Socially Inspired Data Dissemination for Vehicular Ad Hoc Networks

International audiencePeople have routines and their mobility patterns vary during the day, which have a direct impact on vehicular mobility. Therefore, proto-cols and applications designed for Vehicular Ad Hoc Networks need to adapt to these routines in order to provide better services. With this issue in mind, in this work, we propose a data dissemination solution for these networks that considers the daily road traffic vari-ation of large cities and the relationship among vehicles. The focus of our approach is to select the best vehicles to rebroadcast data messages according to social metrics, in particular, the clustering coefficient and the node degree. Moreover, our solution is designed in such a way that it is completely independent of the perceived road traffic density. Simulation results show that, when compared to related protocols, our proposal provides better delivery guarantees, reduces the network overhead and possesses an acceptable delay

Data Transmissions using Hub Nodes in Vehicular Social Networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a ¿social¿ one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.Vegni, AM.; Souza, C.; Loscrí, V.; Hernández-Orallo, E.; Manzoni, P. (2020). Data Transmissions using Hub Nodes in Vehicular Social Networks. IEEE Transactions on Mobile Computing. 19(7):1570-1585. https://doi.org/10.1109/TMC.2019.2928803S1570158519

Emergency warning messages dissemination in vehicular social networks: A trust based scheme

To ensure users' safety on the road, a plethora of dissemination schemes for Emergency Warning Messages (EWMs) have been proposed in vehicular networks. However, the issue of false alarms triggered by malicious users still poses serious challenges, such as disruption of vehicular traffic especially on highways leading to precarious effects. This paper proposes a novel Trust based Dissemination Scheme (TDS) for EWMs in Vehicular Social Networks (VSNs) to solve the aforementioned issue. To ensure the authenticity of EWMs, we exploit the user-post credibility network for identifying true and false alarms. Moreover, we develop a reputation mechanism by calculating a trust-score for each node based on its social-utility, behavior, and contribution in the network. We utilize the hybrid architecture of VSNs by employing social-groups based dissemination in Vehicle-to-Infrastructure (V2I) mode, whereas nodes' friendship-network in Vehicle-to-Vehicle (V2V) mode. We analyze the proposed scheme for accuracy by extensive simulations under varying malicious nodes ratio in the network. Furthermore, we compare the efficiency of TDS with state-of-the-art dissemination schemes in VSNs for delivery ratio, transmission delay, number of transmissions, and hop-count. The experimental results validate the significant efficacy of TDS in accuracy and aforementioned network parameters. © 2019 Elsevier Inc

Exploiting vehicular social networks and dynamic clustering to enhance urban mobility management

Transport authorities are employing advanced traffic management system (ATMS) to improve vehicular traffic management efficiency. ATMS currently uses intelligent traffic lights and sensors distributed along the roads to achieve its goals. Furthermore, there are other promising technologies that can be applied more efficiently in place of the abovementioned ones, such as vehicular networks and 5G. In ATMS, the centralized approach to detect congestion and calculate alternative routes is one of the most adopted because of the difficulty of selecting the most appropriate vehicles in highly dynamic networks. The advantage of this approach is that it takes into consideration the scenario to its full extent at every execution. On the other hand, the distributed solution needs to previously segment the entire scenario to select the vehicles. Additionally, such solutions suggest alternative routes in a selfish fashion, which can lead to secondary congestions. These open issues have inspired the proposal of a distributed system of urban mobility management based on a collaborative approach in vehicular social networks (VSNs), named SOPHIA. The VSN paradigm has emerged from the integration of mobile communication devices and their social relationships in the vehicular environment. Therefore, social network analysis (SNA) and social network concepts (SNC) are two approaches that can be explored in VSNs. Our proposed solution adopts both SNA and SNC approaches for alternative route-planning in a collaborative way. Additionally, we used dynamic clustering to select the most appropriate vehicles in a distributed manner. Simulation results confirmed that the combined use of SNA, SNC, and dynamic clustering, in the vehicular environment, have great potential in increasing system scalability as well as improving urban mobility management efficiency1916CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP401802/2016-7; 2015/25588-6; 2016/24454-9; 2018/02204-6; 465446/2014-088887.136422/2017-002014/50937-

ReFIoV: a novel reputation framework for information-centric vehicular applications

In this article, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the K-Means clustering algorithm allows to integrate recommendations from other nodes even if they behave in an unpredictable manner. AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others’ mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison to another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time