Improving Performance of Opportunistic Routing Protocol using Fuzzy Logic for Vehicular Ad-hoc Networks in Highways

Vehicular ad hoc networks are an emerging technology with an extensive capability in various applications including vehicles safety, traffic management and intelligent transportation systems. Considering the high mobility of vehicles and their inhomogeneous distributions, designing an efficient routing protocol seems necessary. Given the fact that a road is crowded at some sections and is not crowded at the others, the routing protocol should be able to dynamically make decisions. On the other hand, VANET networks environment is vulnerable at the time of data transmission. Broadcast routing, similar to opportunistic routing, could offer better efficiency compared to other protocols. In this paper, a fuzzy logic opportunistic routing (FLOR) protocol is presented in which the packet rebroadcasting decision-making process is carried out through the fuzzy logic system along with three input parameters of packet advancement, local density, and the number of duplicated delivered packets. The rebroadcasting procedures use the value of these parameters as inputs to the fuzzy logic system to resolve the issue of multicasting, considering the crowded and sparse zones. NS-2 simulator is used for evaluating the performance of the proposed FLOR protocol in terms of packet delivery ratio, the end-to-end delay, and the network throughput compared with the existing protocols such as: FLOODING, P-PERSISTENCE and FUZZBR. The performance comparison also emphasizes on effective utilization of the resources. Simulations on highway environment show that the proposed protocol has a better QoS efficiency compared to the above published methods in the literature

Future cities and autonomous vehicles: analysis of the barriers to full adoption

The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development

A Fuzzy-Rule Based Data Delivery Scheme in VANETs with Intelligent Speed Prediction and Relay Selection

Data delivery in vehicular networks (VANETs) is a challenging task due to the high mobility and constant topological changes. In common routing protocols, multihop V2V communications suffer from higher network delay and lower packet delivery ratio (PDR), and excessive dependence on GPS may pose threat on individual privacy. In this paper, we propose a novel data delivery scheme for vehicular networks in urban environments, which can improve the routing performance without relying on GPS. A fuzzy-rule-based wireless transmission approach is designed to optimize the relay selection considering multiple factors comprehensively, including vehicle speed, driving direction, hop count, and connection time. Wireless V2V transmission and wired transmissions among RSUs are both utilized, since wired transmissions can reduce the delay and improve the reliability. Each RSU is equipped with a machine learning system (MLS) to make the selected relay link more reliably without GPS through predicting vehicle speed at next moment. Experiments show the validity and rationality of the proposed method.Peer Reviewe

An adaptive urban planning framework to support autonomous car technologies

In the last few decades, there has been increased discussion around smart mobility and the development of autonomous vehicles (AVs). The upcoming technology of self-driving vehicles has the potential to improve the quality of urban living and enhance sustainability, but our cities are not yet ready to adopt AVs. The physical infrastructure and legislative frameworks required are not yet in place, and public attitudes towards AVs are unclear. Although a great deal of current discussion revolves around the technical aspects of self-driving vehicles and technological maturity, there is a lack of research examining the full range of barriers to AV adoption and the potential impacts on urban planning. In order to begin to fill this gap, this study explores the barriers to full AV adoption in detail and develops an adaptive urban framework to assist urban planners, citizens, politicians, and stakeholders in their planning decision-making around AVs. To achieve this aim, the study adopts a mixed-methods research methodology following the multilevel model triangulation research design, with four distinct implementation phases. In Phase One, document analysis and content analysis is carried out to identify and analyse the barriers to the adoption of AVs in today’s cities and to analyse AV vehicle specifications and assess their potential impact on the urban transportation infrastructure. The analysis identifies key barriers in the following areas: 1) Safety; 2) User acceptance; 3) Regulations and ethics; 4) Accurate positioning & mapping; 5) Computer software & hardware; and 6) Communication Systems (Networks). The outcomes of this phase contribute to the development of a framework of barriers to the full adoption of AVs combined with the AV system architecture, tracing their interrelations, and an initial list of recommendations. In Phase Two, a semi-structured survey targeting experts in a range of disciplines associated with AVs is used to validate the framework developed in Phase One and to determine the possible impacts on city planning and transportation infrastructure of a hypothetical journey through the city of Nottingham made by a fully autonomous vehicle (Level 4). This phase reveals that the majority of experts believe that both existing design principles and design guidance will be affected, with street elements such as roundabouts/intersections, zebra crossings, charging points, on-street parking, road signs, and drop points most severely affected. For instance, 61% of experts agree that AVs’ hubs should be in each neighborhood. 19% of experts argue that manual driving should be banned. In Phase Three, a structured survey targeting members of the public in Nottingham is used to analyse current public attitudes and behaviours in respect of AVs and to begin to identify factors which might drive AV adoption in future. 57% of people are expected to share AVs and 64% are expected to own them in the city. In terms of data privacy, 46% of people disagree with sharing their data. The final phase of the research involves combining the outcomes of the previous phases to create the final adaptive urban planning framework to support future planning decision-making around AVs. A detailed list of recommendations to address the technical, social and legislative barriers identified is also proposed. The study concludes by suggesting avenues for subsequent research to build on these outcomes and further support the adoption of AVs as part of moves to promote smart mobility and enhance the quality of life in our cities

A Trust Management Framework for Vehicular Ad Hoc Networks

The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers