Efficient Low Cost Range-Based Localization Algorithm for Ad-hoc Wireless Sensors Networks

Revised version submitted to Ad Hoc NetworksBuilding an efficient node localization system in wireless sensor networks is facing several challenges. For example, calculating the square root consumes computational resources and utilizing flooding techniques to broadcast nodes location wastes bandwidth and energy. Reducing computational complexity and communication overhead is essential in order to reduce power consumption, extend the life time of the battery operated nodes, and improve the performance of the limited computational resources of these sensor nodes. In this paper, we revise the mathematical model,the analysis and the simulation experiments of the Trigonometric based Ad-hoc Localiza-tion System (TALS), a range-based localization system presented previously. Furthermore, the study is extended, and a new technique to optimize the system is proposed. An analysis and an extensive simulation for the optimized TALS (OTALS) is presented showing its cost, accuracy, and efficiency, thus deducing the impact of its parameters on performance. Hence, the contribution of this work can be summarized as follows: 1) Proposing and employing a novel modified Manhattan distance norm in the TALS localization process. 2) Analyzing and simulating of OTALS showing its computational cost and accuracy and comparing them with other related work. 3) Studying the impacts of different parameters like anchor density, node density, noisy measurements, transmission range, and non-convex network areas. 4) Extending our previous joint work, TALS, to consider base anchors to be located in positions other than the origin and analyzing this work to illustrate the possibility of selecting a wrong quadrant at the first iteration and how this problem is overcome. Through mathematical analysis and intensive simulation, OTALS proved to be iterative , distributed, and computationally simple. It presented superior performance compared to other localization techniques

Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

Indoor Localisation of Scooters from Ubiquitous Cost-Effective Sensors: Combining Wi-Fi, Smartphone and Wheel Encoders

Indoor localisation of people and objects has been a focus of research studies for several decades because of its great advantage to several applications. Accuracy has always been a challenge because of the uncertainty of the employed sensors. Several technologies have been proposed and researched, however, accuracy still represents an issue. Today, several sensor technologies can be found in indoor environments, some of which are economical and powerful, such as Wi-Fi. Meanwhile, Smartphones are typically present indoors because of the people that carry them along, while moving about within rooms and buildings. Furthermore, vehicles such as mobility scooters can also be present indoor to support people with mobility impairments, which may be equipped with low-cost sensors, such as wheel encoders. This thesis investigates the localisation of mobility scooters operating indoor. This represents a specific topic as most of today's indoor localisation systems are for pedestrians. Furthermore, accurate indoor localisation of those scooters is challenging because of the type of motion and specific behaviour. The thesis focuses on improving localisation accuracy for mobility scooters and on the use of already available indoor sensors. It proposes a combined use of Wi-Fi, Smartphone IMU and wheel encoders, which represents a cost-effective energy-efficient solution. A method has been devised and a system has been developed, which has been experimented on different environment settings. The outcome of the experiments are presented and carefully analysed in the thesis. The outcome of several trials demonstrates the potential of the proposed solutions in reducing positional errors significantly when compared to the state-of-the-art in the same area. The proposed combination demonstrated an error range of 0.35m - 1.35m, which can be acceptable in several applications, such as some related to assisted living. 3 As the proposed system capitalizes on the use of ubiquitous technologies, it opens up to a potential quick take up from the market, therefore being of great benefit for the target audience

Exploiting and optimizing mobility in wireless sensor networks

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2016.Nos últimos anos, as chamadas Redes de Sensores Sem Fio (RSSF) tem sido usadas numa grande variedade de aplicações, tais como monitoramento (p.ex. poluição do ar e água, vulcões, estruturas, sinais vitais), detecção de eventos (p.ex. vigilância, incêndios, inundações, terremotos), e monitoramento de alvos (p.ex. segurança, animais silvestres, etc). RSSF são constituídas tipicamente por dezenas, as vez centenas de pequenos dispositivos alimentados por baterias, capazes de realizar medições e de transmitir tais dados para uma estação base através de um canal sem fio. Uma das formas mais promissoras para melhorar o desempenho das RSSF em termos de conectividade, tempo de vida da rede, e latência na transmissão dos dados é através de técnicas que exploram a mobilidade em um ou mais componentes da rede. A mobilidade na RSSF pode ser tanto controlável como aleatória, sendo que em ambos os casos os protocolos devem ser devidamente ajustados para responder adequadamente aos cenários em questão. No caso de mobilidade aleatória, os nodos sensores podem ser capazes de aprender os padrões de mobilidade dos nodos para poderem otimizar a operação da rede. Por outro lado, sendo os padrões de mobilidade conhecidos, é possível fazer escolhas para melhor sintonizar o desempenho da rede de acordo com os critérios estabelecidos pelo projetista. A presente tese de doutorado procura explorar as vantagens associadas com o uso de mobilidade controlada em RSSF. É possível definir mobilidade controlada como sendo a capacidade de se alterar propositalmente o posicionamento de determinados nodos da RSSF. Com isso se torna possível explorar, controlar, ou mesmo otimizar a trajetória e a velocidade dos nodos móveis da RSSF a fim de maximizar o desempenho da rede como um todo. Definitivamente, o uso de nodos que permitam o ajuste de trajetória e velocidade oferece um alto grau de flexibilidade para se explorar aspectos de mobilidade e projetar protocolos de coleta de dados otimizados. Ao se utilizar mobilidade controlada, algumas das operações realizadas pela RSSF podem ser significativamente melhoradas, de modo a tornar possível ajustar o padrão de desempenho da rede de acordo com os níveis desejados. Por exemplo, o processo de descoberta de nodos pode ser melhorado e mesmo simplificado com o controle dos nodos móveis, de modo que ele possa se aproximar dos nodos estáticos em instantes pré-determinados. Da mesma forma, o processo de coleta de dados pode ser otimizado se os nodos móveis se moverem mais rapidamente nos locais onde eles precisam coletar menos dados. Entretanto, diversos desafios aparecem neste tipo de contexto. Por exemplo, como se deve escalonar a chegada do(s) nodo(s) móvel(is) e como se deve controlar e otimizar a movimentação em termos de velocidade sem afetar a qualidade de serviço. Nesse contexto, o segundo capítulo da teseapresenta um esquema de estimação de localização de nodos estáticos espalhados ao longo de uma área predeterminada, utilizando-se para tanto de um nodo móvel com mobilidade controlada. Tal informação de posicionamento é muito importante para a organização de uma RSSF. Com isso é possível definir a sua cobertura, os protocolos de roteamento, a forma de coleta de dados e também auxiliar em aplicações de rastreamento e detecção de eventos. O esquema proposto consiste de uma técnica de localização para estimar a posição dos nodos sensor de forma eficiente, usando apenas um nodo móvel e técnicas geométricas simples. O esquema não requer hardware adicional ou mesmo comunicação entre nodos sensores, evitando assim maiores gastos de baterias. A estimativa de posição obtida é precisa e capaz de tolerar um certo grau de obstáculos. Os resultados obtidos ao longo da tese demostram que a precisão de localização pode ser bem ajustada selecionando corretamente a velocidade, o intervalo de transmissão de beacons e o padrão de varredura da área de interesse pelo nodo móvel.Já o terceiro capítulo apresentada uma técnica de otimização para fins de controle da mobilidade do nodo coletor de dados (MDC). Com isso torna-se possível desenvolver um esquema inteligente de coleta de dados na RSSF. Em primeiro lugar, são destacados os fatores que afetam o processo de coleta de dados usando um MDC. Em seguida é apresentado um algoritmo adaptativo que permite ajustar os parâmetros de controlenecessários para modificar os parâmetros de movimentação do MDC. Estes parâmetros permitem que a velocidade do MDC seja ajustada em tempo de execução para otimizaro processo de coleta de dados. Com isso o MDC pode se adaptar às diferentes taxas de coletas de dados impostas por um conjunto de nodos heterogêneos. O esquema proposto apresenta vantagens significativas para RSSF de grande escala e também heterogêneas (onde os sensores possuem taxas de amostragem variáveis). Os resultados obtidos mostram um aumento significativo na taxa