In present road traffic system, drone-network based traffic monitoring using the Internet of Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not collect complete data, cover all areas, provide quick medical services, or take vehicle follow-ups in case of an incident. Drone-based system helps to derive important information (such as commuter's behavior, traffic patterns, vehicle follow-ups) and sends this information to centralized or distributed authorities for making traffic diversions or necessary decisions as per laws. The present approaches fail to meet the requirements such as (i) collision free, (ii) drone navigation, and (iii) less computational and communicational overheads. This work has considered the collision-free drone-based movement strategies for road traffic monitoring using Software Defined Networking (SDN). The SDN controllable drone network results in lesser overhead over drones and provide efficient drone-device management. In simulation, two case studies are simulated using JaamSim simulator. Results show that the zones-based strategy covers a large area in few hours and consume 5 kWs to 25 kWs energy for 150 drones (Case study 1). Zone-less based strategies (case study-2) show that the energy consumption lies between 5 kWs to 18 kWs for 150 drones. Further, the use of SDN-based drones controller reduces the overhead over drone-network and increases the area coverage with a minimum of 1.2% and maximum of 2.6%. Simulation (using AnyLogic simulator) shows the 3D view of successful implementation of collision free strategies.

The early observation and elimination of non-recurring incidents is a crucial task in trafficmanagement. The performance of the conventional incident detection methods (trafficcameras and other sensory technologies) is limited and there are still challenges inobtaining an accurate picture of the traffic conditions in real time. During the last decade,the technical development of mobile platforms and the growing online connectivity made itpossible to obtain traffic information from social media and applications based on spatialcrowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receiveinformation about a more comprehensive number of incidents and can monitor areaswhich are not covered by the conventional incident detection systems. The crowdsourcedtraffic data can provide supplementary information for incidents already reported throughother sources and it can contribute to earlier detection of incidents, which can lead tofaster response and clearance time. Furthermore, spatial crowdsourcing can help to detectincident types, which are not collected systematically yet (e.g. potholes, traffic light faults,missing road signs). However, before exploiting crowdsourced traffic data in trafficmanagement, numerous challenges need to be resolved, such as verification of the incidentreports, predicting the severity of the crowdsourced incidents and integration with trafficdata obtained from other sources.During this thesis, the possibilities and challenges of utilizing spatial crowdsourcingtechnologies to detect non-recurring incidents were examined in form of a case study.Traffic incident alerts obtained from Waze, a navigation application using the concept ofcrowdsourcing, were analyzed and compared with officially verified incident reports inStockholm. The thesis provides insight into the spatial and temporal characteristics of theWaze data. Moreover, a method to identify related Waze alerts and to determine matchingincident reports from different sources is presented. The results showed that the number ofreported incidents in Waze is 4,5 times higher than the number of registered incidents bythe Swedish authorities. Furthermore, 27,5 % of the incidents could have been detectedfaster by using the traffic alerts from Waze. In addition, the severity of Waze alerts isexamined depending on the attributes of the alerts.

The early observation and elimination of non-recurring incidents is a crucial task in traffic management. The performance of the conventional incident detection methods (traffic cameras and other sensory technologies) is limited and there are still challenges in obtaining an accurate picture of the traffic conditions in real time. During the last decade, the technical development of mobile platforms and the growing online connectivity made it possible to obtain traffic information from social media and applications based on spatial crowdsourcing. Utilizing the benefits of crowdsourcing, traffic authorities can receive information about a more comprehensive number of incidents and can monitor areas which are not covered by the conventional incident detection systems. The crowdsourced traffic data can provide supplementary information for incidents already reported through other sources and it can contribute to earlier detection of incidents, which can lead to faster response and clearance time. Furthermore, spatial crowdsourcing can help to detect incident types, which are not collected systematically yet (e.g. potholes, traffic light faults, missing road signs). However, before exploiting crowdsourced traffic data in traffic management, numerous challenges need to be resolved, such as verification of the incident reports, predicting the severity of the crowdsourced incidents and integration with traffic data obtained from other sources. During this thesis, the possibilities and challenges of utilizing spatial crowdsourcing technologies to detect non-recurring incidents were examined in form of a case study. Traffic incident alerts obtained from Waze, a navigation application using the concept of crowdsourcing, were analyzed and compared with officially verified incident reports in Stockholm. The thesis provides insight into the spatial and temporal characteristics of the Waze data. Moreover, a method to identify related Waze alerts and to determine matching incident reports from different sources is presented. The results showed that the number of reported incidents in Waze is 4,5 times higher than the number of registered incidents by the Swedish authorities. Furthermore, 27,5 % of the incidents could have been detected faster by using the traffic alerts from Waze. In addition, the severity of Waze alerts is examined depending on the attributes of the alerts.

