años, lo cual ha comportado enormes pérdidas económicas para la sociedad. Una gestión del flujo del tráfico aéreo (ATFM) más adecuada podría mitigar este desequilibrio entre la demanda y la capacidad. La función del ATFM es regular los flujos de tráfico aéreo según la capacidad de control del tráfico aéreo, siempre asegurando que los vuelos sean seguros y eficientes. Las regulaciones del sistema de ATFM se pueden aplicar mucho antes de la ejecución del vuelo –más de un año antes– en función de las previsiones de tráfico aéreo y de la capacidad esperada. Una vez aplicadas, estas regulaciones continuarán evolucionando, con información actualizada, hasta el día de su ejecución. El largo periodo entre la planificación del vuelo y su ejecución permitirá una importante colaboración entre los diferentes miembros implicados, incluida la autoridad del ATFM, los usuarios del espacio aéreo (AUs), los proveedores de servicios de navegación aérea (ANSP), los aeropuertos, etc. En el marco del futuro paradigma de las operaciones basadas en trayectorias, la introducción de vuelos con control sobre la trayectoria en las 4 dimensiones espera mejorar aún más la conexión entre las fases de planificación del vuelo y su ejecución, fomentando así la colaboración en el proceso de toma de decisiones del sistema ATFM. En las operaciones de hoy en día la espera en tierra es una de las regulaciones que más se aplica en el sistema de ATFM con el fin de evitar congestiones en los aeropuertos o en los sectores del espacio aéreo. Aun teniendo en cuenta que esperar en tierra, en el aeropuerto de origen, tiene la ventaja de consumir menos combustible que esperar en el aire en el aeropuerto de destino, su poca flexibilidad podría afectar negativamente al rendimiento del ATFM en algunas circunstancias. Aun así, con una gestión adecuada de la trayectoria de vuelo, también es posible efectuar cierto retraso en el aire sin ningún coste adicional de combustible respecto a lo que resultaría esperando en tierra. Esta tesis doctoral se centra en primer lugar en esta gestión de la trayectoria de vuelo, específicamente en una práctica de espera lineal considerando los costes para la aerolínea. La espera lineal se efectúa progresivamente a lo largo de la trayectoria planificada mediante un control preciso de la velocidad. Las velocidades que generan la espera deseada durante el vuelo se calculan mediante técnicas de optimización. Algunos vuelos típicos de corto y medio alcance se simulan para cuantificar el máximo retraso en el aire que se podría generar utilizando el mismo consumo de combustible que el previsto inicialmente. Basándose en los resultados obtenidos, se investiga su potencial aplicabilidad, como por ejemplo mejorar la planificación de programas de flujo del espacio aéreo, y ayudar a neutralizar los retrasos no deseados adicionales debidos a la incertidumbre del sistema. Se desarrolla un modelo de la red de ATFM basado en el conocido modelo Bertsimas Stock-Patterson (BSP). Como novedad, el modelo desarrollado en esta tesis incorpora diferentes tipos de retraso (incluyendo la espera lineal) para gestionar de manera más flexible el flujo de tráfico dado un conjunto de capacidades predefinidas. Con el fin de explotar al máximo los beneficios del modelo propuesto en esta tesis, se asume que las aerolíneas participaran en el proceso de toma de decisiones, declarando, por ejemplo, la máxima espera lineal asociada a cada vuelo a lo largo de la trayectoria planeada. Este concepto se ilustra con un caso de estudio, donde se demuestra una reducción significativa de los retrasos, comparado con el modelo BSP. Seguidamente, se incluye la participación de las aerolíneas en un sistema de ATFM colaborativo, en el cual no tan sólo se consideran diferentes tipos de retrasos para balancear la capacidad y la demanda, sino también trayectorias alternativas que permiten que los vuelos eviten de forma óptima los sectores del espacio aéreo congestionados. Un modelo de optimización centralizado basado en programación lineal calcula las mejores selecciones de la trayectoria y las distribuciones óptimas de retraso en todos los vuelos afectado por la regulación. Se demuestra que incluir trayectorias alternativas puede reducir notablemente la cantidad de retrasos. Finalmente, se considera también la participación de los ANSP en el sistema de ATFM, a través de la configuración dinámica del espacio aéreo, mejorando aún más la colaboración entre los miembros implicados en el sistema. Como tales, la regulación del flujo de tráfico aéreo y la programación de apertura de los diferentes sectores del espacio aéreo se incluyen en un modelo integrado de optimización y, por lo tanto, se programan de manera sincronizada. El nuevo modelo de balance de demanda y capacidad mejora aún más los resultados, si se compara con los otros modelos ATFM presentados también en esta tesis doctoralPostprint (published version

Mission-Aware Spatio-Temporal Deep Learning Model for UAS Instantaneous Density Prediction

