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Electric vehicle (EV) is the solution to many problems of carbon emissions, cost of maintenance, raising fuel costs etc. An important problem hindering the rapid adoption of EV is the range anxiety problem. Range anxiety problem can be solved by providing the guarantee to find the charging point within the driving range of the vehicle with other constraints of waiting time, reliability, cost etc. This work proposes a P2P secure and reliable electric charge trading framework on top of VANET infrastructure. Different from existing energy trading solutions, this work is distributed, secure, reliable and provide non repudiation to the transactions. The proposed electric charge trading framework is built on convergence of three concepts of vehicular social networks, named data networking and block chain to address the factors causing range anxiety to EV users. Reliability is addressed through cooperative trust computation model built on vehicular social networks. Security is ensured through authentication of entities before trading and secure exchange of trading parameters. Higher availability of reliable charging stations with multi criteria station selection is facilitated using named data networking. Through simulation analysis, the proposed solution is found to provide 13% more charging supplier selection coverage, filter fake supplier by 18% and 17% lower energy trade establishment time compared to existing works. [ABSTRACT FROM AUTHOR]

The accurate identification of malicious nodes in vehicular social networks (VSN) can ensure the secure and efficient operation of the mobile network. The roadside unit (RSU) undertakes various tasks in the internet of vehicles (IoV) and processes a large amount of data. It plays an indispensable and crucial role in the IoV. Therefore, the damage and scope of the attack on RSU are more significant. This paper proposes a trust model for detecting malicious RSUs (TMDMR) to realize the trust decision problem of the roadside unit in VSN. The trust management model is constructed by designing two main decision indicators (QoS trust and social trust) in vehicular social networks, and the algorithm is intended to complete the calculation of global trust. Finally, the global trust server implements the trust decision of RSU. The simulation results demonstrate that TMDMR is better than the comparison scheme in terms of Precision and Recall. When the malicious number of nodes is 45% during OA attack, the Precision of TMDMR is 5.3% and 26.2% higher than comparison schemes, and Recall value is also 4.7% and 30.1% higher than them, respectively. When the malicious number is 45%, TMDMR has the lowest packet dropping rate (23.8% and 44.7%) to the comparison schemes. Its end-to-end delay is 42% and 51.3% lower than other two schemes. It also has advantages in terms of response time to complete a round of detection. [ABSTRACT FROM AUTHOR]

Vehicular Social Networks (VSNs) have emerged as a new social interaction paradigm, where vehicles can form social networks on the roads to improve the convenience/safety of passengers. VSNs are part of Vehicle to Everything (V2X) services, which is one of the industrial verticals in the coming sixth generation (6G) networks. The lower latency, higher connection density, and near-100% coverage envisaged in 6G will enable more efficient implementation of VSNs applications. The purpose of this study is to address the problem of lateral movements of attackers who could compromise one device in a VSN, given the large number of connected devices and services in VSNs and attack other devices and vehicles. This challenge is addressed via our proposed Blockchain-based Collaborative Distributed Intrusion Detection (BCDID) system with a novel Dynamic Throttling Strategy (DTS) to detect and prevent attackers’ lateral movements in VSNs. Our experiments showed how the proposed DTS improve the effectiveness of the BCDID system in terms of detection capabilities and handling queries three times faster than the default strategy with 350k queries tested. We concluded that our DTS strategy can increase transaction processing capacity in the BCDID system and improve its performance while maintaining the integrity of data on-chain.

Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular users’ location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality. [ABSTRACT FROM AUTHOR]

Vehicular social networks are emerging hybrid networks that combine traditional vehicular networks and social networks, with two key types of nodes, that is, vehicles and drivers. Since vehicle behaviors are controlled or influenced by drivers, the trustworthiness of a vehicle node is essentially determined by its own communication behaviors and its driver's social characteristics. Therefore, human factors should be considered in securing the communication in vehicular social networks. In this article, we propose a hybrid trust model that considers both communication trust and social trust. Within the proposed scheme, we first construct a communication trust model to quantify the trust value based on the interactions between vehicle nodes, and then develop a social trust model to measure the social trust based on the social characteristics of vehicle drivers. Based on these two trust models, we compute the combined trust assessment of a vehicle node in vehicular social networks. Extensive simulations show that the proposed hybrid trust model improves the accuracy in evaluating the trustworthiness of vehicle nodes and the efficiency of communication in vehicular social networks. [ABSTRACT FROM AUTHOR]

