Enhancing Energy Efficiency in Connected Vehicles Via Access to Traffic Signal Information

This dissertation expounds on algorithms that can deterministically or proba-bilistically predict the future Signal Phase and Timing (SPAT) of a traffic signal by relying on real-time information from numerous vehicles and traffic infrastructure, historical data, and the computational power of a back-end computing cluster. When made available on an open server, predictive information about traffic signals’ states can be extremely valuable in enabling new fuel efficiency and safety functionalities in connected vehicles: Predictive Cruise Control (PCC) can use the predicted timing plan to calculate globally optimal velocity trajectories that reduce idling time at red signals and therefore improve fuel efficiency and reduce emissions. Advanced engine management strategies can shut down the engine in anticipation of a long idling interval at red. Intersection collision avoidance is another functionality that can benefit from the prediction. We start by exploring a globally optimal velocity planning algorithm through the use of Dynamic Programming (DP), and provide to it three levels of traffic signal information - none, real-time only, and full-future information. The no-information case represents the average driver today, and is expected to provide an energy efficiency minimum or baseline. The full-information case represents a driver with full and exact knowledge of the future red and green times of all the traffic signals along their route, and is expected to provide an energy efficiency maximum. We propose a probabilistic method that seeks to optimize fuel efficiency when only real-time only information is available with the goal of obtaining fuel efficiency as close to the full-future knowledge example as possible. We used Monte-Carlo simulations to evaluate whether the fuel efficiency gains found were merely the result of lucky case studies or whether they were statistically significant; we found in related case studies that up to 16% gains in fuel economy were possible. While these results were promising, the delivery of relevant and accurate future traffic signal phase and timing information remained an unsolved problem. The next step we took was towards building The next step we took was towards building traffic signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. signal prediction models. We took several prescient techniques from the data mining and machine learning fields, and adapted them to our purposes in the exploration of massive amounts of data recorded from traffic Management Centers (TMCs). This manuscript evaluates Transition Probability Modeling, Decision Tree, Multi-Linear Regression, and Neural Network machine learning methods for use in the prediction of traffic Signal Phase and Timing (SPaT) information. Finally, we evaluated the influence of providing SPaT data to vehicles. To that end, we investigated both smartphone and in-vehicle proof-of-concepts. An in-vehicle velocity recommendation application has been tested in two cities: San Jose, California and San Francisco, California. The two test locations used two different data sources: data directly from a TMC, and data crowdsourced from public transit bus routes, respectively. A total of 14 test drivers were used to evaluate the effectiveness of the algorithm. In San Jose, the algorithm was found to produce a 8.4% improvement in fuel economy. In San Francisco, traffic conditions were not conducive to testing as the driver was unable to significantly vary his speed to follow the recommendation algorithm, and a negligible difference in fuel economy was observed. However, it did provide an opportunity to evaluate the quality of data coming from the crowdsourced data algorithms. Predicted phase timing com-pared to camera-recorded ground truth data indicated an RMS difference (error) in prediction of approximately 4.1 seconds

Eco-Driving Systems for Connected Automated Vehicles: Multi-Objective Trajectory Optimization

This study aims to leverage advances in connected automated vehicle (CAV) technology to design an eco-driving and platooning system that can improve the fuel and operational efficiency of vehicles during freeway driving. Following a two-stage control logic, the proposed algorithm optimizes CAVs’ trajectories with three objectives: travel time minimization, fuel consumption minimization, and traffic safety improvement. The first stage, designed for CAV trajectory planning, is carried out with two optimization models. The second stage, for real-time control purposes, is developed to ensure the operational safety of CAVs. Based on extensive numerical simulations, the results have confirmed the effectiveness of the proposed framework both in mitigating freeway congestion and in reducing vehicles’ fuel consumption

Assessment of Railway Train Energy Efficiency and Safety Using Real-time Track Condition Information

This paper presents the use of track condition data from the virtual remote wireless sensor network within a simulation model of a battery-hybrid diesel-electric locomotive-driven freight train for a realistic mountain railway route simulation scenario. Simulation model includes the point-mass model of freight train longitudinal motion dynamics subject to wheel-to-track adhesion and head wind variations, the model of hybrid diesel-electric locomotive energy efficiency, and the model of real-time information provide to the virtual train driver about railway track conditions based on a narrow-band wireless remote sensor network. Simulation results are used to assess the possible benefits remote wireless sensor data for freight train energy-optimal control and to increase the transportation safety, including prediction of possible delays due to changed weather conditions en route