de coleta de dados e a redução no tempo total de deslocamento e no número de voltas que o MDC gasta para coletar os dados dos sensores.Por fim, o capítulo 4 propõe um mecanismo de controle de acesso (MAC) adaptado ao cenário de mobilidade, que se ajusta automaticamente de acordo com o padrão de mobilidade do MDC. O mesmo foca umaredução no consumo de energia e na melhoria da coleta de dados, suportando mobilidade e evitando colisões de mensagens. Este protocolo destina-se a aplicações de coleta de dados nas quais os nós sensores têm de reportar periodicamente a um nó receptor ou estação base. O conceito básico é baseado em acesso múltiplo de divisão de tempo, onde a duração do padrão de sono-vigília é definida de acordo com o padrão de mobilidade do MDC. O esquema proposto é capaz de atender tanto mobilidade aleatória quanto controlada por parte do MDC, desde que as RSSF sejam organizadas em cluster. Uma análise de simulação detalhada é realizada para avaliar seu desempenho em cenários mais gerais e sob diferentes condições operacionais. Os resultados obtidos mostram que o nosso esquema proposto supera amplamente oprotocolo 802.15.4 com sinais (beacons) em termos de eficiência energética, tempo de deslocamento do MDC e taxas de coleta de dados.Abstract : One of the promising techniques for improving the performance of a wireless sensor network (WSN), in terms of connectivity, network lifetime, and data latency, is to introduce and exploit mobility in some of the network components. Mobility in WSN can be either uncontrollable or controllable and needs to be optimized in both cases. In the case of uncontrolled mobility, sensor nodes can learn the mobility patterns of mobile nodes to improve network performance. On the other hand, if the mobility is controllable in terms of trajectory and speed, it can be best tuned to enhance the performance of the network to the desired level. This thesis considers the problem of exploiting and optimizing mobility in wireless sensor networks in order to increase the performance and efficiency of the network.First, a location estimation scheme is discussed for static nodes within a given sensor area using a controlled mobile node. Position information of static nodes is very important in WSN. It helps in effective coverage, routing, data collection, target tracking, and event detection. The scheme discusses a localization technique for efficient position estimation of the sensor nodes using a mobile node and simple geometric techniques. The scheme does not require extra hardware or data communication and does not make the ordinary sensor nodes to spend energy on any interaction with neighboring nodes. The position estimation is accurate and efficient enough to tolerate obstacles and only requires broadcasting of beacon messages by the mobile node. Obtained simulation results show that the localization accuracy can be well adjusted by properly selecting the speed, beacon interval, and scan pattern of the mobile node.Second, an optimization technique for controlled mobility of a mobile data collector is presented in order to develop a smart data collection scheme in WSN. In this case, first, the factors affecting the data collection process using an MDC is highlighted. Then, an adaptive algorithm and control parameters that the MDC uses for autonomously controlling its motion is presented. These parameters allow the speed of the MDC to be adjusted at run time in order to adaptively improve the data collection process. Built-in intelligence helps our system adapting to the changing requirements of data collection. Our scheme shows significant advantages for sparsely deployed, large scale sensor networks and heterogeneous networks (where sensors have variable sampling rates). The simulation results show a significant increase in data collection rate and reduction in the overall traverse time and number of laps that the MDC spends for data gathering.Finally, a mobility aware adaptive medium access control (MAC) is proposed for WSNs which automatically adjusts according to the mobility pattern of the MDC, focusing on reducing energy consumption and improving data collection, while supporting mobility and collision avoidance. This protocol is targeted to data collection applications (e.g. monitoring and surveillance), in which sensor nodes have to periodically report to a sink node. The core concept is based on adaptive time division multiple access, where the sleep-wake duration is defined according to the MDC mobility pattern. The proposed scheme is described for random, predictable, and controlled arrival of MDC in cluster-based WSNs. A detailed simulation analysis is carried out to evaluate its performance in more general scenarios and under different operating conditions. The obtained results show that our scheme largely outperforms the commonly used 802.15.4 beacon-enabled and other fixed duty-cycling schemes in terms of energy efficiency, MDC traverse time, and data collection rates

Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu

State and Parameter Estimation of Vehicle-Trailer Systems

Vehicle-trailer systems have different unstable modes that should be considered in their stability control, including trailer snaking, jack-knifing, and roll-over. In general, vehicle control systems require vehicle parameters and states, including geometric parameters, mass, tire forces, and side slip angles which some are not constant or can be measured economically. In a vehicle-trailer system, the trailer states and parameters such as articulation angle, trailer geometric parameters, trailer mass, trailer tire forces, and yaw rate need to be measured or identified/estimated, in addition to the unknown vehicle states/parameters. The trailer states and parameters can be measured by sensors such as Inertial Measurement Unit (IMU), wheel torque sensors, and force measurement units. However, most of these sensors are not commercially viable to be used in a vehicle or trailer due to significant extra costs. Estimation algorithms are the other tools to identify the parameters and states of the system without imposing extra costs. Accurate state and parameter estimators are needed for the development and implementation of a stability control system for a vehicle-trailer system. The main purpose of this research is to design real-time state and parameter estimation algorithms for vehicle-trailer systems. Correspondingly, a comprehensive overview of different model-based and non-model-based techniques/algorithms used for estimating vehicle-trailer states and parameters are provided. The vehicle-trailer system equations of motion are then presented and based on the presented vehicle-trailer model, the possibility of the trailer states and parameters estimation are investigated for different possible vehicle-trailer on-board sensor settings. Two different methods are proposed to estimate trailer mass for arbitrary vehicle-trailer configurations: model-based and Machine Learning (ML). The stability of the model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a deep neural network is designed to estimate trailer mass. The inputs of the ML-based method are selected based on the vehicle-trailer model and are normalized by the vehicle mass, tire sizes, and geometry so that retraining of the network is not needed for different towing vehicles. The simulation and experimental results demonstrate that the trailer mass can be estimated with with acceptable computational costs. In this thesis, ultrasonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four Ultra sonic sensors. As the second and third approaches, kinematic and dynamic models of the vehicle-trailer system are used to develop least-square and Kalman filter based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Additionally, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry. The designed lateral tire forces and hitch-force estimation algorithms can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations studies in CarSim along with experimental tests are used to validate the presented method to confirm the accuracy of the developed observer. Moreover, using the vehicle-trailer lateral dynamics along with the LuGre tire model, an estimation system for the lateral velocity of a vehicle-trailer is proposed. It is shown that the proposed estimation is robust to the road conditions. An affine quadratic stability approach is used to analyze the stability of the proposed estimation. The test results confirm the accuracy of the developed estimation and convergence of the vehicle-trailer lateral velocity estimation to the actual value. Model-based and ML-based estimators are developed for estimating road angles for arbitrary vehicle-trailer configurations. The estimators are shown to be independent from road friction conditions. The model-based method employs unknown input observers on the vehicle-trailer roll and pitch dynamic models. In the proposed ML-based estimator, a recurrent neural network with Long-short-term-memory gates is designed to estimate the road angles. The inputs to the ML-based method are normalized by the vehicle wheel-base, mass, and CG height to make it applicable to any towing vehicle with the need of retraining. The simulation and experimental results justify the convergence of the road angle estimation error