In present road traffic system, drone-network based traffic monitoring using the Internet of Vehicles (IoVs) is a promising solution. However, camera-based traffic monitoring does not collect complete data, cover all areas, provide quick medical services, or take vehicle follow-ups in case of an incident. Drone-based system helps to derive important information (such as commuter's behavior, traffic patterns, vehicle follow-ups) and sends this information to centralized or distributed authorities for making traffic diversions or necessary decisions as per laws. The present approaches fail to meet the requirements such as (i) collision free, (ii) drone navigation, and (iii) less computational and communicational overheads. This work has considered the collision-free drone-based movement strategies for road traffic monitoring using Software Defined Networking (SDN). The SDN controllable drone network results in lesser overhead over drones and provide efficient drone-device management. In simulation, two case studies are simulated using JaamSim simulator. Results show that the zones-based strategy covers a large area in few hours and consume 5 kWs to 25 kWs energy for 150 drones (Case study 1). Zone-less based strategies (case study-2) show that the energy consumption lies between 5 kWs to 18 kWs for 150 drones. Further, the use of SDN-based drones controller reduces the overhead over drone-network and increases the area coverage with a minimum of 1.2% and maximum of 2.6%. Simulation (using AnyLogic simulator) shows the 3D view of successful implementation of collision free strategies.

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This thesis presents a novel self-powered infrastructural traffic monitoring approach that estimates traffic information by combining three detection techniques. The traffic information can be obtained from the presented approach includes vehicle counts, speed estimation and vehicle classification based on size. Two categories of sensors are used including IR Lidar and IR camera. With the two sensors, three detection techniques are used: Time of Flight (ToF) based, vision based and Laser spot flow based. Each technique outputs observations about vehicle location at different time step. By fusing the three observations in the framework of Kalman filter, vehicle location is estimated, based on which other concerned traffic information including vehicle counts, speed and class is obtained. In this process, high reliability is achieved by combing the strength of each techniques. To achieve self-powering, a dynamic power management strategy is developed to reduce system total energy cost and optimize power supply in traffic monitoring based on traffic pattern recognition. The power manager attempts to adjust the power supply by reconfiguring system setup according to its estimation about current traffic condition. A system prototype has been built and multiple field experiments and simulations were conducted to demonstrate traffic monitoring accuracy and power reduction efficacy. / Master of Science

We describe CoSMo, a Cognitively Inspired Service and Model Architecture for situational awareness and monitoring of vehicular traffic in urban transportation systems using a network of wireless sensors. The system architecture combines (i) a cognitively inspired internal representation for analyzing and answering queries concerning the observed system and (ii) a service oriented architecture that facilitates interaction among individual modules, of the internal representation, the observed system and the user. The cognitively inspired model architecture allows one to effectively respond to deductive as well as inductive queries by combining simulation based dynamic models with traditional relational databases. On the other hand the service oriented design of interaction allows one to build flexible, extensible and scalable systems that can be deployed in practical settings. To illustrate our concepts and the novel features of our architecture, we have recently completed a prototype implementation of CoSMo. The prototype illustrates advantages of our approach over other traditional approaches for designing scalable software for situational awareness in large complex systems. The basic architecture and its prototype implementation are generic and can be applied for monitoring other complex systems. CoSMo's architecture has a number of features that distinguish cognitive systems. This includes: dynamic internal models of the observed system, inductive and deductive learning and reasoning, perception, memory and adaptation. This thesis describes the service oriented model and the associated prototype implementation. Two important contributions of this thesis include the following: The Generic Service Architecture - CoSMo's service architecture is generic and can be applied to many other application domains without much change in underlying infrastructure. Integration of emerging web technologies - Use of Web Services, UPnP, UDDI and many other emerging technologies have taken CoSMo beyond a prototype implementation and towards a real production system. / Master of Science

Thesis (PhD (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006. / This thesis addresses the problem of bulding a video-based traffic monitoring system. We employ clustering, trackiing and three-dimensional reconstruction of moving objects over a long image sequence. We present an algorithms that robustly recovers the motion and reconstructs three-dimensional shapes from a sequence of video images, Magaia et al [91]. The problem ...

