Reinforcement Learning based Gateway Selection in VANETs

In vehicular ad hoc networks (VANETs), providing the Internet has become an urgent necessity, where mobile gateways are used to ensure network connection to all customer vehicles in the network. However, the highly dynamic topology and bandwidth limitations of the network represent a significant issue in the gateway selection process. Two objectives are defined to overcome these challenges. The first objective aims to maximize the number of vehicles connected to the Internet by finding a suitable gateway for them depending on the connection lifetime. The second objective seeks to minimize the number of connected vehicles to the same gateway to overcome the limitation of gateways\u27 bandwidth and distribute the load in the network. For this purpose, A gateway discovery system assisted by the vehicular cloud is implemented to find a fair trade-off between the two conflicting objectives. Proximal Policy Optimization, a well-known reinforcement learning strategy, is used to define and train the agent. The trained agent was evaluated and compared with other multi-objective optimization methods under different conditions. The obtained results show that the proposed algorithm has better performance in terms of the number of connected vehicles, load distribution over the mobile gateways, link connectivity duration, and execution time

Cloud-Assisted Safety Message Dissemination in VANET-Cellular Heterogeneous Wireless Network

In vehicular ad hoc networks (VANETs), efficient message dissemination is critical to road safety and traffic efficiency. Since many VANET-based schemes suffer from high transmission delay and data redundancy, the integrated VANET–cellular heterogeneous network has been proposed recently and attracted significant attention. However, most existing studies focus on selecting suitable gateways to deliver safety message from the source vehicle to a remote server, whereas rapid safety message dissemination from the remote server to a targeted area has not been well studied. In this paper, we propose a framework for rapid message dissemination that combines the advantages of diverse communication and cloud computing technologies. Specifically, we propose a novel Cloud-assisted Message Downlink dissemination Scheme (CMDS), with which the safety messages in the cloud server are first delivered to the suitable mobile gateways on relevant roads with the help of cloud computing (where gateways are buses with both cellular and VANET interfaces), and then being disseminated among neighboring vehicles via vehicle-to-vehicle (V2V) communication. To evaluate the proposed scheme, we mathematically analyze its performance and conduct extensive simulation experiments. Numerical results confirm the efficiency of CMDS in various urban scenarios

Do we all really know what a fog node is? Current trends towards an open definition

Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.Postprint (author's final draft

New Gateway Selection Algorithm Based on Multi-Objective Integer Programming and Reinforcement Learning

Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requires finding suitable gateways to connect the source vehicles to the infrastructure. The major feature of using gateways is to decrease the load of the network infrastructure resources so that each gateway is responsible for a group of vehicles. Unfortunately, the implementation of this goal is facing many challenges, including the highly dynamic topology of VANETs, which causes network instability, and the deployment of applications with high bandwidth demand that can cause network congestion, particularly in urban areas with a high-density vehicle. This work introduces a novel gateway selection algorithm for vehicular networks in urban areas, consisting of two phases. The first phase identifies the best gateways among the deployed vehicles using multi-objective integer programming. While in the second phase, reinforcement learning is employed to select a suitable gateway for any vehicular node in need to access the VANET infrastructure. The proposed model is evaluated and compared to other existing solutions. The obtained results show the efficiency of the proposed system in identifying and selecting the gateways

A Review Paper on Accident Detection System Using Intelligent Algorithm for VANET

Our lives became easier with the Quick accretion of technology and infrastructure. The advent of technology has also rise the traffic hazards and the road accident take place repeatedly which causes massive loss of life and property because of the poor emergency facilities. Recently, intelligent transportation systems (ITS) have emerged as an efficient way of improving interpretation of transportation systems and enhancing travel safety. Accident detection systems are one of the most effective (ITS) tools. The accident detected system which based on Global Positioning System (GPS) and Global System for Mobile communication (GSM) can be accomplish though one or several sensors, the system can gathers the information and coordinates of accident spot then send this data to the rescues services center over a network link in shortest time, It represented as an instance helping system. In this review paper, we proposed an intelligent system that composed of a GPS receiver, Vibration sensor, GSM Modem and integrated with Vehicular AD-Hoc Network (VANET). The employ of (VANET) by enhanced Ad hoc On-Demand Distance Vector protocol (AODV) helps these services in finding the optimum route to the emergency message. The use of GSM, GPS, and VANET technologies allows the system to track vehicle and provides the most instant and accurate information about the vehicle accident spot. Keywords: GPS, GSM, VANET, AODV

Cloud Computing in VANETs: Architecture, Taxonomy, and Challenges

Cloud Computing in VANETs (CC-V) has been investigated into two major themes of research including Vehicular Cloud Computing (VCC) and Vehicle using Cloud (VuC). VCC is the realization of autonomous cloud among vehicles to share their abundant resources. VuC is the efficient usage of conventional cloud by on-road vehicles via a reliable Internet connection. Recently, number of advancements have been made to address the issues and challenges in VCC and VuC. This paper qualitatively reviews CC-V with the emphasis on layered architecture, network component, taxonomy, and future challenges. Specifically, a four-layered architecture for CC-V is proposed including perception, co-ordination, artificial intelligence and smart application layers. Three network component of CC-V namely, vehicle, connection and computation are explored with their cooperative roles. A taxonomy for CC-V is presented considering major themes of research in the area including design of architecture, data dissemination, security, and applications. Related literature on each theme are critically investigated with comparative assessment of recent advances. Finally, some open research challenges are identified as future issues. The challenges are the outcome of the critical and qualitative assessment of literature on CC-V