Fast energy-aware OLSR routing in VANETs by means of a parallel evolutionary.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/17174This work tackles the problem of reducing the power consumption of the OLSR routing protocol in vehicular networks. Nowadays, energy-aware and green communication protocols are important research topics, specially when deploying wireless mobile networks. This article introduces a fast automatic methodology to search for energy-efficient OLSR configurations by using a parallel evolutionary algorithm. The experimental analysis demonstrates that significant improvements over the standard configuration can be attained in terms of power consumption, with no noteworthy loss in the QoS

Performance Evaluation of Routing Protocols for Vehicle Re-Routing in ITS-based Vehicular Networks

This study aims to assess the performance of routing protocols in Intelligent Transportation System (ITS)-based vehicular networks, specifically in accident and highway scenarios. The effective management of traffic flow in these situations is crucial for ensuring the safety and smooth operation of vehicular networks. Therefore, it is imperative to evaluate routing protocols to identify the most suitable one for these scenarios. The evaluation considers various commonly used routing protocols in vehicular networks, including Ad hoc On-Demand Distance Vector (AODV), Ad hoc On-Demand Multipath Distance Vector (AOMDV), and Destination-Sequenced Distance Vector (DSDV). The evaluation is based on several performance metrics, such as packet delivery ratio, end-to-end delay, network throughput, normalized routing load, and routing overhead. These metrics provide insights into the effectiveness and efficiency of the routing protocols in handling re-routing in accident and highway scenarios. The research is divided into two modules, Module I and Module II, to evaluate the effectiveness of routing protocols in these distinct scenarios using the NS2 simulation tool. The simulation results are analyzed and compared to determine the performance of the routing protocols in each module. The findings indicate that AODV consistently achieves the highest throughput, packet delivery ratio, and lowest end-to-end delay, routing overhead, and normalized routing load, followed by AOMDV and then DSDV. The findings of this study contribute to the understanding of the strengths and weaknesses of different routing protocols in accident and highway scenarios. This knowledge can assist in the development of more efficient and reliable routing protocols for vehicular networks

Cognitive Radio Assisted OLSR Routing for Vehicular Sensor Networks

AbstractVehicular Sensor Network (VSN) emerged due to recent developments in Wireless Sensor Network (WSN) and functioning as a way for observing metropolitan environments and enabling vehicles to share relevant sensor data to assist safety, convenience and commercial applications. Data dissemination is an important aspect of these networks and requires timely delivery of important sensor information. In VSNs, rapid mobility of the vehicles causes recurrent topography modifications. The possibility of on-demand protocols that makes routing decisions reactively in Vehicular Networks are restricted owing to its structural instability and current routing protocols, operating in a table-driven fashion like OLSR are unable to cope up with the high demands imposed by vehicular applications. Furthermore, sensor data transmissions are accompanied by rapid fluctuations in the convention of licensed spectrum and acquire more number of channels to transmit huge bandwidth data and result in spectrum scarcity. Existing works on OLSR protocol failed to examine spectrum conditions and calculate utilization of channel. Cognitive Radio (CR) is a possible solution for guiding OLSR to discover unused frequency bands and utilize them opportunistically. This paper presents an optimal OLSR routing for efficient data communication using Cognitive Radio enabled Vehicular Sensor Networks (CR-VSNs). The proposed model was tested under simulated traffic of Chennai urban road map. Delay is observed to be minimal for data communications in CR-VSN

A pervasive prediction model for vehicular ad-hoc network (VANET)

