Dynamic Q-learning and fuzzy CNN based vertical handover decision for integration of DSRC, mmWave 5G and LTE in internet of vehicles (IoV)

Internet of vehicles commonly known as IOV is a newly emerged area which with the help of internet assisted communication provides the support to the vehicles. Due to the access of more than one radio access network, 5G makes the connectivity ubiquitous. Vehicle mobility demands for handover in such heterogeneous networks. Instead of using better technology for long ranges and other types of traffic, the vehicles are using devoted short range communications at short ranges. Commonly, networks for handovers were used to be selected directly or with the available radio access it used to connect automatically. With the help of this, the hand over occurrence now takes places frequently. This paper is based on the incorporation of DSRC, LTE as well as mm Wave on Internet of vehicles which is integrated with the Handover decision making algorithm, Network Selection and Routing. The decision of the handovers is to ensure that if there is any requirement of the vertical handovers using dynamic Q-learning algorithms in which entropy function is used to predict the threshold according to the characteristics of the environment. The network selection process is done using Fuzzy Convolution Neural Network commonly known as FCNN which makes the fuzzy rules by considering the parameters such as strength of its signal, its distance, the density of the vehicle, the type of its data as well the Line of Sight (LoS). V2V chain routing is presented in such a manner that V2V pairs are also selected with the help of jellyfish optimization algorithm considering three metrics – Vehicle metrics, Channel metrics and Vehicle performance metrics. OMNET++ simulator is the software in which system is developed. The performance evaluation is done according to its Handover Success Probability, Handover Failure, Redundant Handover, Mean Throughput, delay and Packet Loss

Contributions to Vehicular Communications Systems and Schemes

La dernière décennie a marqué une grande hausse des applications véhiculaires comme une nouvelle source de revenus et un facteur de distinction dans l'industrie des véhicules. Ces applications véhiculaires sont classées en deux groupes : les applications de sécurité et les applications d'info divertissement. Le premier groupe inclue le changement intelligent de voie, l'avertissement de dangers de routes et la prévention coopérative de collision qui comprend la vidéo sur demande (VoD), la diffusion en direct, la diffusion de météo et de nouvelles et les jeux interactifs. Cependant, Il est à noter que d'une part, les applications véhiculaires d'info divertissement nécessitent une bande passante élevée et une latence relativement faible ; D'autre part, les applications de sécurité requièrent exigent un délai de bout en bout très bas et un canal de communication fiable pour la livraison des messages d'urgence. Pour satisfaire le besoin en applications efficaces, les fabricants de véhicules ainsi que la communauté académique ont introduit plusieurs applications à l’intérieur de véhicule et entre véhicule et véhicule (V2V). Sauf que, l'infrastructure du réseau sans fil n'a pas été conçue pour gérer les applications de véhicules, en raison de la haute mobilité des véhicules, de l'imprévisibilité du comportement des conducteurs et des modèles de trafic dynamiques. La relève est l'un des principaux défis des réseaux de véhicules, car la haute mobilité exige au réseau sans fil de faire la relève en un très court temps. De plus, l'imprévisibilité du comportement du conducteur cause l'échec des protocoles proactifs traditionnels de relève, car la prédiction du prochain routeur peut changer en fonction de la décision du conducteur. Aussi, le réseau de véhicules peut subir une mauvaise qualité de service dans les régions de relève en raison d'obstacles naturels, de véhicules de grande taille ou de mauvaises conditions météorologiques. Cette thèse se concentre sur la relève dans l'environnement des véhicules et son effet sur les applications véhiculaires. Nous proposons des solutions pratiques pour les réseaux actuellement déployés, principalement les réseaux LTE, l'infrastructure véhicule à véhicule (V2V) ainsi que les outils efficaces d’émulateurs de relèves dans les réseaux véhiculaires.----------ABSTRACT: The last decade marked the rise of vehicular applications as a new source of revenue and a key differentiator in the vehicular industry. Vehicular Applications are classified into safety and infotainment applications. The former include smart lane change, road hazard warning, and cooperative collision avoidance; however, the latter include Video on Demand (VoD), live streaming, weather and news broadcast, and interactive games. On one hand, infotainment vehicular applications require high bandwidth and relatively low latency; on the other hand, safety applications requires a very low end to end delay and a reliable communication channel to deliver emergency messages. To satisfy the thirst for practical applications, vehicle manufacturers along with research institutes introduced several in-vehicle and Vehicle to Vehicle (V2V) applications. However, the wireless network infrastructure was not designed to handle vehicular applications, due to the high mobility of vehicles, unpredictability of drivers’ behavior, and dynamic traffic patterns. Handoff is one of the main challenges of vehicular networks since the high mobility puts pressure on the wireless network to finish the handoff within a short period. Moreover, the unpredictability of driver behavior causes the traditional proactive handoff protocols to fail, since the prediction of the next router may change based on the driver’s decision. Moreover, the vehicular network may suffer from bad Quality of Service (QoS) in the regions of handoff due to natural obstacles, large vehicles, or weather conditions. This thesis focuses on the handoff on the vehicular environment and its effect on the vehicular applications. We consider practical solutions for the currently deployed networks mainly Long Term Evolution (LTE) networks, the Vehicle to Vehicle (V2V) infrastructure, and the tools that can be used effectively to emulate handoff on the vehicular networks

SDNと車両クラスタリングを活用したV2I通信方式

電気通信大学202