Road-based routing in vehicular ad hoc networks

Vehicular ad hoc networks (VANETs) can provide scalable and cost-effective solutions for applications such as traffic safety, dynamic route planning, and context-aware advertisement using short-range wireless communication. To function properly, these applications require efficient routing protocols. However, existing mobile ad hoc network routing and forwarding approaches have limited performance in VANETs. This dissertation shows that routing protocols which account for VANET-specific characteristics in their designs, such as high density and constrained mobility, can provide good performance for a large spectrum of applications. This work proposes a novel class of routing protocols as well as three forwarding optimizations for VANETs. The Road-Based using Vehicular Traffic (RBVT) routing is a novel class of routing protocols for VANETs. RBVT protocols leverage real-time vehicular traffic information to create stable road-based paths consisting of successions of road intersections that have, with high probability, network connectivity among them. Evaluations of RBVT protocols working in conjunction with geographical forwarding show delivery rate increases as much as 40% and delay decreases as much as 85% when compared with existing protocols. Three optimizations are proposed to increase forwarding performance. First, one- hop geographical forwarding is improved using a distributed receiver-based election of next hops, which leads to as much as 3 times higher delivery rates in highly congested networks. Second, theoretical analysis and simulation results demonstrate that the delay in highly congested networks can be reduced by half by switching from traditional FIFO with Taildrop queuing to LIFO with Frontdrop queuing. Third, nodes can determine suitable times to transmit data across RBVT paths or proactively replace routes before they break using analytical models that accurately predict the expected road-based path durations in VANETs

Exploring intelligent service migration in a highly mobile network

Mobile services allow services to be migrated or replicated closer to users as they move around. This is now regarded as a viable mechanism to provide good Quality of Service to users in highly mobile environments such as vehicular networks. The vehicular environment is rapidly becoming a significant part of the internet and this presents various challenges that must be addressed; this is due to continuous handovers as mobile devices change their point of attachment to these networks resulting in a loss of service. Therefore, this explains the need to build a framework for intelligent service migration. This thesis addresses these issues. It starts by discussing the requirements for intelligent service migration. Then it investigates a low latency Quality of Service Aware Framework as well as an experimental transport protocol that would be favoured by vehicular networks. Furthermore, two analytical models are developed using the Zero-Server Markov Chain technique which is a way of analysing scenarios when the server is not continuously available to serve. Using the Zero-Server Markov Chain, the first analytical model looks at lost service due to continuous handovers and the communication dynamics of vehicular networks, while the second model analyses how service migration affects service delivery in these networks. Formulas are developed to yield the average number of packets in the system, the response time, the probability of blocking and a new parameter called the probability of lost service. These formulas are then applied to the Middlesex VANET Testbed to look at reactive and proactive service migration. These techniques are then incorporated into a new Service Management Framework to provide sustainable Quality of Service and Quality of Experience to mobile users in vehicular networks. This thesis also shows that this new approach is better than current approaches as it addresses key issues in intelligent service migration in such environments, and hence can play a significant part in the development of Intelligent Transport Systems for Smart Cities

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Hybrid performance modelling of opportunistic networks

We demonstrate the modelling of opportunistic networks using the process algebra stochastic HYPE. Network traffic is modelled as continuous flows, contact between nodes in the network is modelled stochastically, and instantaneous decisions are modelled as discrete events. Our model describes a network of stationary video sensors with a mobile ferry which collects data from the sensors and delivers it to the base station. We consider different mobility models and different buffer sizes for the ferries. This case study illustrates the flexibility and expressive power of stochastic HYPE. We also discuss the software that enables us to describe stochastic HYPE models and simulate them.Comment: In Proceedings QAPL 2012, arXiv:1207.055

Content Download in Vehicular Networks in Presence of Noisy Mobility Prediction

Bandwidth availability in the cellular backhaul is challenged by ever-increasing demand by mobile users. Vehicular users, in particular, are likely to retrieve large quantities of data, choking the cel- lular infrastructure along major thoroughfares and in urban areas. It is envisioned that alternative roadside network connectivity can play an important role in offloading the cellular infrastructure. We investigate the effectiveness of vehicular networks in this task, considering that roadside units can exploit mobility prediction to decide which data they should fetch from the Internet and to schedule transmissions to vehicles. Rather than adopting a specific prediction scheme, we propose a fog-of-war model that allows us to express and account for different degrees of prediction accuracy in a simple, yet effective, manner. We show that our fog-of-war model can closely reproduce the prediction accuracy of Markovian techniques. We then provide a probabilistic graph-based representation of the system that includes the prediction information and lets us optimize content prefetching and transmission scheduling. Analytical and simulation results show that our approach to content downloading through vehicular networks can achieve a 70% offload of the cellular networ

Using Distributed Ledger Technologies in VANETs to Achieve Trusted Intelligent Transportation Systems

With the recent advancements in the networking realm of computers as well as achieving real-time communication between devices over the Internet, IoT (Internet of Things) devices have been on the rise; collecting, sharing, and exchanging data with other connected devices or databases online, enabling all sorts of communications and operations without the need for human intervention, oversight, or control. This has caused more computer-based systems to get integrated into the physical world, inching us closer towards developing smart cities. The automotive industry, alongside other software developers and technology companies have been at the forefront of this advancement towards achieving smart cities. Currently, transportation networks need to be revamped to utilize the massive amounts of data being generated by the public’s vehicle’s on-board devices, as well as other integrated sensors on public transit systems, local roads, and highways. This will create an interconnected ecosystem that can be leveraged to improve traffic efficiency and reliability. Currently, Vehicular Ad-hoc Networks (VANETs) such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-grid (V2G) communications, all play a major role in supporting road safety, traffic efficiency, and energy savings. To protect these devices and the networks they form from being targets of cyber-related attacks, this paper presents ideas on how to leverage distributed ledger technologies (DLT) to establish secure communication between vehicles that is decentralized, trustless, and immutable. Incorporating IOTA’s protocols, as well as utilizing Ethereum’s smart contracts functionality and application concepts with VANETs, all interoperating with Hyperledger’s Fabric framework, several novel ideas can be implemented to improve traffic safety and efficiency. Such a modular design also opens up the possibility to further investigate use cases of the blockchain and distributed ledger technologies in creating a decentralized intelligent transportation system (ITS)

Design and Reliability Performance Evaluation of Network Coding Schemes for Lossy Wireless Networks

This thesis investigates lossy wireless networks, which are wireless communication networks consisting of lossy wireless links, where the packet transmission via a lossy wireless link is successful with a certain value of probability. In particular, this thesis analyses all-to-all broadcast in lossy wireless networks, where every node has a native packet to transmit to all other nodes in the network. A challenge of all-to-all broadcast in lossy wireless networks is the reliability, which is defined as the probability that every node in the network successfully obtains a copy of the native packets of all other nodes. In this thesis, two novel network coding schemes are proposed, which are the neighbour network coding scheme and the random neighbour network coding scheme. In the two proposed network coding schemes, a node may perform a bit-wise exclusive or (XOR) operation to combine the native packet of itself and the native packet of its neighbour, called the coding neighbour, into an XOR coded packet. The reliability of all-to-all broadcast under both the proposed network coding schemes is investigated analytically using Markov chains. It is shown that the reliability of all-to-all broadcast can be improved considerably by employing the proposed network coding schemes, compared with non-coded networks with the same link conditions, i.e. same probabilities of successful packet transmission via wireless channels. Further, the proposed schemes take the link conditions of each node into account to maximise the reliability of a given network. To be more precise, the first scheme proposes the optimal coding neighbour selection method while the second scheme introduces a tuning parameter to control the probability that a node performs network coding at each transmission. The observation that channel condition can have a significant impact on the performance of network coding schemes is expected to be applicable to other network coding schemes for lossy wireless networks

Performance metrics and routing in vehicular ad hoc networks

The aim of this thesis is to propose a method for enhancing the performance of Vehicular Ad hoc Networks (VANETs). The focus is on a routing protocol where performance metrics are used to inform the routing decisions made. The thesis begins by analysing routing protocols in a random mobility scenario with a wide range of node densities. A Cellular Automata algorithm is subsequently applied in order to create a mobility model of a highway, and wide range of density and transmission range are tested. Performance metrics are introduced to assist the prediction of likely route failure. The Good Link Availability (GLA) and Good Route Availability (GRA) metrics are proposed which can be used for a pre-emptive action that has the potential to give better performance. The implementation framework for this method using the AODV routing protocol is also discussed. The main outcomes of this research can be summarised as identifying and formulating methods for pre-emptive actions using a Cellular Automata with NS-2 to simulate VANETs, and the implementation method within the AODV routing protocol

Design and Reliability Performance Evaluation of Network Coding Schemes for Lossy Wireless Networks

This thesis investigates lossy wireless networks, which are wireless communication networks consisting of lossy wireless links, where the packet transmission via a lossy wireless link is successful with a certain value of probability. In particular, this thesis analyses all-to-all broadcast in lossy wireless networks, where every node has a native packet to transmit to all other nodes in the network. A challenge of all-to-all broadcast in lossy wireless networks is the reliability, which is defined as the probability that every node in the network successfully obtains a copy of the native packets of all other nodes. In this thesis, two novel network coding schemes are proposed, which are the neighbour network coding scheme and the random neighbour network coding scheme. In the two proposed network coding schemes, a node may perform a bit-wise exclusive or (XOR) operation to combine the native packet of itself and the native packet of its neighbour, called the coding neighbour, into an XOR coded packet. The reliability of all-to-all broadcast under both the proposed network coding schemes is investigated analytically using Markov chains. It is shown that the reliability of all-to-all broadcast can be improved considerably by employing the proposed network coding schemes, compared with non-coded networks with the same link conditions, i.e. same probabilities of successful packet transmission via wireless channels. Further, the proposed schemes take the link conditions of each node into account to maximise the reliability of a given network. To be more precise, the first scheme proposes the optimal coding neighbour selection method while the second scheme introduces a tuning parameter to control the probability that a node performs network coding at each transmission. The observation that channel condition can have a significant impact on the performance of network coding schemes is expected to be applicable to other network coding schemes for lossy wireless networks