Estratégias de encaminhamento para recolha oportunística de informação em redes móveis de internet das coisas

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work focuses on two main topics to enhance the forwarding decision: i) forwarding strategies that make use of location-aware and social-based to perform neighborhood selection, ii) and packet selection mechanisms to provide Quality of Service (QoS). The neighborhood selection is performed through multiple metrics, resulting in three forwarding strategies: (1) Gateway Location Awareness (GLA), a location-aware ranking classification making use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns; (2) Aging Social-Aware Ranking (ASAR) that exploits the social behaviours of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles; (3) and to merge both location and social aforementioned algorithms, a hybrid approach emerges, thus generating a more intelligent mechanism. Allied to the forwarding criteria, two packet selection mechanisms are proposed to address distinct network functionalities, namely: Distributed Packet Selection, that focuses primarily on data type prioritization and secondly, on packet network lifetime; and Equalized Packet Selection, which uses network metrics to calculate a storage packet ranking. To do so, the packet number of hops, the packet type and packet network lifetime are used. In order to perform the evaluation of the proposed mechanisms, both real and emulation experiments were performed. For each forwarding strategy, it is evaluated the influence of several parameters in the network's performance, as well as comparatively evaluate the strategies in different scenarios. Experiment results, obtained with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network, are used to evaluate the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. Later, these strategies' viability is also validated in a real scenario. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of moving networks as a smart city network infrastructure. To evaluate the proposed packet selection mechanisms, a First In First Out packet selection technique is used as ground rule, thus contrasting with the more objective driven proposed techniques. The results show that the proposed mechanisms are capable of provide distinct network functionalities, from prioritizing a packet type to enhancing the network's performance.A elevada mobilidade em cenários veiculares urbanos origina descontinuidades de comunicação entre veículos, um fator altamente importante quando se desenha uma estratégia de encaminhamento para redes veiculares. Mecanismos de store, carry and forward (guardar, carregar e entregar) possibilitam a recolha de dados de sensores em aplicações da Internet das coisas, contribuindo para plataformas de cidades inteligentes. Este trabalho é focado em dois tópicos principais de forma a melhorar a decisão de encaminhamento: i) estratégias de encaminhamento que fazem uso de métricas sociais e de localização para efetuar a seleção de vizinhos, ii) e mecanismos de seleção de pacotes que qualificam a rede com qualidade de serviço. A seleção de vizinhos é feita através de múltiplas métricas, resultando em três estratégias de encaminhamento: Gateway Location Awareness (GLA), uma classificação baseada em localização que faz uso de velocidade, ângulo de direção e distância até uma gateway, para selecionar os veículos com maior probabilidade de entregar a informação num menor período temporal, distinguindo os veículos através dos seus padrões de movimento. Aging Social-Aware Ranking (ASAR) explora os comportamentos sociais de cada veículo, onde é atribuída uma classificação aos veículos com base num histórico de contactos, diferenciando veículos com um alto número de contactos de outros com menos. Por fim, por forma a tirar partido das distintas características de cada uma das destas estratégias, é proposta uma abordagem híbrida, Hybrid between GLA and ASAR (HYBRID). Aliado ao critério de encaminhamento, são propostos dois mecanismos de seleção de pacotes que focam distintas funcionalidades na rede, sendo estes: Distributed Packet Selection, que foca em primeiro lugar na prioritização de determinados tipos de pacotes e em segundo lugar, no tempo de vida que resta ao pacote na rede; e Equalized Packet Selection, que usa métricas da rede para calcular a classificação de cada pacote em memória. Para tal, é usado o numero de saltos do pacote, o tipo de dados do pacote e o tempo de vida que resta ao pacote na rede. De forma a avaliar os mecanismos propostos, foram realizadas experiências em emulador e em cenário real. Para cada estratégia de encaminhamento, e avaliada a influência de vários parâmetros de configuração no desempenho da rede. Para além disso, é feita uma avaliação comparativa entre as várias estratégias em diferentes cenários. Resultados experimentais, obtidos usando traços reais de mobilidade e conetividade de uma rede veicular urbana, são utilizados para avaliar a performance dos esquemas GLA, ASAR e HYRID. Posteriormente, a viabilidade destas estratégias é também validada em cenário real. Os resultados obtidos mostram que estas estratégias são um bom tradeoff para maximizar a taxa de entrega de dados e minimizar a sobrecarga de dados na rede. Para avaliar os mecanismos de seleção de pacotes, um simples mecanismo First In First Out é utilizado como base, contrapondo com as técnicas propostas mais orientadas a objectivos concretos. Os resultados obtidos mostram que os mecanismos propostos são capazes de proporcionar à rede diferentes funcionalidades, desde prioritização de determinado tipos de dados a melhoramentos no desempenho da rede.Agradeço à Fundação Portuguesa para a Ciência e Tecnologia pelo suporte financeiro através de fundos nacionais e quando aplicável cofi nanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 pelo projecto MobiWise através do programa Operacional Competitividade e Internacionalização (COMPETE 2020) do Portugal 2020 (POCI-01-0145-FEDER-016426).Mestrado em Engenharia Eletrónica e Telecomunicaçõe

A Survey of Software-Defined Networks-on-Chip: Motivations, Challenges and Opportunities

Current computing platforms encourage the integration of thousands of processing cores, and their interconnections, into a single chip. Mobile smartphones, IoT, embedded devices, desktops, and data centers use Many-Core Systems-on-Chip (SoCs) to exploit their compute power and parallelism to meet the dynamic workload requirements. Networks-on-Chip (NoCs) lead to scalable connectivity for diverse applications with distinct traffic patterns and data dependencies. However, when the system executes various applications in traditional NoCs—optimized and fixed at synthesis time—the interconnection nonconformity with the different applications’ requirements generates limitations in the performance. In the literature, NoC designs embraced the Software-Defined Networking (SDN) strategy to evolve into an adaptable interconnection solution for future chips. However, the works surveyed implement a partial Software-Defined Network-on-Chip (SDNoC) approach, leaving aside the SDN layered architecture that brings interoperability in conventional networking. This paper explores the SDNoC literature and classifies it regarding the desired SDN features that each work presents. Then, we described the challenges and opportunities detected from the literature survey. Moreover, we explain the motivation for an SDNoC approach, and we expose both SDN and SDNoC concepts and architectures. We observe that works in the literature employed an uncomplete layered SDNoC approach. This fact creates various fertile areas in the SDNoC architecture where researchers may contribute to Many-Core SoCs designs.Las plataformas informáticas actuales fomentan la integración de miles de núcleos de procesamiento y sus interconexiones, en un solo chip. Los smartphones móviles, el IoT, los dispositivos embebidos, los ordenadores de sobremesa y los centros de datos utilizan sistemas en chip (SoC) de muchos núcleos para explotar su potencia de cálculo y paralelismo para satisfacer los requisitos de las cargas de trabajo dinámicas. Las redes en chip (NoC) conducen a una conectividad escalable para diversas aplicaciones con distintos patrones de tráfico y dependencias de datos. Sin embargo, cuando el sistema ejecuta varias aplicaciones en las NoC tradicionales -optimizadas y fijadas en el momento de síntesis, la disconformidad de la interconexión con los requisitos de las distintas aplicaciones genera limitaciones en el rendimiento. En la literatura, los diseños de NoC adoptaron la estrategia de redes definidas por software (SDN) para evolucionar hacia una solución de interconexión adaptable para los futuros chips. Sin embargo, los trabajos estudiados implementan un enfoque parcial de red definida por software en el chip (SDNoC) de SDN, dejando de lado la arquitectura en capas de SDN que aporta interoperabilidad en la red convencional. Este artículo explora la literatura sobre SDNoC y la clasifica en función de las características SDN que presenta cada trabajo. A continuación, describimos los retos y oportunidades detectados a partir del estudio de la literatura. Además, explicamos la motivación para un enfoque SDNoC, y exponemos los conceptos y arquitecturas de SDN y SDNoC. Observamos que los trabajos en la literatura emplean un enfoque SDNoC por capas no completo. Este hecho crea varias áreas fértiles en la arquitectura SDNoC en las que los investigadores pueden contribuir a los diseños de SoCs de muchos núcleos

Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

A scalable dynamic parking allocation framework

International audienceCities suffer from high traffic c ongestion of which one of the main causes is the unorganized pursuit for available parking. Apart from traffic congestion, the blind search for a parking slot causes financial and environmental losses. We consider a general parking allocation scenario in which the GPS data of a set of vehicles, such as the current locations and destinations of the vehicles, are available to a central agency which will guide the vehicles toward a designated parking lot, instead of the entered destination. In its natural form, the parking allocation problem is dynamic, i.e., its input is continuously updated. Therefore, standard static allocation and assignment rules do not apply in this case. In this paper, we propose a framework capable of tackling these real-time updates. From a methodological point of view, solving the dynamic version of the parking allocation problem represents a quantum leap compared with solving the static version. We achieve this goal by solving a sequence of 0-1 programming models over the planning horizon, and we develop several parking policies. The proposed policies are empirically compared on real data gathered from three European cities: Belgrade, Luxembourg, and Lyon. The results show that our framework is scalable and can improve the quality of the allocation, in particular when parking capacities are low

실시간 동적 계획법 및 강화학습 기반의 공공자전거 시스템의 동적 재배치 전략

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 고승영.The public bicycle sharing system is one of the modes of transportation that can help to relieve several urban problems, such as traffic congestion and air pollution. Because users can pick up and return bicycles anytime and anywhere a station is located, pickup or return failure can occur due to the spatiotemporal imbalances in demand. To prevent system failures, the operator should establish an appropriate repositioning strategy. As the operator makes a decision based on the predicted demand information, the accuracy of forecasting demand is an essential factor. Due to the stochastic nature of demand, however, the occurrence of prediction errors is inevitable. This study develops a stochastic dynamic model that minimizes unmet demand for rebalancing public bicycle sharing systems, taking into account the stochastic demand and the dynamic characteristics of the system. Since the repositioning mechanism corresponds to the sequential decision-making problem, this study applies the Markov decision process to the problem. To solve the Markov decision process, a dynamic programming method, which decomposes complex problems into simple subproblems to derive an exact solution. However, as a set of states and actions of the Markov decision process become more extensive, the computational complexity increases and it is intractable to derive solutions. An approximate dynamic programming method is introduced to derive an approximate solution. Further, a reinforcement learning model is applied to obtain a feasible solution in a large-scale public bicycle network. It is assumed that the predicted demand is derived from the random forest, which is a kind of machine