AEGRP: an enhanced geographical routing protocol for vanet

Vehicular ad hoc network (VANET), is a derivative type of mobile ad hoc networks with its unique characteristics and an essential part of intelligent transportation system (ITS). In VANET, the vehicles can disseminate information to certain or all vehicles within a region for different applications. Applications can be categorized as safety, convenience and comfort of the driver and passengers such as traffic conditions, accident detection, roadway safety, mobile sensing, and infotainment. These promising applications require intelligent and efficient routing protocols, which are capable of adapting rapidly changing topologies, high mobility in the network. Geographic routing protocols have become a popular routing type because of its simplicity and low overhead features, but recent research has recognized these protocols are not considering many particular constraints of the vehicular environment. However, existing routing protocols offered limited performance due to frequent disconnectivity, high signal interference in the presence of obstacles and lead to network delay and overhead issues. The main objective of this paper is to design an enhanced geographical routing protocol that addresses the network delay problems and provide necessary improvements over conventional geographic routing in light of constraints of these environments

TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET

Vehicle-to-everything (V2X) communication in the vehicular ad hoc network (VANET), an infrastructure-free mechanism, has emerged as a crucial component in the advanced Intelligent Transport System (ITS) for special information transmission and inter-vehicular communications. One of the main research challenges in VANET is the design and implementation of network routing protocols which manage to trigger V2X communication with the reliable end-to-end connectivity and efficient packet transmission. The organically changing nature of road transport vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packet delivery. Therefore, a position-based routing protocol tends to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. In this paper, a target-driven and mobility prediction (TDMP) based routing protocol is therefore developed for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. The primary idea in TDMP is that the destination target of a driver is included in the mobility prediction to assist the implementation of the routing protocol. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, TDMP is developed to enhance the packet transmission with the consideration of the estimation of inter-vehicles link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout.Comment: 35 pages,16 Figure

Performance improvement in geographic routing for vehicular Ad Hoc networks

Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular Ad Hoc Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and spee

Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Ubiquitous Sensor Networks and Its Application

Ubiquitous sensor networks and its application are emerging rapidly as an exciting new paradigm to provide reliable and comfortable life services. The ever-growing ubiquitous sensor networks and its application will provide an intelligent and ubiquitous communication and network technology for tomorrow. That is, the UCMA have emerged rapidly as an exciting new paradigm that includes ubiquitous, grid, and peer-to-peer computing to provide computing and communication services at anytime and anywhere. In order to realize the advantages of such services, it is important that intelligent systems be suitable for UCMA

CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data

Crime, Policing and Citizenship (CPC) – Space-Time Interactions of Dynamic Networks has been a major UK EPSRC-funded research project. It has been a multidisciplinary collaboration of geoinformatics, crime science, computer science and geography within University College London (UCL), in partnership with the Metropolitan Police Service (MPS). The aim of the project has been to develop new methods and applications in space-time analytics and emergent network complexity, in order to uncover patterning and interactions in crime, policing and citizen perceptions. The work carried out throughout the project will help inform policing at a range of scales, from the local to the city-wide, with the goal of reducing both crime and the fear of crime. The CPC project is timely given the tremendous challenging facing policing in big cities nationally and globally, as consequences of changes in society, population structure and economic well-being. It addresses these issues through an intelligent approach to data-driven policing, using daily reported crime statistics, GPS traces of foot and vehicular patrols, surveys of public attitudes and geo-temporal demographic data of changing community structure. The analytic focus takes a spatio-temporal perspective, reflecting the strong spatial and temporal integration of criminal, policing and citizen activities. Street networks are used throughout as a basis for analysis, reflecting their role as a key determinant of urban structure and the substrate on which crime and policing take place. The project has presented a manifesto for ‘intelligent policing’ which embodies the key issues arising in the transition from Big Data into actionable insights. Police intelligence should go beyond current practice, incorporating not only the prediction of events, but also how to respond to them, and how to evaluate the actions taken. Cutting-edge network-based crime prediction methods have been developed to accurately predict crime risks at street segment level, helping police forces to focus resources in the right places at the right times. Methods and tools have been implemented to support senior offices in strategic planning, and to provide guidance to frontline officers in daily patrolling. To evaluate police performance, models and tools have been developed to aid identification of areas requiring greater attention, and to analyse the patrolling behaviours of officers. Methods to understand and model confidence in policing have also been explored, suggesting strategies by which confidence in the police can be improved in different population segments and neighbourhood areas. A number of tools have been developed during the course of the project include data-driven methods for crime prediction and for performance evaluation. We anticipate that these will ultimately be adopted in daily policing practice and will play an important role in the modernisation of policing. Furthermore, we believe that the approaches to the building of public trust and confidence that we suggest will contribute to the transformation and improvement of the relationship between the public and police