Mission Aware Energy Saving Strategies For Army Ground Vehicles

Fuel energy is a basic necessity for this planet and the modern technology to perform many activities on earth. On the other hand, quadrupled automotive vehicle usage by the commercial industry and military has increased fuel consumption. Military readiness of Army ground vehicles is very important for a country to protect its people and resources. Fuel energy is a major requirement for Army ground vehicles. According to a report, a department of defense has spent nearly $13.6 billion on fuel and electricity to conduct ground missions. On the contrary, energy availability on this plant is slowly decreasing. Therefore, saving energy in Army ground vehicles is very important. Army ground vehicles are embedded with numerous electronic systems to conduct missions such as silent and normal stationary surveillance missions. Increasing electrical energy consumption of these systems is influencing higher fuel consumption of the vehicle. To save energy, the vehicles can use any of the existing techniques, but they require complex, expensive, and time consuming implementations. Therefore, cheaper and simpler approaches are required. In addition, the solutions have to save energy according to mission needs and also overcome size and weight constraints of the vehicle. Existing research in the current literature do not have any mission aware approaches to save energy. This dissertation research proposes mission aware online energy saving strategies for stationary Army ground vehicles to save energy as well as to meet the electrical needs of the vehicle during surveillance missions. The research also proposes theoretical models of surveillance missions, fuzzy logic models of engine and alternator efficiency data, and fuzzy logic algorithms. Based on these models, two energy saving strategies are proposed for silent and normal surveillance type of missions. During silent mission, the engine is on and batteries power the systems. During normal surveillance mission, the engine is on, gear is on neutral position, the vehicle is stationary, and the alternator powers the systems. The proposed energy saving strategy for silent surveillance mission minimizes unnecessary battery discharges by controlling the power states of systems according to the mission needs and available battery capacity. Initial experiments show that the proposed approach saves 3% energy when compared with the baseline strategy for one scenario and 1.8% for the second scenario. The proposed energy saving strategy for normal surveillance mission operates the engine at fuel-efficient speeds to meet vehicle demand and to save fuel. The experiment and simulation uses a computerized vehicle model and a test bench to validate the approach. In comparison to vehicles with fixed high-idle engine speed increments, experiments show that the proposed strategy saves fuel energy in the range of 0-4.9% for the tested power demand range of 44-69 kW. It is hoped to implement the proposed strategies on a real Army ground vehicle to start realizing the energy savings

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Sécurité collaborative pour l internet des objets

