Analysis of GFDM as a robust 5G communication technique in noisy environment

One of the challenges of modulation techniques used in Fifth-Generation (5G) is their robustness in noisy environment. Conventional Orthogonal Frequency Division Multiplexing (OFDM) cannot be considered as a 5G waveform in its original form because of its certain limitations, such as performance degradation by impulsive noise (IN) and high peak to average power ratio (PAPR). Numerous modulation schemes proposed for 5G communications are able to overcome these drawbacks. Generalised Frequency Division Multiplexing (GFDM) is one of them. This paper analyses the performance of GFDM in presence of Additive White Gaussian Noise (AWGN), IN and Narrow Band Interference (NBI). It is found that GFDM is able to perform better than OFDM and Vector Orthogonal Frequency Division Multiplexing (VOFDM) in presence of noises, which can potentially be present in 5G applications. Simulation results show that GFDM achieve lower PAPR and Symbol Error Rate (SER) and an average of 10.73 dB and 4.73 dB gain in Signal to Noise Ratio (SNR) in presence of IN and combined IN and NBI respectively, as compared to OFDM and VOFDM

Blockchain based secure message dissemination in vehicular networks

Vehicular ad-hoc networks (VANETs) are one of the key elements in Intelligent Transportation System (ITS) to enable information exchange among vehicles and Roadside Units (RSUs) via vehicle-to-vehicle (V2V) and vehicle-to- nfrastructure (V2I) communications. With continuously increasing number of vehicles on road, there are numerous security and privacy challenges associated with VANETs. Communication among vehicles is needed to be secure and bandwidth efficient. Also, the messages exchanged between vehicles must be authentic so as to maintain a trusted network in a privacy-preserving manner. Furthermore, a sustainable economic model is required to incentivise honest and cooperative vehicles. Traditional security and privacy solutions in centralised networks are not applicable to VANETs due to its distributed nature, heterogeneity, high mobility and low latency requirements. Meanwhile, the new development of blockchain has been attracting significant interests due to its key features including consensus to evaluate message credibility and immutable storage in distributed ledger, which provides an alternative solution to the security and privacy challenges in VANETs. This thesis aims to present blockchain solutions for the security and privacy of VANETs meeting the stringent requirements of low latency and bandwidth-efficient message dissemination. VANETs are simulated in OMNeT++ to validate the proposed solutions. Specifically, two novel blockchain consensus algorithms have been developed for message authentication and relay selection in presence of malicious vehicles. The first employs a voting based message validation and relay selection, which reduces the failure rate in message validation by 11% as compared to reputation based consensus. The second utilises federated learning supported by blockchain as a better privacy-preserving solution, which is 65.2% faster than the first voting based solution. Both approaches include blockchain-based incentive mechanisms and game theory analysis to observe strategic behaviour of honest and malicious vehicles. To further study the privacy aspect of vehicular networks, the integration of blockchain with physical layer security is also theoretically analysed in Vehicle-to-Everything (V2X) communications scenarios. The integration results in 8.2 Mbps increased goodput as compared to the blockchain solution alone. In essence, our research work shows that blockchain can offer better control and security, as compared to centralised solutions, if properly adjusted according to the application and network requirements. Thus, the proposed solutions can provide guidelines for practically feasible application of blockchain in vehicular networks

Vision, enabling technologies, and scenarios for a 6G-Enabled internet of verticals (6G-IoV)

5G is the critical mobile infrastructure required to both enable and accelerate the full digital transformation of vertical sectors. While the 5G for vertical sectors is aiming at connectivity requirements of specific verticals, such as manufacturing, automotive and energy, we envisage that in the longer term the expansion of wide area cellular connectivity to these sectors will pave the way for a transformation to a new Internet of Verticals (IoV) in the 6G era, which we call 6G-IoV. In this paper, we describe our vision of 6G-IoV and examine its emerging and future architectural and networking enablers. We then illustrate our vision by describing a number of future scenarios of the 6G-IoV, namely the Internet of Cloud Manufacturing accounting for around 25% of digital services and products, the Internet of Robotics to cater the challenges of the growing number of robotics and expected 7% increase in usage over the coming years and the Internet of Smart Energy Grids for net-zero energy balance and shifting to 100% dependence on the renewables of energy generation

A Proof-of-Quality-Factor (PoQF) based blockchain and edge computing for vehicular message dissemination

Blockchain applications in vehicular networks can offer many advantages including decentralization and improved security. However, most of consensus algorithms in blockchain are difficult to be implemented in a Vehicular Ad-Hoc Networks (VANET) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time consuming consensus algorithms, e.g., Proof-of-Work, running on mobile edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g. Proof-of-Stake (PoS) is biased towards nodes with higher amount of stakes and Proof-of-Elapsed-Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on Proof-of-Quality-Factor (PoQF) consensus algorithm, where threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on probabilistic prediction of channel quality between transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. The paper also analyzes the throughput of block generation, as well as the asymptotic latency, security and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against collusion of nodes is proved using game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET

A blockchain based federated learning for message dissemination in vehicular networks

Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proofof-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbour vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analysed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks

Blockchain-empowered AI for 6G-enabled internet of vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network

Blockchain-enabled FD-NOMA based vehicular network with Physical Layer Security

Vehicular networks are vulnerable to large scale attacks. Blockchain, implemented upon application layer, is recommended as one of the effective security and privacy solutions for vehicular networks. However, due to an increasing complexity of connected nodes, heterogeneous environment and rising threats, a robust security solution across multiple layers is required. Motivated by the Physical Layer Security (PLS) which utilizes physical layer characteristics such as channel fading to ensure reliable and confidential transmission, in this paper we analyze the impact of PLS on a blockchain-enabled vehicular network with two types of physical layer attacks, i.e., jamming and eavesdropping. Throughout the analysis, a Full Duplex Non-Orthogonal Multiple Access (FD-NOMA) based vehicle-to-everything (V2X) is considered to reduce interference caused by jamming and meet 5G communication requirements. Simulation results show enhanced goodput of a blockckchain enabled vehicular network integrated with PLS as compared to the same solution without PLS

Analysis of deep convolutional neural network models for the fine-grained classification of vehicles

Intelligent transportation systems (ITS) is a broad area that encompasses vehicle identification, classification, monitoring, surveillance, prediction, management, reduction of traffic jams, license plate recognition, etc. Machine learning has practical and significant applications in ITS. Intelligent transportation systems rely heavily on vehicle classification for traffic management and monitoring. This research uses convolutional neural networks to classify cars at fine-grained classifications (make and model). Numerous obstacles must be overcome in order to complete the task, the greatest of which are intra- and inter-class similarities between the manufacturer and model of vehicles, different lighting effects, the shape and size of the vehicle, shadows, camera view angle, background, vehicle speed, colour occlusion and environmental conditions. This paper studies various machine learning algorithms used for the fine-grained classification of vehicles and presents a comparative analysis in terms of accuracy and the size of the implemented deep convolutional neural network (DCNN). Specifically, four DCNN models, mobilenet-v2, inception-v3, vgg-19 and resnet-50, are evaluated with three datasets, BMW-10, Stanford Cars and PAKCars. The evaluation results show that mobileNet-v2 is the smallest model as it is not computationally intensive due to depthwise separable convolution. However, resnet-50 and vgg-19 outperform inception-v3 and mobilenet-v2 in terms of accuracy due to their complex structure

Two-layer distributed content caching for infotainment applications in VANETs

For vehicular ad-hoc networks (VANETs), edge caching has attracted considerable research attention to maximize the efficiency and reliability of infotainment applications. In this paper, we propose a two-layer distributed content caching scheme for VANETs by jointly exploiting the cache at both vehicles and roadside units (RSUs). Specifically, we formulate content caching problem to minimize the overall transmission delay and cost as a nonlinear integer programming (NLIP) problem and propose an alternate dynamic programming search (ADPS) based algorithm to solve it. In ADPS, we divide the original problem into three sub-problems, then we use the dynamic programming (DP) method to solve each sub-problem separately. To reduce the complexity, we further propose a cooperation-based greedy (CBG) algorithm to solve the large scale original problem. Both numerical simulation results and experiments in testbed show that the proposed caching scheme outperforms existed caching schemes, the transmission delay and cost can be reduced by 10% and 24% respectively, while the hit ratio can be increased by 30% in a practical environment, as compared to popularity-based caching scheme

Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network