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experimental results demonstrate outstanding performance of the deep learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by more than 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A* routing algorithm, our model can still achieve 0.823 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction

A Corridor Level GIS-Based Decision Support Model to Evaluate Truck Diversion Strategies

Increased urbanization, population growth, and economic development within the U.S. have led to an increased demand for freight travel to meet the needs of individuals and businesses. Consequently, freight transportation has grown significantly over time and has expanded beyond the capacity of infrastructure, which has caused new challenges in many regions. To maintain quality of life and enhance public safety, more effort must be dedicated to investigating and planning in the area of traffic management and to assessing the impact of trucks on highway systems. Traffic diversion is an effective strategy to reduce the impact of incident-induced congestion, but alternative routes for truck traffic must be carefully selected based on a route\u27s restrictions on the size and weight of commercial vehicles, route\u27s operational characteristics, and safety considerations. This study presents a diversion decision methodology that integrates the network analyst tool package of the ArcGIS platform with regression analysis to determine optimal alternative routes for trucks under nonrecurrent delay conditions. When an incident occurs on a limited-access road, the diversion algorithm can be initiated. The algorithm is embedded with an incident clearance prediction model that estimates travel time on the current route based on a number of factors including incident severity; capacity reduction; number of lanes closed; type of incident; traffic characteristics; temporal characteristics; responders; and reporting, response, and clearance times. If travel time is expected to increase because of the event, a truck alternative route selection module is activated. This module evaluates available routes for diversion based on predefined criteria including roadway characteristics (number of lanes and lane width), heavy vehicle restrictions (vertical clearance, bridge efficiency ranking, bridge design load, and span limitations), traffic conditions (level of service and speed limit), and neighborhood impact (proximity to schools and hospitals and the intensity of commercial and residential development). If any available alternative routes reduce travel time, the trucks are provided with a diversion strategy. The proposed decision-making tool can assist transportation planners in making truck diversion decisions based on observed conditions. The results of a simulation and a feasibility analysis indicate that the tool can improve the safety and efficiency of the overall traffic network

Thermal-Aware Networked Many-Core Systems

Advancements in IC processing technology has led to the innovation and growth happening in the consumer electronics sector and the evolution of the IT infrastructure supporting this exponential growth. One of the most difficult obstacles to this growth is the removal of large amount of heatgenerated by the processing and communicating nodes on the system. The scaling down of technology and the increase in power density is posing a direct and consequential effect on the rise in temperature. This has resulted in the increase in cooling budgets, and affects both the life-time reliability and performance of the system. Hence, reducing on-chip temperatures has become a major design concern for modern microprocessors. This dissertation addresses the thermal challenges at different levels for both 2D planer and 3D stacked systems. It proposes a self-timed thermal monitoring strategy based on the liberal use of on-chip thermal sensors. This makes use of noise variation tolerant and leakage current based thermal sensing for monitoring purposes. In order to study thermal management issues from early design stages, accurate thermal modeling and analysis at design time is essential. In this regard, spatial temperature profile of the global Cu nanowire for on-chip interconnects has been analyzed. It presents a 3D thermal model of a multicore system in order to investigate the effects of hotspots and the placement of silicon die layers, on the thermal performance of a modern ip-chip package. For a 3D stacked system, the primary design goal is to maximise the performance within the given power and thermal envelopes. Hence, a thermally efficient routing strategy for 3D NoC-Bus hybrid architectures has been proposed to mitigate on-chip temperatures by herding most of the switching activity to the die which is closer to heat sink. Finally, an exploration of various thermal-aware placement approaches for both the 2D and 3D stacked systems has been presented. Various thermal models have been developed and thermal control metrics have been extracted. An efficient thermal-aware application mapping algorithm for a 2D NoC has been presented. It has been shown that the proposed mapping algorithm reduces the effective area reeling under high temperatures when compared to the state of the art.Siirretty Doriast

Load balancing using cell range expansion in LTE advanced heterogeneous networks

The use of heterogeneous networks is on the increase, fueled by consumer demand for more data. The main objective of heterogeneous networks is to increase capacity. They offer solutions for efficient use of spectrum, load balancing and improvement of cell edge coverage amongst others. However, these solutions have inherent challenges such as inter-cell interference and poor mobility management. In heterogeneous networks there is transmit power disparity between macro cell and pico cell tiers, which causes load imbalance between the tiers. Due to the conventional user-cell association strategy, whereby users associate to a base station with the strongest received signal strength, few users associate to small cells compared to macro cells. To counter the effects of transmit power disparity, cell range expansion is used instead of the conventional strategy. The focus of our work is on load balancing using cell range expansion (CRE) and network utility optimization techniques to ensure fair sharing of load in a macro and pico cell LTE Advanced heterogeneous network. The aim is to investigate how to use an adaptive cell range expansion bias to optimize Pico cell coverage for load balancing. Reviewed literature points out several approaches to solve the load balancing problem in heterogeneous networks, which include, cell range expansion and utility function optimization. Then, we use cell range expansion, and logarithmic utility functions to design a load balancing algorithm. In the algorithm, user and base station associations are optimized by adapting CRE bias to pico base station load status. A price update mechanism based on a suboptimal solution of a network utility optimization problem is used to adapt the CRE bias. The price is derived from the load status of each pico base station. The performance of the algorithm was evaluated by means of an LTE MATLAB toolbox. Simulations were conducted according to 3GPP and ITU guidelines for modelling heterogeneous networks and propagation environment respectively. Compared to a static CRE configuration, the algorithm achieved more fairness in load distribution. Further, it achieved a better trade-off between cell edge and cell centre user throughputs. [Please note: this thesis file has been deferred until December 2016

Embedded dynamic programming networks for networks-on-chip

PhD ThesisRelentless technology downscaling and recent technological advancements in three dimensional integrated circuit (3D-IC) provide a promising prospect to realize heterogeneous system-on-chip (SoC) and homogeneous chip multiprocessor (CMP) based on the networks-onchip (NoCs) paradigm with augmented scalability, modularity and performance. In many cases in such systems, scheduling and managing communication resources are the major design and implementation challenges instead of the computing resources. Past research efforts were mainly focused on complex design-time or simple heuristic run-time approaches to deal with the on-chip network resource management with only local or partial information about the network. This could yield poor communication resource utilizations and amortize the benefits of the emerging technologies and design methods. Thus, the provision for efficient run-time resource management in large-scale on-chip systems becomes critical. This thesis proposes a design methodology for a novel run-time resource management infrastructure that can be realized efficiently using a distributed architecture, which closely couples with the distributed NoC infrastructure. The proposed infrastructure exploits the global information and status of the network to optimize and manage the on-chip communication resources at run-time. There are four major contributions in this thesis. First, it presents a novel deadlock detection method that utilizes run-time transitive closure (TC) computation to discover the existence of deadlock-equivalence sets, which imply loops of requests in NoCs. This detection scheme, TC-network, guarantees the discovery of all true-deadlocks without false alarms in contrast to state-of-the-art approximation and heuristic approaches. Second, it investigates the advantages of implementing future on-chip systems using three dimensional (3D) integration and presents the design, fabrication and testing results of a TC-network implemented in a fully stacked three-layer 3D architecture using a through-silicon via (TSV) complementary metal-oxide semiconductor (CMOS) technology. Testing results demonstrate the effectiveness of such a TC-network for deadlock detection with minimal computational delay in a large-scale network. Third, it introduces an adaptive strategy to effectively diffuse heat throughout the three dimensional network-on-chip (3D-NoC) geometry. This strategy employs a dynamic programming technique to select and optimize the direction of data manoeuvre in NoC. It leads to a tool, which is based on the accurate HotSpot thermal model and SystemC cycle accurate model, to simulate the thermal system and evaluate the proposed approach. Fourth, it presents a new dynamic programming-based run-time thermal management (DPRTM) system, including reactive and proactive schemes, to effectively diffuse heat throughout NoC-based CMPs by routing packets through the coolest paths, when the temperature does not exceed chip’s thermal limit. When the thermal limit is exceeded, throttling is employed to mitigate heat in the chip and DPRTM changes its course to avoid throttled paths and to minimize the impact of throttling on chip performance. This thesis enables a new avenue to explore a novel run-time resource management infrastructure for NoCs, in which new methodologies and concepts are proposed to enhance the on-chip networks for future large-scale 3D integration.Iraqi Ministry of Higher Education and Scientific Research (MOHESR)

Optimizing Urban Distribution Routes for Perishable Foods Considering Carbon Emission Reduction

The increasing demand for urban distribution increases the number of transportation vehicles which intensifies the congestion of urban traffic and leads to a lot of carbon emissions. This paper focuses on carbon emission reduction in urban distribution, taking perishable foods as the object. It carries out optimization analysis of urban distribution routes to explore the impact of low carbon policy on urban distribution routes planning. On the base of analysis of the cost components and corresponding constraints of urban distribution, two optimization models of urban distribution route with and without carbon emissions cost are constructed, and fuel quantity related to cost and carbon emissions in the model is calculated based on traffic speed, vehicle fuel quantity and passable time period of distribution. Then an improved algorithm which combines genetic algorithm and tabu search algorithm is designed to solve models. Moreover, an analysis of the influence of carbon tax price is also carried out. It is concluded that in the process of urban distribution based on the actual network information, the path optimization considering the low carbon factor can effectively reduce the distribution process of CO2, and reduce the total cost of the enterprise and society, thus achieving greater social benefits at a lower cost. In addition, the government can encourage low-carbon distribution by rationally adjusting the price of carbon tax to achieve a higher social benefit