Vehicular social networks (VSNs) are considered as an emerging class of vehicular ad hoc networks (VANETs) with social attributes. In contrast to the traditional randomly distributed human social networks, vehicles normally only move along fıxed paths. As such, information sharing and forwarding become challenging because of the high speed of vehicles, complicated propagation environment, and quickly changing network topologies. The social characteristics and human behavior features are used to improve the effıciency of information delivery among vehicles in VANETs. In this paper, we propose a dynamic periodic location encounter network (DPLEN) model that considering both time-varying and location-varying characteristics of vehicles. We calculate the line similarity for each encountered pair of vehicles based on the periodic time and location information of the encounter event, and analyze social attributes based on the network model, such as the Community structure, Betweenness Centrality and K-shell. We also use the similarity to design two routing protocols for unicast and multicast patterns. After testing the proposed model on empirically collected datasets of mobility traces, the results show that these two routing protocols are able to greatly improve the efficiency of information forwarding in the VSNs. [ABSTRACT FROM AUTHOR]

Vehicular social networks (VSNs) have emerged as the promising paradigm of vehicular networks that can improve traffic safety, relieve traffic congestion and even provide comprehensive social services by sharing vehicular sensory data. To selectively share the sensory data with other vehicles in the vicinity and reduce the local storage burden of vehicles, the vehicular sensory data are usually outsourced to vehicle cloud server for sharing and searching. However, existing data sharing systems for VSNs can neither provide secure selective one-to-many data sharing and verifiable data retrieval over encrypted data nor ensure that the integrity of retrieved data. In this paper, we propose FTDS, a secure flexible and tampering-resistant data sharing system for VSNs by introducing a novel secure key-aggregate search encryption scheme and a tampering-resistant blockchain technology. With the proposed FTDS system for VSNs, the vehicular sensory data can be selectively shared and retrieved in a fine-grained way. Besides, our system allows vehicle data users to detect any unauthorized manipulation. Then, we present the detailed security analysis to prove that the proposed data sharing system can achieve both selective security and verifiability. We also evaluate its performance and demonstrate that it is efficient and practical for the VSNs scenarios. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid development of the vehicular ad-hoc network (VANET) promotes the development of vehicle electronics, networking, and intelligence. In addition, each vehicle can establish social relationships with others freely in vehicular social networks (VSNs). Multimedia also plays an important entity in vehicles' data sharing. However, VSNs are still in the early stage of development, and it may bring many security challenges in the process of sharing multimedia data. Both the users' and vehicles' privacy may be leaked, and users' communication habits are easy to be analyzed by adversaries. In addition, the shared multimedia data are more likely to be maliciously tampered with or forged. In this paper, we propose a privacy-preserving scheme based on blockchain for multimedia data sharing in VSNs. We use cryptographical primitives to hide the real identity of users, vehicles and RSUs. At the same time, we use blockchain to ensure reliable data sources and keep attackers from tampering or forging multimedia data. Finally, the feasibility of our scheme is proved by theoretical security analysis and comprehensive experimental evaluation. [ABSTRACT FROM AUTHOR]

With the rapid growth of fifth-generation mobile networks and vehicle to everything communication technologies on Internet of vehicles, vehicular social networks (VSNs) present new research fields for content sharing, data dissemination, and delivery services. Data owners outsource valuable information in the public cloud and share with others. But for security consideration, the owners tend to encrypt the outsourced data and unwilling to share the decryption key. Thus, there is a dilemma: owners will risk privacy leakage by handing over the key while hiding the decryption key, and other users cannot share the data. In this paper, we propose and analyze a selective sharing scheme (S2PD) for vehicular data owners to share their sensitive data with some authorized data users in VSN. S2PD ensures different users acquiring different data with different privilege received from the data owners. In the scheme, the owners upload the privacy data encrypted by key pairs and let the cloud decode the encrypted data to half-decrypted when they need to share the data with some users; the authorized users download the half-decrypted data and decrypt it with a secret key sent by the sharing owners in a secure manner. Then, it protects the data leakage from the curious cloud server and other unauthorized data users. With the increasing data and sharing requests in VSN, compared with other five sharing scheme, experiments proved our scheme is practically efficient.

Vehicle data, which may have some errors, provide a source for big data analysis. Car owners sometimes publish false information to protect their privacy or interests. The spread of these false messages will contribute to potential loss as rumors. Recently, the emergence of the vehicular social network has further accelerated the spread of rumors and anti-rumors. Researchers have proposed a number of methods to reduce the loss caused by rumors. However, these methods do not account for the fact that it takes some time for users to reply to the message after receiving the rumors. Each user responds differently to rumors, but previous methods do use fixed conversion rates in different states. To overcome these difficulties, individuals are classified as susceptible, trustful, contagious, immune or recoverable (STCIR). We propose a novel STCIR model to study the dynamic propagation of rumors. Each user is assigned a time threshold attribute to indicate the time delay of the user's response. In some cases, a user may receive a rumor repeatedly in the course of the time threshold. Furthermore, we introduce vehicular nodes as authorities that produce correct information to curb rumors through user forwarding, and the anti-rumors starting from users are regarded as a rumor cascade. The experimental results reveal that vehicular nodes can reduce the scale and number of rumors. Vehicular nodes emerge early and move fast, and by moving to a greater degree, these nodes can increase the efficiency of mitigating rumors.

To realize the construction of intelligent transportation system, data mining based on large-scale taxi traces has become a hot research topic. A crucial direction for analyzing taxi GPS data set is to recommend cruising areas for taxi drivers. Most of the existing researches merely concentrate on how to maximize drivers' profits while overlooking the benefit of passengers. Such imbalance makes the existing solutions do not work well in a real-world environment. In this paper, we construct a recommendation system by jointly considering the profits of both drivers and passengers. Specifically, we first investigate the real-time demand-supply level for taxis and then make an adaptive tradeoff between the utilities of drivers and passengers for different hotspots. At last, the qualified candidates are recommended to drivers based on the analysis results. Simulation results indicate that the constructed recommendation system can achieve a remarkable improvement on the global utility and make equilibrium between the utilities of drivers and passengers, simultaneously.

In this letter, the efficient dissemination of content in a socially-aware cache-enabled hybrid network using multi-armed bandit learning theory is analyzed. Specifically, an overlay cellular network over a vehicular social network is considered, where commuters request for multimedia content from either the stationary road-side units (RSUs), the base station, or the single mobile cache unit (MCU), if accessible. Firstly, we propose an algorithm to optimally distribute popular contents among the locally deployed RSU caches. To further maximize the cache hits experienced by vehicles, we then present an algorithm to find the best traversal path for the MCU based on commuters’ social degree distribution. For performance evaluation, the asymptotic regret upper bounds of the two algorithms are also derived. Simulations reveal that the proposed algorithms outperform existing content placement methods in terms of overall network throughput. [ABSTRACT FROM AUTHOR]

The mobile industry's evolution from 4G to 5G will lead to a deep progress on mobile applications that are widely used in some new environments, such as vehicular social networks (VSNs). In VSNs, which are considered the first automobile social networks, vehicular communication can facilitate large-scale data sharing between drivers and their neighbours. However, malicious users of VSNs can also disseminate false information over the network. Traditional public key infrastructure (PKI) cannot recognize these malicious users, as they all have authorized identities. Thus, a trust management mechanism is introduced to secure vehicular social data. This paper demonstrates a high-level trust management model and its deployment scheme based on a vehicular cloud system. We propose a layered trust management mechanism that benefits from efficient use of physical resources (e.g., computing, storage, communication cost) and explore its deployment in a VSN scenario based on a three-layer cloud computing architecture. Moreover, performance modeling of the proposed trust management scheme is conducted through a novel formal compositional approach - Performance Evaluation Process Algebra (PEPA). PEPA has superior features in compositionality and parsimony, which means that it can efficiently model systems with layered architectures and complex behaviours. PEPA also supports various numerical analyses through calculating its underlying continuous time Markov chains (CTMCs) directly or solving a set of approximated ordinary differential equations (ODEs). According to analysis outcomes, we analyzed several key performance properties of the scheme and related capacity issues in deployment. The findings also reveal an efficient investigation approach for evaluating the performances of trust models.

Vehicular social networks, as one type of new future networks, integrate mobile wireless communications and social networks together, and they have attracted a plenty of interest from researchers. This paper argues that vehicular social networks joined with cloud and fog computing platforms can serve as recommenders and play a significant role in social lives. A new application for recommender systems that selects vehicles as recommenders is proposed to support marketers in improving marketing effectiveness. Then, we propose three algorithms to enhance the marketers' marketing effectiveness based on different evaluation standards. The first algorithm selects those vehicles that can obtain maximum benefits for the marketers, in which the vehicles are selected based on passing regions with more benefits. However, this selection method may lose some potential markets because of region limitations. The second algorithm selects those vehicles that can reach the maximum coverage ratio in the city and bring the marketers more marketing effectiveness in the future, although the current benefits are not the best. The third algorithm combines the two prior algorithms, finding a tradeoff between coverage and benefits. We finally evaluate the effectiveness of the proposed algorithm with real-world data to show the effectiveness and efficiency of the "On Selecting Vehicles as Recommenders for Vehicular Social Networks" scheme: for the scheme based on the benefit factor, the performance criteria of the benefit ratio can be improved by 21% over the existing selection methods; for the scheme based on the coverage factor, the performance criteria of the coverage ratio can be improved by 13% over the existing selection methods; and for the third selection algorithm based on the two factors, the performance criteria of the benefit ratio and the metric 9 can be improved by 18.9% and 13.7%, respectively.

