Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.EC/H2020/825496/EU/5G for cooperative & connected automated MOBIility on X-border corridors/5G-MOBI

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements

Timely and reliable packets delivery over Internet of Vehicles (IoVs) for road accidents prevention: a cross-layer approach

With the envisioned era of Internet of Things (IoTs), all aspects of Intelligent Transportation Systems (ITS) will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, enhance the comfort of transportation and significantly reduce road accidents. In IoVs, regular exchange of current position, direction, velocity, etc., enables mobile vehicles to predict an upcoming accident and alert the human drivers in time or proactively take precautionary actions to avoid the accident. The actualization of this concept requires the use of channel access protocols that can guarantee reliable and timely broadcast of safety messages. This paper investigates the application of network coding concept to increase content of every transmission and achieve improved broadcast reliability with less number of retransmission. In particular, we proposed Code Aided Retransmission-based Error Recovery (CARER) scheme, introduced an RTB/CTB handshake to overcome hidden node problem and reduce packets collision rate. In order to avoid broadcast storm problem associated with the use of RTB/CTB packet in a broadcast transmission, we developed a rebroadcasting metric used to successfully select a vehicle to rebroadcast the encoded message. The performance of CARER protocol is clearly shown with detailed theoretical analysis and further validated with simulation experiments

UAV-assisted data dissemination based on network coding in vehicular networks

Efficient and emergency data dissemination service in vehicular networks (VN) is very important in some situations, such as earthquakes, maritime rescue, and serious traffic accidents. Data loss frequently occurs in the data transition due to the unreliability of the wireless channel and there are no enough available UAVs providing data dissemination service for the large disaster areas. UAV with an adjustable active antenna can be used in light of the situation. However, data dissemination assisted by UAV with the adjustable active antenna needs corresponding effective data dissemination framework. A UAV-assisted data dissemination method based on network coding is proposed. First, the graph theory to model the state of the data loss of the vehicles is used; the data dissemination problem is transformed as the maximum clique problem of the graph. With the coverage of the directional antenna being limited, a parallel method to find the maximum clique based on the region division is proposed. Lastly, the method\u27s effectiveness is demonstrated by the simulation; the results show that the solution proposed can accelerate the solving process of finding the maximum clique and reduce the number of UAV broadcasts. This manuscript designs a novel scheme for the UAV-assisted data dissemination in vehicular networks based on network coding. The graph theory is used to model the state of the data loss of the vehicles. With the coverage of the directional antenna being limited, then a parallel method is proposed to find the maximum clique of the graph based on the region division. The effectiveness of the method is demonstrated by the simulation

Low-Latency Strategies for Service Migration in Fog Computing Enabled Cellular Networks

This chapter presents a fog computing enabled cellular network (FeCN), in which the high user-mobility feature brings critical challenges for service continuity under stringent service requirements. Service migration is promising to fulfill the service continuity during mobility. However, service migration cannot be completed immediately and may lead to situations where the user-experience degrades. For this, a quality-of-service aware service migration strategy is proposed. The method is based on existing handover procedures with newly introduced distributed fog computing resource management scheme to minimize the potential negative effects induced by service migration. The performance of the proposed schemes is evaluated by a case study, where realistic vehicular mobility pattern in the metropolitan network of Luxembourg is used. Results show that low end-to-end latency for vehicular communication can be achieved. During service migration, both the traffic generated by migration and the other traffic (e.g., control information, video) are transmitted via mobile backhaul networks. To balance the performance of the two kinds of traffic, a delay-aware bandwidth slicing scheme is proposed. Simulation results show that, with the proposed method, migration data can be transmitted successfully within a required time threshold, while the latency and jitter for nonmigration traffic with different priorities can be reduced significantly

Performance Evaluation of Wireless Medium Access Control Protocols for Internet of Things

The Internet of Things makes the residents in Smart Cities enjoy a more efficient and high-quality lifestyle by wirelessly interconnecting the physical and visual world. However, the performance of wireless networks is challenged by the ever-growing wireless traffic data, the complexity of the network structures, and various requirements of Quality of Service (QoS), especially on the Internet of Vehicle and wireless sensor networks. Consequently, the IEEE 802.11p and 802.11ah standards were designed to support effective inter-vehicle communications and large-scale sensor networks, respectively. Although their Medium Access Control protocols have attracted much research interest, they have yet to fully consider the influences of channel errors and buffer sizes on the performance evaluation of these Medium Access Control (MAC) protocols. Therefore, this thesis first proposed a new analytical model based on a Markov chain and Queuing analysis to evaluate the performance of IEEE 802.11p under imperfect channels with both saturated and unsaturated traffic. All influential factors of the Enhanced Distributed Channel Access (EDCA) mechanism in IEEE 802.11p are considered, including the backoff counter freezing, Arbitration Inter-Frame Spacing (AIFS) defers, the internal collision, and finite MAC buffer sizes. Furthermore, this proposed model considers more common and actual conditions with the influence of channel errors and finite MAC buffer sizes. The effectiveness and accuracy of the developed model have been validated through extensive ns-3 simulation experiments. Second, this thesis proposes a developed analytical model based on Advanced Queuing Analysis and the Gilbert-Elliot model to analyse the performance of IEEE 802.11p with burst error transmissions. This proposed analytical model simultaneously describes transmission queues for all four Access Categories (AC) queues with the influence of burst errors. Similarly, this presented model can analyse QoS performance, including throughputs and end-to-end delays with the unsaturated or saturated load traffics. Furthermore, this model operates under more actual bursty error channels in vehicular environments. In addition, a series of simulation experiments with a natural urban environment is designed to validate the efficiency and accuracy of the presented model. The simulation results reflect the reliability and effectiveness of the presented model in terms of throughput and end-to-end delays under various channel conditions. Third, this thesis designed and implemented a simulation experiment to analyse the performance of IEEE 802.11ah. These simulation experiments are based on ns-3 and an extension. These simulation experiments' results indicate the Restricted Access Window (RAW) mechanism's influence on the throughputs, end-to-end delays, and packet loss rates. Furthermore, the influences of channel errors and bursty errors are considered in the simulations. The results also show the strong impact of channel errors on the performance of IEEE 802.11ah due to urban environments. Finally, the potential future work based on the proposed models and simulations is analysed in this thesis. The proposed models of IEEE 802.11p can be an excellent fundamental to optimise the QoS due to the precise evaluation of the influence of factors on the performance of IEEE 802.11p. Moreover, it is possible to migrate the analytical models of IEEE 802.11p to evaluate the performance of IEEE 802.11ah