Software-Defined Networking (SDN) objetiva aliviar as limitações impostas por redes IP tradicionais dissociando tarefas de rede executadas em cada dispositivo em planos específicos. Esta abordagem oferece vários benefícios, tais como a possibilidade de uso de protocolos de comunicação padrão, funções de rede centralizadas, e elementos de rede mais específicos e modulares, tais como controladores de rede. Apesar destes benefícios, ainda há uma falta de apoio adequado para a realização de tarefas relacionadas à classificação de tráfego, pois (i) as características de fluxo nativas disponíveis no protocolo OpenFlow, tais como contadores de bytes e pacotes, não oferecem informação suficiente para distinguir de forma precisa fluxos específicos; (ii) existe uma falta de suporte para determinar qual é o conjunto ótimo de características de fluxo para caracterizar um dado perfil de tráfego; (iii) existe uma necessidade de estratégias flexíveis para compor diferentes mecanismos relacionados à detecção, classificação e mitigação de anomalias de rede usando abstrações de software; (iv) existe uma necessidade de monitoramento de tráfego em tempo real usando técnicas leves e de baixo custo; (v) não existe um framework capaz de gerenciar detecção, classificação e mitigação de anomalias de uma forma coordenada considerando todas as demandas acima. Adicionalmente, é sabido que mecanismos de detecção e classificação de anomalias de tráfego precisam ser flexíveis e fáceis de administrar, a fim de detectar o crescente espectro de anomalias. Detecção e classificação são tarefas difíceis por causa de várias razões, incluindo a necessidade de obter uma visão precisa e abrangente da rede, a capacidade de detectar a ocorrência de novos tipos de ataque, e a necessidade de lidar com erros de classificação. Nesta dissertação, argumentamos que SDN oferece ambientes propícios para a concepção e implementação de esquemas mais robustos e extensíveis para detecção e classificação de anomalias. Diferentemente de outras abordagens na literatura relacionada, que abordam individualmente detecção ou classificação ou mitigação de anomalias, apresentamos um framework para o gerenciamento e orquestração dessas tarefas em conjunto. O framework proposto é denominado ATLANTIC e combina o uso de técnicas com baixo custo computacional para monitorar tráfego e técnicas mais computacionalmente intensivas, porém precisas, para classificar os fluxos de tráfego. Como resultado, ATLANTIC é um framework flexível capaz de categorizar anomalias de tráfego utilizando informações coletadas da rede para lidar com cada perfil de tráfego de um modo específico, como por exemplo, bloqueando fluxos maliciosos. / Software-Defined Networking (SDN) aims to alleviate the limitations imposed by traditional IP networks by decoupling network tasks performed on each device in particular planes. This approach offers several benefits, such as standard communication protocols, centralized network functions, and specific network elements, such as controller devices. Despite these benefits, there is still a lack of adequate support for performing tasks related to traffic classification, because (i) the native flow features available in OpenFlow, such as packet and byte counts, do not convey sufficient information to accurately distinguish between some types of flows; (ii) there is a lack of support to determine what is the optimal set of flow features to characterize different types of traffic profiles; (iii) there is a need for a flexible way of composing different mechanisms to detect, classify and mitigate network anomalies using software abstractions; (iv) there is a need of online traffic monitoring using lightweight/low-cost techniques; (v) there is no framework capable of managing anomaly detection, classification and mitigation in a coordinated manner and considering all these demands. Additionally, it is well-known that anomaly traffic detection and classification mechanisms need to be flexible and easy to manage in order to detect the ever growing spectrum of anomalies. Detection and classification are difficult tasks because of several reasons, including the need to obtain an accurate and comprehensive view of the network, the ability to detect the occurrence of new attack types, and the need to deal with misclassification. In this dissertation, we argue that Software-Defined Networking (SDN) form propitious environments for the design and implementation of more robust and extensible anomaly classification schemes. Different from other approaches from the literature, which individually tackle either anomaly detection or classification or mitigation, we present a management framework to perform these tasks jointly. Our proposed framework is called ATLANTIC and it combines the use of lightweight techniques for traffic monitoring and heavyweight, but accurate, techniques to classify traffic flows. As a result, ATLANTIC is a flexible framework capable of categorizing traffic anomalies and using the information collected to handle each traffic profile in a specific manner, e.g., blocking malicious flows.

This thesis presents a framework for motor vehicle detection-tracking surveillance systems. Given an optimized object detection template, the feasibility and effectiveness of the methodology is considered for vehicle counting applications, implementing both a filtering operation of false detection, based on the speed variability in each segment of traffic state, and an occlusion handling technique which considers the unusual affine transformation of tracking subspace, as well as its highly fluctuating averaged acceleration data. The result presents the overall performance considering the trade-off relationship between true detection rate and false detection rate. The filtering operation achieved significant success in removing the majority of non-vehicle elements that do not move like a vehicle. The occlusion handling technique employed also improved the systems performance, contributing counts that would otherwise be lost. For all video samples tested, the proposed framework obtained high correct count (>93% correct counting rate) while simultaneously minimizing the false count rate. For future research, the author recommends the use of more sophisticated filters for specific sets of conditions as well as the implementation of discriminative classifier for detecting different occlusion cases.