The growth of city traffic has contributed to severe traffic congestion and traffic accidents in the most of the cities in the world. Since people’s travel demand rise at a rate usually greater than the addition of road capacity to lead many other issues, such as environmental problems and the quality of life. Intelligent Transportation System (ITS) is committed to solving the worsening traffic problems. Wide deployment of such ITS can eventually provide more dynamic, real-time and efficient solutions to transportation problems. ITS uses a variety of high technologies, especially electronic information technology and data communications technology to improve road traffic efficiency, road traffic safety and environmental protection. A number of researchers have depended on the wireless mobile communication to improve data collection and utilisation. The data could be used for early warning and forecasting traffic conditions in real-time. The benefit of wireless mobile communication research, especially Car to Car (C2C) communication is to abandon the expensive wireline-deployed and central processing units. Through the interconnection of many personal mobile devices, a low- cost freely extended, high-performance and parallel system can be formed. Car to Car communication can make possible efficient and reliable data transmission by wireless links in a traffic area. It is based on principles of mobile ad-hoc network (MANET) and applies to the domain of vehicles, being Vehicular ad-hoc network (VANET) which is a key component of ITS. The C2C communication system has become essential for driving safety and comfort and also for improving road condition. Also, the traffic prediction system is also an important part of ITS, traffic condition prediction can be regarded as an extension application of VANET. It provides traffic condition in advance via a variety of prediction models and helps the people make better driving safety, travel decisions and route selections regarding departure or driving time. The challenge of wireless traffic prediction technology is the uncertainty of traffic and real-time traffic data collection. It is widely known that urban transport system is a participatory, time-varying and complex nonlinear system. This uncertainty comes not only from the natural causes, such as seasonal and weather factors, but also from human factors, such as traffic accidents, emergencies and driver’s behaviour. In particular, the short-term traffic prediction is more affected by random interference factors. Current wireless traffic prediction research is usually based on a combination of wireless technology and traditional prediction model. The predictable traffic conditions include travel speed, travel time, traffic density, traffic accident, congestion level. However, in a large network environment, as the number of nodes increases, the transmission performance degrades and the prediction accuracy decreases because the prediction model does not obtain enough data. In this thesis, a novel traffic prediction framework (PPM-C2C) is proposed – Pervasive Prediction Model (PPM) based on the C2C communication. The framework utilises ad-hoc data via C2C communications for a short time traffic prediction in each car. This project builds and investigates the behaviour of a pervasive traffic simulation model in Ad-hoc network, with a particular part of it embedded into each vehicle’s equipment. It includes the data collection, aggregation and application aim to be running in all individual cars so that cars have up to date information on the traffic at all times. Moreover, those cars could predict the traffic conditions of a road section in a short time through the proposed prediction framework, especially travel speed prediction. When the car receives the current traffic information about other vehicles, the prediction system will incorporate the information, analyse the data and predict the traffic conditions of this road section for a future time. The design does not depend on any roadside communications infrastructure. It is a simple and flexible car communication and processing technology to collect real-time traffic information. This process will be aided by car to car wireless communication technology available nowadays. To achieve this goal, a mobility model adapted to VANET needs to be generated that a realistic city scenario based on the actual traffic traces is carried out through simulation. Based on this, we investigate the necessary influencing factors for predicted results. The simulation results illustrate that the prediction model can be applied to wireless network environment for a short time prediction, and our results demonstrate the viability and effectiveness of the proposed prediction framework over Car to Car communications. Furthermore, the wireless environment and derived factors can result in decreased application performance

Enhancing the VANET Network Layer

The aim of this thesis is to examine existing VANET network layer functionality and to propose enhancements to the VANET network layer to facilitate vehicular (V2X) communication. This thesis proposes three enhancements to the VANET network layer which address many of the issues with V2X communication, these enhancements are: a geographic overlay allowing vehicles to localize themselves; an IPv6 addressing strategy which embeds positional information within an IP address allowing for location based routing; and finally a novel position based routing protocol which has two primary advantages over existing protocols, firstly removing unnecessary overhead information and control communication, and secondly support for multiple types of V2X communication models. The simulation results show that the proposed enhancements are well suited in low and medium vehicular density environments. Based on the observed behaviors the author recommends further modification and study of position based routing protocols

LS-AODV: A ROUTING PROTOCOL BASED ON LIGHTWEIGHT CRYPTOGRAPHIC TECHNIQUES FOR A FANET OF NANO DRONES

With the battlespace rapidly shifting to the cyber domain, it is vital to have secure, robust routing protocols for unmanned systems. Furthermore, the development of nano drones is gaining traction, providing new covert capabilities for operators at sea or on land. Deploying a flying ad hoc network (FANET) of nano drones on the battlefield comes with specific performance and security issues. This thesis provides a novel approach to address the performance and security concerns faced by FANET routing protocols, and, in our case, is specifically tailored to improve the Ad Hoc On-Demand Distance Vector (AODV) routing protocol. The proposed routing protocol, Lightweight Secure Ad Hoc On-Demand Distance Vector (LS-AODV), uses a lightweight stream cipher, Trivium, to encrypt routing control packets, providing confidentiality. The scheme also uses Chaskey-12-based message authentication codes (MACs) to guarantee the authenticity and integrity of control packets. We use a network simulator, NS-3, to compare LS-AODV against two benchmark routing protocols, AODV and the Optimized Link State Routing (OLSR) protocol, in order to gauge network performance and security benefits. The simulation results indicate that when the FANET is not under attack from black-hole nodes, LS-AODV generally outperforms OLSR but performs slightly worse than AODV. On the other hand, LS-AODV emerges as the protocol of choice when a FANET is subject to a black-hole attack.ONROutstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms