Vehicle to Vehicle (V2V) communication opens a new way to make vehicles directly communicate with each other, providing faster responses for time-sensitive tasks than cellular networks. Effective V2V routing protocols are essential yet challenging, as the high dynamic road environment makes communication easy to break. Many prediction methods proposed in the existing protocols to address this issue are either flawed or have a poor effect. In this paper, to cope with the two aspects of the problems that cause communication interrupt, i.e., link breaks and route quality degradation, we design an acceleration-based trajectory prediction algorithm to estimate the link lifetime, and a machine learning model to predict route quality. Based on the prediction algorithms, we propose PQR, a Prediction-supported Quality-aware Routing protocol, which can proactively switch to a better route before the current link breaks or the route quality degrades. Especially, considering the limitations of the current routing protocols, we elaborate a new hybrid routing protocol that integrates the topology-based method and location-based method to achieve instant communication. Simulation results show that PQR outperforms the existing protocols in Packet Delivery Ratio (PDR), Roundtrip Time (RTT), and Normalized Routing Overhead (NRO). Specifically, we have also implemented a vehicular testbed to demonstrate PQR’s real-world performance, and results show that PQR achieves almost no packet loss with latency less than 10ms during route handoff for topology change.

Intelligent Transport System (ITS) raises the increasing demand on accurate vehicle trajectory prediction for navigation efficiency. The rapidly developing 5G networks provides communications with high transmission bandwidth and super-low latency, paving the way for Mobile Edge Computing (MEC) to calculate more accurate trajectory prediction for vehicles, as the MEC server holds more comprehensive vehicular information. However, the current methods for trajectory prediction are not efficient due to the dynamical environment. To address this issue, we propose GlobalInsight, a Long Short-Term Memory (LSTM) based model, which runs on the MEC to perform accurate trajectory prediction for multiple vehicles no matter how scenario changes. In particular, we use three auxiliary layers to respectively capture the principal component of vehicle features, social interaction of adjacent vehicles, and the cross-vehicle correlation of similar vehicles. We further integrate the above information into LSTM in the main layer to enhance the trajectory learning and prediction. We evaluate our model under the NGSIM dataset, and experimental results exhibit that our model outperforms the state-of-the-art approaches.

Existing routing algorithms that based on traditional Vehicular Ad-Hoc NETwork (VANET) architectures cannot provide fast and diverse routing services due to dynamic and unstable environment. To address this issue, we propose a three-level routing hierarchy in improved Software-Defined VANET architecture based on Mobile Edge Computing (SDN-MEC-VANET) to improve routing performance and enrich the data transmission mode for the VANET. Moreover, it can be applied to almost all VANET protocols, enabling protocol-independent forwarding. Besides, this improved architecture can coordinate different edge devices to timely adjust the service delivery strategy under the predictive correction from controllers, providing high-bandwidth and low-delay transmission for Internet of Vehicles (IoV). Meanwhile, MEC technology is introduced to perform local control, leveraging the storage and computing capabilities of edge devices to reduce the processing pressure of the controller. Simulation results show that our routing algorithm in improved network architecture can achieve a higher packet delivery ratio within a reasonable delay than other approaches under different scenarios of network scale, communication frequency and vehicular velocity.

Vehicular Ad hoc network (VANET) has received great attention in recent research, but many challenges still lie in innovating efficient routing protocols to support the highly dynamic environment. Existing ID-based routing protocols cannot fundamentally tackle the dynamic topology problem in VANET. The recent emerging Information-Centric Networking (ICN) makes routing decisions based on data itself instead of a particular host, seeming to have the potential to handle the dynamic topology, but problems (e.g., severe flooding overhead) still remain. Therefore, inspired by the idea of ICN, we propose a name/ID hybrid routing (NIHR) protocol that combines the data-namebased routing and host-ID-based routing to address the above two issues simultaneously. In particular, we develop an announce strategy to improve the efficiency of the in-network cache, and we design a bloom filter based structure to achieve fast content lookup. Simulation results show NIHR's high performance in terms of Packet Delivery Ratio (PDR), Roundtrip time (RTT) and roundtrip hop count. Especially, to verify NIHR's performance in real-world scenarios, we have implemented a vehicular real-time video conference system based on MK5 OBU [1] (On-Board Unit).

High topology dynamics and intermittent connectivity in Vehicular Ad hoc Network (VANET) bring huge challenges to end-to-end communication. Existing routing protocols for MANET such as AODV and OLSR work fine under modest mobility, but have a difficult time to handle frequent topology changes in VANET. This paper proposes Peeking at the Past and Present Routing (P3R), a routing protocol that will calculate next-hops when the past forwarding is considered invalid. The next-hop calculation is based on the predicted locations of forwarder's neighbors and the packet's destination node, overcoming the inaccuracy caused by stale location information. Furthermore, we differentiate vehicles on crossroads as they have high connectivity in actual urban streets. In this way, P3R is able to deal with link breakages quickly and exploit new links. Simulation results show that P3R outperforms state-of-the-art alternatives in terms of packet delivery ratio, delay and cost, while maintaining strong scalability and robustness. We also implement P3R in a real vehicular testbed and the results reveal it has high connectivity on real streets.