Fixed Cluster Based Cluster Head Selection Algorithm in Vehicular Adhoc Network

The emergence of Vehicular Adhoc Networks (VANETs) is expected support variety of applications for driver assistance, traffic efficiency and road safety. For proper transmission of messages in VANET, one of the proposed solutions is dividing the network into clusters and then selecting a cluster head (CH) in each cluster. This can decrease the communication overhead between road side units (RSUs) and other components of VANETs, because instead of every node communicating with RSU, only CH communicates with RSU and relays relevant messages. In clustering, an important step is the selection of CH. In this thesis, we implemented vehicle to vehicle (V2V), cluster head to road side unit and road side unit to trusted authority authentication for the clustered network. We also presented a heuristic algorithm for selecting a suitable vehicle as the cluster head in a cluster. For the selection of head vehicle, we used weighted fitness values based on three parameters; trust value, position from the cluster boundary and absolute relative average speed. Simulation results indicate that the proposed approach can lead to improvements in terms of QoS metrics like delay, throughput and packet delivery ratio

Secure Intelligent Vehicular Network Including Real-Time Detection of DoS Attacks in IEEE 802.11P Using Fog Computing

VANET (Vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. Intermittent exchange of real-time safety message delivery in VANET has become an urgent concern, due to DoS (Denial of service), and smart and normal intrusions (SNI) attacks. Intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication need. In this research, Fog computing (FC) integrates with hybrid optimization algorithms (OAs) including: Cuckoo search algorithm (CSA), Firefly algorithm (FA) and Firefly neural network, in addition to key distribution establishment (KDE), for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme which is also termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC) utilizes feedforward back propagation neural network (FFBP-NN). This is also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The proposed scheme is initially compared with the Cuckoo and FA, and the Firefly neural network to evaluate the QoS parameters such as jitter and throughput. In addition, VANET is a means whereby Intelligent Transportation System (ITS) has become important for the benefit of daily lives. Therefore, real-time detection of all form attacks including hybrid DoS attacks in IEEE 802.11p, has become an urgent attention for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate enormous amount of message. This leads to the RSU (roadside unit) or the CPU (central processing unit) overutilization for computation. Therefore, it is required that efficient storage and intelligence VANET infrastructure architecture (VIA), which include trustworthiness is desired. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computations, and communication need for VANET. This dissertation also utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence including: Cuckoo/CSA Artificial Bee Colony (ABC) Firefly/Genetic Algorithm (GA), in additionally to provide Real-time Detection of DoS attacks in IEEE 802.11p, using VFC for Intelligent Vehicular network. Vehicles are moving with certain speed and the data is transmitted at 30Mbps. Firefly FFBPNN (Feed forward back propagation neural network) has been used as a classifier to also distinguish between the attacked vehicles and the genuine vehicle. The proposed scheme has also been compared with Cuckoo/CSA ABC and Firefly GA by considering Jitter, Throughput and Prediction accuracy

A trust-driven privacy architecture for vehicular ad-hoc networks

Vehicular Ad-Hoc NETworks (VANETs) are an emerging technology which aims to improve road safety by preventing and reducing traffic accidents. While VANETs offer a great variety of promising applications, such as, safety-related and infotainment applications, they remain a number of security and privacy related research challenges that must be addressed. A common approach to security issues widely adopted in VANETs is the use of Public Key Infrastructures (PKI) and digital certificates in order to enable authentication, authorization and confidentiality. These approaches usually rely on a large set of regional Certification Authorities (CAs). Despite the advantages of PKI-based approaches, there are two main problems that arise, i) the secure interoperability among the different and usually unknown- issuing CAs, and ii) the sole use of PKI in a VANET environment cannot prevent privacy related attacks, such as, linking a vehicle with an identifier, tracking vehicles ¿big brother scenario" and user profiling. Additionally, since vehicles in VANETs will be able to store great amounts of information including private information, unauthorized access to such information should be carefully considered. This thesis addresses authentication and interoperability issues in vehicular communications, considering an inter-regional scenario where mutual authentication between nodes is needed. To provide interoperability between vehicles and services among different domains, an Inter-domain Authentication System (AS) is proposed. The AS supplies vehicles with a trusted set of authentication credentials by implementing a near real-time certificate status service. The proposed AS also implements a mechanism to quantitatively evaluate the trust level of a CA, in order to decide on-the-y if an interoperability relationship can be created. This research work also contributes with a Privacy Enhancing Model (PEM) to deal with important privacy issues in VANETs. The PEM consists of two PKI-based privacy protocols: i) the Attribute-Based Privacy (ABP) protocol, and ii) the Anonymous Information Retrieval (AIR) protocol. The ABP introduces Attribute-Based Credentials (ABC) to provide conditional anonymity and minimal information disclosure, which overcome with the privacy issues related to linkability (linking a vehicle with an identifier) and vehicle tracking (big brother scenario). The AIR protocol addresses user profiling when querying Service Providers (SPs), by relying in a user collaboration privacy protocol based on query forgery and permutation; and assuming that neither participant nodes nor SPs could be completely trusted. Finally, the Trust Validation Model (TVM) is proposed. The TVM supports decision making by evaluating entities trust based on context information, in order to provide i) access control to driver and vehicle's private information, and ii) public information trust validation

Secure Intelligent Vehicular Network Using Fog Computing

VANET (vehicular ad hoc network) has a main objective to improve driver safety and traffic efficiency. The intermittent exchange of real-time safety message delivery in VANET has become an urgent concern due to DoS (denial of service) and smart and normal intrusions (SNI) attacks. The intermittent communication of VANET generates huge amount of data which requires typical storage and intelligence infrastructure. Fog computing (FC) plays an important role in storage, computation, and communication needs. In this research, fog computing (FC) integrates with hybrid optimization algorithms (OAs) including the Cuckoo search algorithm (CSA), firefly algorithm (FA), firefly neural network, and the key distribution establishment (KDE) for authenticating both the network level and the node level against all attacks for trustworthiness in VANET. The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). A feedforward back propagation neural network (FFBP-NN), also termed the firefly neural, is used as a classifier to distinguish between the attacking vehicles and genuine vehicles. The SIVNFC scheme is compared with the Cuckoo, the FA, and the firefly neural network to evaluate the quality of services (QoS) parameters such as jitter and throughput.http://dx.doi.org/10.3390/electronics804045

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks