“…In order to solve the problem of high overhead of certificate management, Basudan et al [21] proposed a certificateless aggregation signature scheme based on fog computing in the road condition monitoring. Kong et al [22] proposed a secure query mechanism for the vehicle fog data publishing based on homomorphic encryption and batch verification technology in the architecture of fog computing in VANET, in which RSUs verified the correctness of recovered data requests. All the schemes above are based on cryptography to achieve user identity authentication.…”
With the increasing development of wireless communication technology and Vehicular Ad hoc Network (VANET), as well as the continuous popularization of various sensors, Mobile Crowdsensing (MCS) paradigm has been widely concerned in the field of transportation. As a currently popular data sensing way, it mainly relies on wireless sensing devices to complete large-scale and complex sensing tasks. However, since vehicles are highly mobile in this scenario and the sensing system is open, that is, any vehicle equipped with sensing device can join the system, the credibility of all participating vehicles cannot be guaranteed. In addition, malicious users will upload false data in the sensing system, which makes the sensing data not meet the needs of the sensing tasks and will threaten traffic safety in some serious cases. There are many solutions to the above problems, such as cryptography, incentive mechanism, and reputation mechanisms. Unfortunately, although these schemes guaranteed the credibility of users, they did not give much thought to the reliability of data. In addition, some schemes brought a lot of overhead, some used a centralized server management architecture, and some were not suitable for the scenario of VANET. Therefore, this paper firstly proposes the MCS-VANET architecture-based blockchain, which consists of participating vehicles (PVs), road side units (RSUs), cloud server (CS), and the blockchain (BC), and then designs a malicious user detection scheme composed of three phases. In the data collecting phase, to reduce the data uploading overhead, data aggregation and machine learning technologies are combined by fully considering the historical reputation value of PVs, and the proportion of data uploading is determined based on the historical data quality evaluation result of PVs. In the data quality evaluation phase, a new reputation computational model is proposed to effectively evaluate the sensing data, which contains four indicators: the reputation history of PVs, the data unbiasedness, the leadership of PVs, and the spatial force of PVs. In the reputation updating phase, to achieve the effective change of reputation values, the logistic model function curve is introduced and the result of the reputation updating is stored in the blockchain for security publicity. Finally, on real datasets, the feasibility and effectiveness of our proposed scheme are demonstrated through the experimental simulation and security analysis. Compared with existing schemes, the proposed scheme not only reduces the cost of data uploading but also has better performance.
“…In order to solve the problem of high overhead of certificate management, Basudan et al [21] proposed a certificateless aggregation signature scheme based on fog computing in the road condition monitoring. Kong et al [22] proposed a secure query mechanism for the vehicle fog data publishing based on homomorphic encryption and batch verification technology in the architecture of fog computing in VANET, in which RSUs verified the correctness of recovered data requests. All the schemes above are based on cryptography to achieve user identity authentication.…”
With the increasing development of wireless communication technology and Vehicular Ad hoc Network (VANET), as well as the continuous popularization of various sensors, Mobile Crowdsensing (MCS) paradigm has been widely concerned in the field of transportation. As a currently popular data sensing way, it mainly relies on wireless sensing devices to complete large-scale and complex sensing tasks. However, since vehicles are highly mobile in this scenario and the sensing system is open, that is, any vehicle equipped with sensing device can join the system, the credibility of all participating vehicles cannot be guaranteed. In addition, malicious users will upload false data in the sensing system, which makes the sensing data not meet the needs of the sensing tasks and will threaten traffic safety in some serious cases. There are many solutions to the above problems, such as cryptography, incentive mechanism, and reputation mechanisms. Unfortunately, although these schemes guaranteed the credibility of users, they did not give much thought to the reliability of data. In addition, some schemes brought a lot of overhead, some used a centralized server management architecture, and some were not suitable for the scenario of VANET. Therefore, this paper firstly proposes the MCS-VANET architecture-based blockchain, which consists of participating vehicles (PVs), road side units (RSUs), cloud server (CS), and the blockchain (BC), and then designs a malicious user detection scheme composed of three phases. In the data collecting phase, to reduce the data uploading overhead, data aggregation and machine learning technologies are combined by fully considering the historical reputation value of PVs, and the proportion of data uploading is determined based on the historical data quality evaluation result of PVs. In the data quality evaluation phase, a new reputation computational model is proposed to effectively evaluate the sensing data, which contains four indicators: the reputation history of PVs, the data unbiasedness, the leadership of PVs, and the spatial force of PVs. In the reputation updating phase, to achieve the effective change of reputation values, the logistic model function curve is introduced and the result of the reputation updating is stored in the blockchain for security publicity. Finally, on real datasets, the feasibility and effectiveness of our proposed scheme are demonstrated through the experimental simulation and security analysis. Compared with existing schemes, the proposed scheme not only reduces the cost of data uploading but also has better performance.
“…in Vehicular Fog Data Dissemination (QS-VFDD) [67] -In this paper, the authors have introduced a querying scheme for data dissemination in the vehicular network using fog computing. The RSU in this technique is a fog storage device that helps to prevent the association of data and vehicle from which the data originated.…”
Section: ) a Privacy-preserving And Verifiable Querying Schemementioning
The number of vehicles on the roads has increased proportionally over the last couple of years and this number is likely to rise due to the increase in population growth and the number of vehicles that are manufacturing every day. This high traffic density leads to several problems, from which effectively disseminating the emergency messages is a major concern. Keeping in view the dynamic characteristics of VANETs, significant challenges are faced in disseminating the message across the network. The major challenges are the broadcast storm problem, hidden node problem and the packet collision. Many studies have been performed to devise an effective and reliable mechanism for disseminating emergency messages in a Vehicular ad-Hoc Network (VANET). Researchers have proposed different models to tackle various types of scenarios for emergency message dissemination. This paper not only reviews some recent contributions to emergency message dissemination in vehicular networks but also discusses various proposed methods based on Intelligent Transportation System (ITS), Internet of Things (IoT), Priority messaging, Clustering approach, Software Defined Network (SDN) and Fog Computing. We have also tried to explore the latest developments in emergency message dissemination using 5G networks.
“…Sampaio et al [68 ] developed an efficient, privacy‐preserving and reliable data dissemination methods, which control the usage of relevant data by third parties. Later on, Kong et al [69 ] proposed a secure querying data dissemination method for the vehicular fog. In this section, various data privacy‐based approaches used for secure data dissemination in IoT are discussed.…”
Section: Privacy‐based Verification and Validation Schemesmentioning
A huge amount of data is generated from the interaction of various sensors and Internet of Things (IoT) enabled devices used in various smart industrial applications. This enormous amount of data requires fast processing, huge storage capacity, secure dissemination, and aggregation to make it resistant from the attackers. Secure data dissemination for IoT‐based applications has been a prominent issue in consideration with the heterogeneity in generated data. Existing secure data dissemination schemes are inadequate to handle secure data distribution. Research communities across the globe are focused on the delivery of the data among the sensor nodes and overlook the difficulty of its secure streaming. Hence, there is a need to validate the performance of secure data distribution schemes for IoT networks using relevant parameters. Motivated from the aforementioned facts, in this study, we perform a comprehensive review on the state‐of‐the‐art techniques, which can verify and validate the performance of data dissemination schemes for IoT networks. We present a solution taxonomy of various verification and validation methods along with their merits and demerits. Finally, recent issues and future directions on verification and validation methods for the secure data distribution in an IoT network is presented.
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