Cette thèse aborde des nouveaux défis de sécurité dans l'Internet des Objets (IdO). La transition actuelle de l'Internet classique vers l'Internet des Objets conduit à de nombreux changements dans les modèles de communications sous-jacents. La nature hétérogène des communications de l IdO et le déséquilibre entre les capacités des entités communicantes qui le constituent rendent difficile l'établissement de connexions sécurisées de bout en bout. Contrairement aux nœuds de l Internet traditionnel, la plupart des composants de l'Internet des Objets sont en effet caractérisés par de faibles capacités en termes d'énergie et de puissance calcul. Par conséquent, ils ne sont pas en mesure de supporter des systèmes de sécurité complexes. En particulier, la mise en place d'un canal de communication sécurisé de bout en bout nécessite l établissement d'une clé secrète commune entre les deux nœuds souhaitant communiquer, qui sera négociée en s'appuyant sur un protocole d'échange de clés tels que le Transport Layer Security (TLS) Handshake ou l Internet Key Exchange (IKE). Or, une utilisation directe de ces protocoles pour établir des connexions sécurisées entre deux entités de l IdO peut être difficile en raison de l'écart technologique entre celles-ci et des incohérences qui en résultent sur le plan des primitives cryptographiques supportées. Le sujet de l'adaptation des protocoles de sécurité existants pour répondre à ces nouveaux défis a récemment été soulevé dans la communauté scientifique. Cependant, les premières solutions proposées n'ont pas réussi à répondre aux besoins des nœuds à ressources limitées. Dans cette thèse, nous proposons de nouvelles approches collaboratives pour l'établissement de clés, dans le but de réduire les exigences des protocoles de sécurité existants, afin que ceux-ci puissent être mis en œuvre par des nœuds à ressources limitées. Nous avons particulièrement retenu les protocoles TLS Handshake, IKE et HIP BEX comme les meilleurs candidats correspondant aux exigences de sécurité de bout en bout pour l'IdO. Puis nous les avons modifiés de sorte que le nœud contraint en énergie puisse déléguer les opérations cryptographiques couteuses à un ensemble de nœuds au voisinage, tirant ainsi avantage de l'hétérogénéité spatiale qui caractérise l IdO. Nous avons entrepris des vérifications formelles de sécurité et des analyses de performance qui prouvent la sureté et l'efficacité énergétique des protocoles collaboratifs proposés. Dans une deuxième partie, nous avons porté notre attention sur une classe d attaques internes que la collaboration entre les nœuds peut induire et que les mécanismes cryptographiques classiques, tels que la signature et le chiffrement, s'avèrent impuissants à contrer. Cela nous a amené à introduire la notion de confiance au sein d'un groupe collaboratif. Le niveau de fiabilité d'un nœud est évalué par un mécanisme de sécurité dédié, connu sous le nom de système de gestion de confiance. Ce système est lui aussi instancié sur une base collaborative, dans laquelle plusieurs nœuds partagent leurs témoignages respectifs au sujet de la fiabilité des autres nœuds. En nous appuyant sur une analyse approfondie des systèmes de gestion de confiance existants et des contraintes de l IoD, nous avons conçu un système de gestion de confiance efficace pour nos protocoles collaboratifs. Cette efficacité a été évaluée en tenant compte de la façon dont le système de gestion de la confiance répond aux exigences spécifiques à nos approches proposées pour l'établissement de clés dans le contexte de l'IdO. Les résultats des analyses de performance que nous avons menées démontrent le bon fonctionnement du système proposé et une efficacité accrue par rapport à la littératureThis thesis addresses new security challenges in the Internet of Things (IoT). The current transition from legacy Internet to Internet of Things leads to multiple changes in its communication paradigms. Wireless sensor networks (WSNs) initiated this transition by introducing unattended wireless topologies, mostly made of resource constrained nodes, in which radio spectrum therefore ceased to be the only resource worthy of optimization. Today's Machine to Machine (M2M) and Internet of Things architectures further accentuated this trend, not only by involving wider architectures but also by adding heterogeneity, resource capabilities inconstancy and autonomy to once uniform and deterministic systems. The heterogeneous nature of IoT communications and imbalance in resources capabilities between IoT entities make it challenging to provide the required end-to-end secured connections. Unlike Internet servers, most of IoT components are characterized by low capabilities in terms of both energy and computing resources, and thus, are unable to support complex security schemes. The setup of a secure end-to-end communication channel requires the establishment of a common secret key between both peers, which would be negotiated relying on standard security key exchange protocols such as Transport Layer Security (TLS) Handshake or Internet Key Exchange (IKE). Nevertheless, a direct use of existing key establishment protocols to initiate connections between two IoT entities may be impractical because of the technological gap between them and the resulting inconsistencies in their cryptographic primitives. The issue of adapting existing security protocols to fulfil these new challenges has recently been raised in the international research community but the first proposed solutions failed to satisfy the needs of resource-constrained nodes. In this thesis, we propose novel collaborative approaches for key establishment designed to reduce the requirements of existing security protocols, in order to be supported by resource-constrained devices. We particularly retained TLS handshake, Internet key Exchange and HIP BEX protocols as the best keying candidates fitting the end-to-end security requirements of the IoT. Then we redesigned them so that the constrained peer may delegate its heavy cryptographic load to less constrained nodes in neighbourhood exploiting the spatial heterogeneity of IoT nodes. Formal security verifications and performance analyses were also conducted to ensure the security effectiveness and energy efficiency of our collaborative protocols. However, allowing collaboration between nodes may open the way to a new class of threats, known as internal attacks that conventional cryptographic mechanisms fail to deal with. This introduces the concept of trustworthiness within a collaborative group. The trustworthiness level of a node has to be assessed by a dedicated security mechanism known as a trust management system. This system aims to track nodes behaviours to detect untrustworthy elements and select reliable ones for collaborative services assistance. In turn, a trust management system is instantiated on a collaborative basis, wherein multiple nodes share their evidences about one another's trustworthiness. Based on an extensive analysis of prior trust management systems, we have identified a set of best practices that provided us guidance to design an effective trust management system for our collaborative keying protocols. This effectiveness was assessed by considering how the trust management system could fulfil specific requirements of our proposed approaches for key establishment in the context of the IoT. Performance analysis results show the proper functioning and effectiveness of the proposed system as compared with its counterparts that exist in the literatureEVRY-INT (912282302) / SudocSudocFranceF

Architecture distribuée pour la détection d'activité dans un Espace Intelligent

La présente étude porte sur la capacité d'améliorer la détection des Activités de la Vie Quotidienne, AVQ (ou ADL :"Activity of Daily Life") par l'utilisation de capteur [i.e. capteurs] de mouvements portés par l'occupant d'un habitat intelligent. Les données provenant de ces capteurs devraient fusionner avec les informations issues de l'appartement pour donner une information plus pertinente par le principe de synergie [21]. La solution choisie pour le dispositif porté par la personne est l'innovation principale du projet : un réseau de capteurs disposés à plusieurs endroits sur le corps, communicant sans fil entre eux et avec le contrôle de l'appartement. Les données extraites sont le mouvement relatif du corps, et plus spécifiquement des mains et du tronc, par rapport à la verticale. De par les propriétés de ces éléments - nécessairement petits, discrets - des MEMS seront utilisés pour satisfaire ces critères. Le projet repose sur la conception des dispositifs embarqués sur l'occupant dans l'optique d'en étendre les fonctionnalités à d'autres analyses tels [i.e. telles] que le son, la position dans l'environnement, le statut médical, etc. Pour prouver la faisabilité, des capteurs externes seront ajoutés pour compléter les informations de base et donc étendre la qualité des inférences sur les activités en cours. Le mouvement est une donnée facilement détectable de par sa relative simplicité de mise en oeuvre et il fournit une bonne base de travail pour étudier de façon systématique les différents points clés de l'étude : la communication, la synergie des informations, l'analyse des activités, etc