Abstract The mobile crowdsourcing capabilities of commuters in Vehicular Social Networks (VSNs) can provide promising solutions to several challenges of today's smart cities, such as traffic congestion control, traffic management, smart parking, and route recommendation. In VSNs, mobile crowdsourcing depends upon the cooperative data forwarding behavior of nodes. In most of the existing data forwarding protocols designed for VSNs and delay tolerant networks (DTNs), it is assumed that nodes actively participate and are fully cooperative to relay data for others. In the real world, the selfish behavior of nodes considerably degrades the performance of these protocols. The reason is those selfish nodes only cooperate to relay data for nodes with whom they have strong social ties or mutual interests. In this paper, we present a Cooperative Data Forwarding (CDF) mechanism to stimulate the selfish nodes to participate in data forwarding. To enhance data forwarding mechanism, CDF is based on a socially-aware routing mechanism and a cooperative algorithm using direct observations and mobile crowdsourcing information to stimulate selfish nodes to participate in data forwarding. Besides, CDF is a multi-hop single copy forwarding mechanism which considerably decreases the network overhead. In our experimental results and analysis, we use real-world vehicular mobility dataset based on mobile crowdsourcing. The results show that CDF encourages more and more nodes to cooperate and improves network performance significantly in terms of data delivery ratio, transmission cost, and end-to-end delay which can substantially enhance the mobile crowdsourcing applications of VSNs. [ABSTRACT FROM AUTHOR]

Ride sharing has been widely studied over the past several decades as a means of reducing traffic and pollution by utilizing empty car seats in vehicles that are being driven no matter what. As they increase in popularity, ride sharing applications have already encountered several challenges: Vehicle allocation, price strategy, and route planning, are just a few such examples among many. Tracking human activity patterns with regard to ride share applications can potentially improve these systems in numerous ways. For example, taxi GPS trajectories offer a remarkable source of data for determining human activity patterns, among other things, in cities across the world. However, existing studies either focus solely on meeting order requirements or analyzing Points Of Interest (POI) based only on static information. The former issue cannot solve problems with balancing vehicle allocation, while the latter cannot precisely describe the POI locational feature. In order to develop a more specific analysis of activity patterns for ride sharing systems, we propose a Vehicular Social Network Based Analytical Framework (NBAF) to determine the specific urban activity of ride sharing systems at a low computational cost. The analytical framework contains two special contributions: Firstly, a novel trip mapping method named Trip-Embedding POI Decomposition Method (TEPID) is proposed to describe the feature of geo-nodes in the network. Secondly, the particular features for ride sharing systems are generated by vehicular social networks. Based on this framework, we propose a clustering method to reveal trip activity and regional features. As a case study, we analyze 30 days of taxi trips in New York City in 2016. The results demonstrate that NBAF can effectively determine urban activity and location patterns for vehicle allocation, price strategy, and route planning for ride sharing systems. [ABSTRACT FROM AUTHOR]

In recent years, many schemes have been proposed to disseminate Emergency Warning Messages (EWMs) in VANETs. However, various problems such as broadcast storm, hidden terminal and connectivity issues still persist to reduce the efficacy of these systems. In this paper, we propose a novel Social Utility-based Dissemination Scheme (SUDS) for Emergency Warning Messages in Vehicular Social Networks (VSNs). We utilize social properties of nodes such as centrality, interests and friendships to mitigate broadcast storm and hidden terminal problems. Furthermore, we employ the hybrid architecture of VSNs to solve connectivity and contact duration-related issues in sparse and high mobility environments. For this purpose, we devise a dual-strategy-based mechanism, where vehicles communicate with each other in a distributed or centralized manner according to the required situation. In order to evaluate the performance of proposed scheme, we have conducted extensive experiments for a highway scenario under varying vehicular density, vehicular speed and distance, in comparison with the state-of-the-art dissemination schemes. Simulation results have demonstrated the superiority of SUDS over the compared protocols in terms of delivery ratio, transmission delay and total number of transmissions. We have also demonstrated the positive effects of hybrid architecture of VSNs on various network parameters. [ABSTRACT FROM AUTHOR]

Packet routing is important for vehicular delay tolerant networks (VDTNs). Opportunistic routing algorithms based on historical records are insufficiently accurate in forwarder selection due to movement randomness of vehicles. Trajectory-based routing algorithms tackle vehicle movement randomness but cannot be directly used in VDTNs due to the dependence on APs. In this paper, we develop a distributed trajectory-based routing algorithm (called MobiT) for VDTNs. This non-trivial task faces three challenges. First, vehicle trajectories must be sufficiently collected. Second, the trajectories cannot be updated frequently due to limited resources of the repository nodes. Third, achieving high routing performance even with partially collected trajectories. Our real trace study lays the foundation of the design of MobiT. Taking advantage of different roles of vehicles, MobiT uses service vehicles that move in wide areas to collect vehicle trajectories, and rely on the service vehicles and roadside units (called schedulers) for routing scheduling. By using regular temporal congestion state of road segments, MobiT schedules the packet to arrive at a roadside unit prior to the destination vehicle to improve routing performance. Furthermore, MobiT leverages vehicles’ long-term mobility patterns to assist routing. Our trace-driven simulation and real experiments show the effectiveness and efficiency of MobiT. [ABSTRACT FROM AUTHOR]

Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization. [ABSTRACT FROM PUBLISHER]

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 efficiency.

A Vehicular Social Network (VSN) is an emerging field of communication where relevant concepts are being borrowed from two different disciplines, i.e., vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). This emerging paradigm presents new research fields for content sharing, data dissemination, and delivery services. Based on social network analysis (SNA) applications and methodologies, interdependencies of network entities can be exploited in VSNs for prospective applications. VSNs involve social interactions of commuters having similar objectives, interests, or mobility patterns in the virtual community of vehicles, passengers, and drivers on the roads. In this paper, considering social networking in a vehicular environment, we investigate the prospective applications of VSNs and communication architecture. VSNs benefit from the social behaviors and mobility of nodes to develop novel recommendation systems and route planning. We present a state-of-the-art literature review on socially-aware applications of VSNs, data dissemination, and mobility modeling. Further, we give an overview of different recommendation systems and path planning protocols based on crowdsourcing and cloud-computing with future research directions. [ABSTRACT FROM AUTHOR]

• The advancement of global social networks necessitates the need for a more effective approach to data security management. • Functional algorithms for news screening should be integrated with methods based on investigators' intuition. • The reliance on the quality of single participants is not critical enough to make the crowdsourcing approach not feasible through. The openness of Internet and the use of big data often lead to reliability issues. The advancement of social networks necessitates the need for a more effective approach to security management, especially in crowdsourcing-based vehicular social networking systems. This paper discusses issues surrounding the practice of trust management in international crowdsourcing projects based on massive amounts of unscrupulous data. The study looks at Bellingcat's investigative projects to identify the working patterns of data verification. Data analysis was performed with the help of the grounded theory methodology. The results of the study suggest that functional algorithms for news screening should be integrated with intuition-based methods of data verification. The proposed approach is expected to reduce the number of factors affecting the accuracy of crowdsourced data analysis. [ABSTRACT FROM AUTHOR]

Vehicular social networks (VSNs) are the vehicular ad hoc networks (VANETs) that integrate social networks. Compared with traditional VANETs, VSNs are more suitable to serve a group of vehicles with common interests. In VSNs, vehicles can upload the necessary data in the cloud service provider (CSP) and other vehicles can query the data they are interested in through CSP, which enables VSNs to provide more user-friendly services. However, due to the wireless network communication environment, the data sent by the vehicle can easily be monitored. Adversaries are able to violate the privacy of the vehicle based on the collected data, thereby threatening the security of the entire network. In addition, if a vehicle shares malicious or false data with other vehicles, it is easy to mislead drivers and even cause serious traffic accidents. This paper proposes an effective data sharing scheme based on blockchain in VSNs. By integrating an identity based signature mechanism and pseudonym generation mechanism, we first propose an anonymous authentication mechanism as the basis for establishing trust relationships before data transmission between entities in VSNs. Then, a data sharing scheme based on blockchain is described, in which the signature mechanism and the consensus mechanism guarantee the security and traceability of data. The result of the performance analysis and the simulation experiment indicate that VAB can achieve a favourable performance compared with existing schemes. [ABSTRACT FROM AUTHOR]

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 efficiency. [ABSTRACT FROM AUTHOR]