learning technique, and that the observed demand occurred along the Poisson distribution whose mean is the predicted demand to simulate the uncertainty of the future demand. Total unmet demand is used as a key performance indicator in this study. In this study, a repositioning strategy that quickly responds to the prediction error, which means the difference between the observed demand and the predicted demand, is developed and the effectiveness is assessed. Strategies developed in previous studies or applied in the field are also modeled and compared with the results to verify the effectiveness of the strategy. Besides, the effects of various safety buffers and safety stock are examined and appropriate strategies are suggested for each situation. As a result of the analysis, the repositioning effect by the developed strategy was improved compared to the benchmark strategies. In particular, the effect of a strategy focusing on stations with high prediction errors is similar to the effect of a strategy considering all stations, but the computation time can be further reduced. Through this study, the utilization and reliability of the public bicycle system can be improved through the efficient operation without expanding the infrastructure.공공자전거 시스템은 교통혼잡과 대기오염 등 여러 도시문제를 완화할 수 있는 교통수단이다. 대여소가 위치한 곳이면 언제 어디서든 이용자가 자전거를 이용할 수 있는 시스템의 특성상 수요의 시공간적 불균형으로 인해 대여 실패 또는 반납 실패가 발생한다. 시스템 실패를 예방하기 위해 운영자는 적절한 재배치 전략을 수립해야 한다. 운영자는 예측 수요 정보를 전제로 의사결정을 하므로 수요예측의 정확성이 중요한 요소이나, 수요의 불확실성으로 인해 예측 오차의 발생이 불가피하다. 본 연구의 목적은 공공자전거 수요의 불확실성과 시스템의 동적 특성을 고려하여 불만족 수요를 최소화하는 재배치 모형을 개발하는 것이다. 공공자전거 재배치 메커니즘은 순차적 의사결정 문제에 해당하므로, 본 연구에서는 순차적 의사결정 문제를 모형화할 수 있는 마르코프 결정 과정을 적용한다. 마르코프 결정 과정을 풀기 위해 복잡한 문제를 간단한 부문제로 분해하여 정확해를 도출하는 동적 계획법을 이용한다. 하지만 마르코프 결정 과정의 상태 집합과 결정 집합의 크기가 커지면 계산 복잡도가 증가하므로, 동적 계획법을 이용한 정확해를 도출할 수 없다. 이를 해결하기 위해 근사적 동적 계획법을 도입하여 근사해를 도출하며, 대규모 공공자전거 네트워크에서 가능해를 얻기 위해 강화학습 모형을 적용한다. 장래 공공자전거 이용수요의 불확실성을 모사하기 위해, 기계학습 기법의 일종인 random forest로 예측 수요를 도출하고, 예측 수요를 평균으로 하는 포아송 분포를 따라 수요를 확률적으로 발생시켰다. 본 연구에서는 관측 수요와 예측 수요 간의 차이인 예측오차에 빠르게 대응하는 재배치 전략을 개발하고 효과를 평가한다. 개발된 전략의 우수성을 검증하기 위해, 기존 연구의 재배치 전략 및 현실에서 적용되는 전략을 모형화하고 결과를 비교한다. 또한, 재고량의 안전 구간 및 안전재고량에 관한 민감도 분석을 수행하여 함의점을 제시한다. 개발된 전략의 효과를 분석한 결과, 기존 연구의 전략 및 현실에서 적용되는 전략보다 개선된 성능을 보이며, 특히 예측오차가 큰 대여소를 탐색하는 전략이 전체 대여소를 탐색하는 전략과 재배치 효과가 유사하면서도 계산시간을 절감할 수 있는 것으로 나타났다. 공공자전거 인프라를 확대하지 않고도 운영의 효율화를 통해 공공자전거 시스템의 이용률 및 신뢰성을 제고할 수 있고, 공공자전거 재배치에 관한 정책적 함의점을 제시한다는 점에서 본 연구의 의의가 있다.Chapter 1. Introduction １ 1.1 Research Background and Purposes １ 1.2 Research Scope and Procedure ７ Chapter 2. Literature Review １０ 2.1 Vehicle Routing Problems １０ 2.2 Bicycle Repositioning Problem １２ 2.3 Markov Decision Processes ２３ 2.4 Implications and Contributions ２６ Chapter 3. Model Formulation ２８ 3.1 Problem Definition ２８ 3.2 Markov Decision Processes ３４ 3.3 Demand Forecasting ４０ 3.4 Key Performance Indicator (KPI) ４５ Chapter 4. Solution Algorithms ４７ 4.1 Exact Solution Algorithm ４７ 4.2 Approximate Dynamic Programming ５０ 4.3 Reinforcement Learning Method ５２ Chapter 5. Numerical Example ５５ 5.1 Data Overview ５５ 5.2 Experimental Design ６１ 5.3 Algorithm Performance ６６ 5.4 Sensitivity Analysis ７４ 5.5 Large-scale Cases ７６ Chapter 6. Conclusions ８２ 6.1 Conclusions ８２ 6.2 Future Research ８３ References ８６ 초 록 ９２Docto

Future benefits and applications of intelligent on-board processing to VSAT services

The trends and roles of VSAT services in the year 2010 time frame are examined based on an overall network and service model for that period. An estimate of the VSAT traffic is then made and the service and general network requirements are identified. In order to accommodate these traffic needs, four satellite VSAT architectures based on the use of fixed or scanning multibeam antennas in conjunction with IF switching or onboard regeneration and baseband processing are suggested. The performance of each of these architectures is assessed and the key enabling technologies are identified

Rede multi-tecnologia para recolha de dados ambientais através de comunicações oportunistas

mestrado em Engenharia Eletrónica e TelecomunicaçõesO conceito de Smart City surge da combinação do paradigma de Internet of Things (IoT) sobre contextos urbanos aliado à exploração de soluções de Tecnologias de Informação e Comunicação (TIC). O típico cenário de Smart City tem de lidar com desafios, tais como as elevadas quantidades de sensores e geradores de dados, dos quais alguns são colocados em dispositivos de grande mobilidade, visando a recolha e geração de todo o tipo de informações e levando ao aumento do número de dispositivos comunicantes. Esta dissertação foca o desenvolvimento e implementação de uma plataforma heterogénea de sonorização ambiental com o objectivo de servir de infraestrutura para aplicações no âmbito das Smart Cities. Esta pretende tirar proveito da utilização de múltiplas tecnologias de comunicação, nomeadamente tecnologias de longo e curto alcance. Para al em disto, visto que a plataforma visa ambientes urbanos, esta tira proveito de uma rede oportunista e tolerante a atrasos, Delay Tolerant Network (DTN), através de entidades m oveis que circulam pela cidade, nomeadamente bicicletas. Assim sendo, esta dissertação propõe: (1) o desenho e desenvolvimento da rede e dos seus constituintes; (2) uma extensão a um protocolo de controlo de acesso ao meio, Medium Access Control (MAC), para a tecnologia LoRa com o objectivo de o dotar compatível para ambientes de gateways múltiplas; (3) novas estratégias de encaminhamento para a rede tolerante a atrasos, tendo em consideração a topologia e as características apresentadas por esta. As avaliações realizadas permitiram concluir que o protocolo MAC para LoRa em ambientes de gateways múltiplas proposto contribui para um aumento da escalabilidade da rede, bem como para uma melhoria do seu desempenho. Relativamente às estratégias de encaminhamento propostas para a DTN, os testes realizados permitiram avaliar o impacto que cada estratégia tem sobre o comportamento da rede, nomeadamente a taxa de entrega dos pacotes de dados, a sobrecarga da rede, o número de pacotes transmitidos, entre outros. Com estes resultados foi possível perceber as in- suficiências que as funcionalidades propostas têm sobre a solução geral, e identificar as caraterísticas necessárias de uma solução escalável para a recolha de dados massivos num ambiente de IoT.The Smart City concept is the combination of the Internet of Things (IoT) paradigm under an urban context with the exploitation of Information and Communication Technologies (ICT) solutions. The typical Smart City scenario has to deal with an extensive amount of sensors and data generators, some of them placed in high mobile devices, deployed to collect and generate all type of information which will increase the number of communicating machines. This dissertation focuses on the development and implementation of a heterogeneous environmental sensing platform to serve as an infrastructure for Smart City applications. It aims to take advantage of the use of multiple communication technologies, namely long and short range. Being within an urban environment, the platform bene ts from an opportunistic and Delay Tolerant Network (DTN) through mobile entities that travel over the city, such as bicycles. Therefore, this dissertation proposes: (1) the design and development of the network and its elements; (2) an extension to a LoRa Medium Access Control (MAC) protocol in order to endow it with capabilities to operate in multi-gateway environments; and lastly, (3) new forwarding strategies for the opportunistic network that takes into consideration the network topology. The performed evaluations showed that the proposed multi-gateway LoRa MAC protocol contributes to increase the LoRa network scalability, as well as its performance. The performed tests to the proposed DTN forwarding strategies evaluate the impact of each strategy on the network behavior, namely the delivery ratio, network overhead, number of transmitted packets, among others. As a result, it is possible to perceive which are the in- uences introduced by the proposed functionalities on the overall solution, and identify the characteristics of a scalable solution to collect massive data in an IoT environment

DISPATCHING AND RELOCATION OF EMERGENCY VEHICLES ON FREEWAYS: THEORIES AND APPLICATIONS

Resource allocation decisions are made to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future decisions can be anticipated with scenarios, previous models follow an assumption that events over a time interval are independent. This dissertation follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This dissertation is a pioneer in relaxing the structural assumptions of independency during the assignment of emergency vehicles. When an emergency is detected and a request arrives, an appropriate emergency vehicle is immediately dispatched. We provide tools for quantifying impacts based on fundamentals of incident occurrences through identification, prediction, and interpretation of secondary incidents. A proposed online dispatching model minimizes the cost of moving the next emergency unit, while making the response as close to optimal as possible. Using the look-ahead concept, the online model flexibly re-computes the solution, basing future decisions on present requests. We introduce various online dispatching strategies with visualization of the algorithms, and provide insights on their differences in behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. After having served a designated request, the available and/or remaining vehicles are relocated to a new base for the next emergency. System costs will be excessive if delay regarding dispatching decisions is ignored when relocating response units. This dissertation presents an integrated method with a principle of beginning with a location phase to manage initial incidents and progressing through a dispatching phase to manage the stochastic occurrence of next incidents. Previous studies used the frequency of independent incidents and ignored scenarios in which two incidents occurred within proximal regions and intervals. The proposed analytical model relaxes the structural assumptions of Poisson process (independent increments) and incorporates evolution of primary and secondary incident probabilities over time. The mathematical model overcomes several limiting assumptions of the previous models, such as no waiting-time, returning rule to original depot, and fixed depot. The temporal locations flexible with look-ahead are compared with current practice that locates units in depots based on Poisson theory. A linearization of the formulation is presented and an efficient heuristic algorithm is implemented to deal with a large-scale problem in real-time