2,401 research outputs found

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    The Development of Microdosimetric Instrumentation for Quality Assurance in Heavy Ion Therapy, Boron Neutron Capture Therapy and Fast Neutron Therapy

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    This thesis presents research for the development of new microdosimetric instrumentation for use with solid-state microdosimeters in order to improve their portability for radioprotection purposes and for QA in various hadron therapy modalities. Monte Carlo simulation applications are developed and benchmarked, pertaining to the context of the relevant therapies considered. The simulation and experimental findings provide optimisation recommendations relating to microdosimeter performance and possible radioprotection risks by activated materials. The first part of this thesis is continuing research into the development of novel Silicon-on-Insulator (SOI) microdosimeters in the application of hadron therapy QA. This relates specifically to the optimisation of current microdosimeters, development of Monte Carlo applications for experimental validation, assessment of radioprotection risks during experiments and advanced Monte Carlo modelling of various accelerator beamlines. Geant4 and MCNP6 Monte Carlo codes are used extensively in this thesis, with rigorous benchmarking completed in the context of experimental verification, and evaluation of the similarities and differences when simulating relevant hadron therapy facilities. The second part of this thesis focuses on the development of a novel wireless microdosimetry system - the Radiodosimeter, to improve the operation efficiency and minimise any radioprotection risks. The successful implementation of the wireless Radiodosimeter is considered as an important milestone in the development of a microdosimetry system that can be operated by an end-user with no prior knowledge

    Improving the Performance of OLSR in Wireless Networks using Reinforcement Learning Algorithms

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    The Optimized Link State Routing Protocol is a popular proactive routing protocol used in wireless mesh networks. However, like many routing protocols, OLSR can suffer from inefficiencies and suboptimal performance in certain network conditions. To address these issues, researchers have proposed using reinforcement learning algorithms to improve the routing decisions made by OLSR. This paper explores the use of three RL algorithms - Q-Learning, SARSA, and DQN - to improve the performance of OLSR. Each algorithm is described in detail, and their application to OLSR is explained. In particular, the network is represented as a Markov decision process, where each node is a state, and each link between nodes is an action. The reward for taking an action is determined by the quality of the link, and the goal is to maximize the cumulative reward over a sequence of actions. Q-Learning is a simple and effective algorithm that estimates the value of each possible action in a given state. SARSA is a similar algorithm that takes into account the current policy when estimating the value of each action. DQN uses a neural network to approximate the Q-values of each action in a given state, providing more accurate estimates in complex network environments. Overall, all three RL algorithms can be used to improve the routing decisions made by OLSR. This paper provides a comprehensive overview of the application of RL algorithms to OLSR and highlights the potential benefits of using these algorithms to improve the performance of wireless networks

    Redes de sensores para la predicción solar a corto plazo en el marco de las microgrids y smartcities

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    En los últimos años, la potencia fotovoltaica instalada global ha crecido notablemente, llegando a superar el 20\% de la demanda energética en varios países. Esto se debe en parte a la reducción de costes de esta tecnología y la política de promover el uso de energías renovables. La producción de la energía fotovoltaica depende directamente de los niveles de radiación solar incidente sobre los paneles, que se trata de un recurso externo y variable. La irradiancia solar fluctúa principalmente por dos factores, pero la mayor variabilidad está asociada a la presencia de nubes, y estas variaciones tienen una duración que va desde unos pocos segundos hasta varios minutos. Debido al funcionamiento del mercado eléctrico y a la nula inercia en la producción energética de estos sistemas, los productores fotovoltaicos necesitan de predicciones precisas en diferentes horizontes temporales con el fin de maximizar la energía ofertada en el mercado, incrementando de este modo la integración de la misma. Por otra parte, también necesitan datos en tiempo real para una gestión más óptima del sistema fotovoltaico. Las predicciones a corto plazo se emplean para el sistema de control y balance de la producción energética, y a medio plazo para la programación y venta de energía en el mercado eléctrico, sin embargo, los sistemas actuales de predicción son escasos y caros para ser contemplados en sistemas de media y pequeña escala. Numerosos estudios han intentado cubrir la necesidad de predicción a corto plazo estimando espacio-temporalmente el campo de irradiancia con cámaras de cielo completo e imágenes de satélite, sin embargo, estos métodos están limitados por la problemática de la conversión de imagen a irradiancia. Investigadores influyentes en este área creen que las redes de sensores de irradiancia pueden jugar un papel fundamental en este contexto, ofreciendo en tiempo real varias medidas espaciales y con la alta resolución temporal necesaria. La información espacio-temporal capturada por la red permitiría estimar el campo de irradiancia y analizar su evolución, capturando incluso los eventos más rápidos. Las tecnologías inalámbricas han evolucionado en el marco de las ciudades inteligentes y el internet de las cosas, apareciendo tecnologías que se adecuan a diferentes escenarios. El interés mostrado en estos sistemas ha producido un abaratamiento de los módulos de comunicaciones inalámbricas, gracias a la economía de escala. Las redes de sensores podrían beneficiarse de estas tecnologías inalámbricas, ofreciendo a su vez un ahorro en costes del despliegue respecto a su equivalente cableado y una mayor flexibilidad para integrar nuevos nodos en la red. Por ello, esta tesis se pretende estudiar el potencial de estas redes inalámbricas como fuente de información crítica para la gestión a corto plazo de sistemas fotovoltaicos, y la explotación de los datos de la misma, implementando y desarrollando algoritmos con estos datos con fines de predicción de la producción y para la operación óptima de estos sistemas.In recent years, global installed photovoltaic power has grown significantly, exceeding 20% of energy demand in several countries. This is partly due to the cost reduction of this technology and the policy of promoting the use of renewable energies. Photovoltaic energy production depends directly on the levels of solar radiation incident on the panels, which is an external and variable resource. Solar irradiance fluctuates mainly due to two factors, but the greatest variability is associated with the presence of clouds, and these variations range in duration from a few seconds to several minutes. Due to the functioning of the electricity market and the lack of inertia in the energy production of these systems, PV producers need accurate forecasts at different time horizons in order to maximize the energy offered in the market, thus increasing the integration of the same. On the other hand, they also need real-time data for more optimal PV system management. Short-term forecasts are used for the energy production control and balancing system, and medium-term forecasts are used for scheduling and selling energy in the electricity market, however, current forecasting systems are scarce and expensive to be contemplated in medium and small-scale systems. Numerous studies have attempted to address the need for short-term forecasting by estimating the spatio-temporal irradiance field with full-sky cameras and satellite imagery, however, these methods are limited by the problems of image-to-irradiance conversion. Influential researchers in this area believe that irradiance sensor networks can play a key role in this context, providing various spatial measurements in real time and with the necessary high temporal resolution. The spatio-temporal information captured by the network would allow estimating the irradiance field and analyzing its evolution, capturing even the fastest events. Wireless technologies have evolved within the framework of smart cities and the internet of things, with the emergence of technologies that are suitable for different scenarios. The interest shown in these systems has led to a reduction in the cost of wireless communications modules, thanks to economies of scale. Sensor networks could benefit from these wireless technologies, offering savings in deployment costs compared to their wired equivalent and greater flexibility to integrate new nodes in the network. Thus, this thesis aims to study the potential of these wireless networks as a source of critical information for the short-term management of photovoltaic systems, and the exploitation of the data from it, implementing and developing algorithms with this data for production prediction purposes and for the optimal operation of these systems

    Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework

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    The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation

    Intégration des méthodes formelles dans le développement des RCSFs

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    In this thesis, we have relied on formal techniques in order to first evaluate WSN protocols and then to propose solutions that meet the requirements of these networks. The thesis contributes to the modelling, analysis, design and evaluation of WSN protocols. In this context, the thesis begins with a survey on WSN and formal verification techniques. Focusing on the MAC layer, the thesis reviews proposed MAC protocols for WSN as well as their design challenges. The dissertation then proceeds to outline the contributions of this work. As a first proposal, we develop a stochastic generic model of the 802.11 MAC protocol for an arbitrary network topology and then perform probabilistic evaluation of the protocol using statistical model checking. Considering an alternative power source to operate WSN, energy harvesting, we move to the second proposal where a protocol designed for EH-WSN is modelled and various performance parameters are evaluated. Finally, the thesis explores mobility in WSN and proposes a new MAC protocol, named "Mobility and Energy Harvesting aware Medium Access Control (MEH-MAC)" protocol for dynamic sensor networks powered by ambient energy. The protocol is modelled and verified under several features

    Model-Predictive Control in Communication Networks

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    This dissertation consists of 8 papers, separated into 3 groups. The first 3 papers show, how model-predictive control can be applied to queueing networks and contain a detailed proof of throughput optimality. Additionally, numerous network examples are discussed, and a connection between the stability properties of assembly queues and random walks on quotient spaces is established. The next two papers develop algorithms, with which robust forecasts of delay can be obtained in queueing networks. To that end, a notion of robustness is proposed, and the network control policy is designed to meet this goal. For the last 3 papers, focus is shifted towards Age-of-Information. Two main contributions are the derivation of the distribution of the Age-of-Information values in networks with clocked working cycles and an algorithm for the exact numerical evaluation of the Age-of-Information state-space in a similar set-up

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ
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